5. Pre-trained Network_ MobileNet.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"5. Pre-trained Network: MobileNet.ipynb","provenance":[],"collapsed_sections":["NaDMLtupmIEk","C884lZO-YiB2"],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","source":["# Solve the problem leveraging MobileNet architecture\n","In this notebook, we will use the MobileNet architecture for our classification problem.\n","\n","First, we'll try a simple **feature extraction**. Since the architecture was trained on a different dataset, we do not expect very good performance, but we can use these results as a baseline for the next step: **fine-tuning**."],"metadata":{"id":"7XLoBMRb6tX1"}},{"cell_type":"markdown","source":["## Import Libraries\n","First of all, let's import all the libraries we will need later."],"metadata":{"id":"l5s-N9DXYZDC"}},{"cell_type":"code","source":["from google.colab import drive\n","import os\n","import shutil\n","import numpy as np\n","import random as rn\n","import tensorflow as tf\n","from tensorflow import keras\n","from tensorflow.keras.utils import image_dataset_from_directory\n","from tensorflow.keras import layers, models\n","from tensorflow.keras.models import Sequential\n","from tensorflow.keras import optimizers\n","from tensorflow.keras import regularizers\n","from keras.models import Model\n","import matplotlib.pyplot as plt\n","from sklearn import metrics\n","from sklearn.metrics import confusion_matrix"],"metadata":{"id":"790GcOInQ_yw","executionInfo":{"status":"ok","timestamp":1642022161182,"user_tz":-60,"elapsed":3342,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}}},"execution_count":1,"outputs":[]},{"cell_type":"markdown","source":["## Load dataset from Google Drive"],"metadata":{"id":"ziUa8uCi9rH0"}},{"cell_type":"code","source":["drive.mount('/content/drive', force_remount=True)\n","!unzip -q \"/content/drive/My Drive/Baldi_Cioffo/SplittedDataset.zip\" -d \"/content\""],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"j6w8FmzyT9cH","outputId":"41ec12ea-2a37-48a2-a432-4c6d7703d835","executionInfo":{"status":"ok","timestamp":1642022248858,"user_tz":-60,"elapsed":87690,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}}},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"markdown","source":["## Set Parameters"],"metadata":{"id":"Yxzg2PnmCPQN"}},{"cell_type":"code","source":["DATASET_PATH = \"/content/SplittedDataset\"\n","TRAINING_PATH = DATASET_PATH + '/training_set'\n","TRAINING_ALL_PATH = TRAINING_PATH + '/all'\n","TRAINING_HEM_PATH = TRAINING_PATH + '/hem'\n","VALIDATION_PATH = DATASET_PATH + '/validation_set'\n","TEST_PATH = DATASET_PATH + '/test_set'\n","\n","LOCAL_MODELS_FOLDER = \"/content/models\"\n","if not os.path.exists(LOCAL_MODELS_FOLDER):\n","  os.makedirs(LOCAL_MODELS_FOLDER)\n","GLOBAL_MODELS_FOLDER = \"/content/drive/My Drive/Baldi_Cioffo/models/mobilenet\"\n","if not os.path.exists(GLOBAL_MODELS_FOLDER):\n","  os.makedirs(GLOBAL_MODELS_FOLDER)\n","\n","# Default values to load the dataset\n","BATCH_SIZE = 32\n","IMAGE_HEIGHT = 224\n","IMAGE_WIDTH = 224"],"metadata":{"id":"4g8Gw3SoCW2-","executionInfo":{"status":"ok","timestamp":1642022248860,"user_tz":-60,"elapsed":56,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}}},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":["## Utility Functions"],"metadata":{"id":"NaDMLtupmIEk"}},{"cell_type":"code","source":["def set_seed ():\n","  ''' \n","  set_seed is used to obtain reproducible results using keras during the development phase\n","  '''\n","  seed = 24\n","  # The below is necessary for reproducible results of certain Python hash-based operations.\n","  os.environ[\"PYTHONHASHSEED\"]=\"0\"\n","  # The below is necessary for starting Numpy generated random numbers in a well-defined initial state.\n","  np.random.seed(seed)\n","  # The below is necessary for starting core Python generated random numbers in a well-defined state.\n","  rn.seed(seed)\n","  # The below tf.random.set_seed will make random number generation in TensorFlow have a well-defined initial state.\n","  tf.random.set_seed(seed)\n","\n","def show_training_and_validation_performance(history):\n","  '''\n","  show_training_and_validation_performance is used to plot the performances during the training phase\n","  :param history: object in which are recorded all the events\n","  '''\n","  acc = history.history['accuracy']\n","  val_acc = history.history['val_accuracy']\n","  loss = history.history['loss']\n","  val_loss = history.history['val_loss']\n","\n","  epochs = range(len(acc))\n","\n","  plt.plot(epochs, acc, 'bo', label='Training accuracy')\n","  plt.plot(epochs, val_acc, 'b', label='Validation accuracy')\n","  plt.title('Training and validation accuracy')\n","  plt.legend()\n","\n","  plt.figure()\n","\n","  plt.plot(epochs, loss, 'bo', label='Training loss')\n","  plt.plot(epochs, val_loss, 'b', label='Validation loss')\n","  plt.title('Training and validation loss')\n","  plt.legend()\n","\n","  plt.show()\n","\n","def load_training_set (image_height, image_width, batch_size):\n","  '''\n","  load_training_set loads the training set from the \"training_set\" folder. The images are resized to height x width\n","  :param image_height: standard height of the images\n","  :param image_width: standard width of the images\n","  :param batch_size: size of each batch\n","  :return the training set\n","  '''\n","  return image_dataset_from_directory(\n","    TRAINING_PATH,\n","    image_size = (image_height, image_width),\n","    batch_size = batch_size,\n","    class_names = ['hem', 'all']) # explicit ordering of the labels, 0 for hem, 1 for all\n","\n","def load_validation_set (image_height, image_width, batch_size):\n","  '''\n","  load_validation_set loads the validation set from the \"validation_set\" folder. The images are resized to height x width\n","  :param image_height: standard height of the images\n","  :param image_width: standard width of the images\n","  :param batch_size: size of each batch\n","  :return the validation set\n","  '''\n","  return image_dataset_from_directory(\n","    VALIDATION_PATH,\n","    image_size = (image_height, image_width),\n","    batch_size = batch_size,\n","    class_names = ['hem', 'all']) # explicit ordering of the labels, 0 for hem, 1 for all\n","\n","def load_test_set (image_height, image_width, batch_size):\n","  '''\n","  load_test_set loads the test set from the \"test_set\" folder. The images are resized to height x width\n","  :param image_height: standard height of the images\n","  :param image_width: standard width of the images\n","  :param batch_size: size of each batch\n","  :return the test set\n","  '''\n","  return image_dataset_from_directory(\n","    TEST_PATH,\n","    image_size = (image_height, image_width),\n","    batch_size = batch_size,\n","    shuffle = False,\n","    class_names = ['hem', 'all']) # explicit ordering of the labels, 0 for hem, 1 for all\n","\n","def compile_model (model, optimizer='adam', learning_rate = 0.001):\n","  '''\n","  compile_model is used to compile the current model\n","  :param model: model to compile\n","  :param optimizer: optimizer to be used\n","  :param learning_rate: learning rate parameter for the optimizer\n","  '''\n","  if optimizer == 'adam':\n","    model.compile(loss=\"binary_crossentropy\",\n","      optimizer=optimizers.Adam(learning_rate=learning_rate),\n","      metrics=[\"accuracy\"])\n","  elif optimizer == 'rmsprop':\n","    model.compile(loss=\"binary_crossentropy\",\n","                  optimizer = optimizers.RMSprop(learning_rate=learning_rate),\n","                  metrics=['accuracy'])\n","  model.summary()\n","\n","def run_model (model, model_name, epochs = 20, patience=5, monitor='val_loss'):\n","  '''\n","  run_model is used to run the current mode\n","  :param model: model to run\n","  :param model_name: name given to save the model\n","  :param epochs: how many epochs to do\n","  :param patience: patience value for Early Stopping\n","  :param monitor: what to monitor for Early Stopping and Model Checkpoint\n","  '''\n","  # local save path for the models\n","  save_path = LOCAL_MODELS_FOLDER + '/' + model_name + '.h5'\n","  callbacks_list = [\n","                  keras.callbacks.EarlyStopping(monitor=monitor, patience=patience),\n","                  keras.callbacks.ModelCheckpoint(\n","                      filepath = save_path,\n","                      monitor=monitor,\n","                      verbose=1,\n","                      save_best_only=True)\n","                  ]\n","  history = model.fit(train_dataset,\n","                    epochs=epochs,\n","                    validation_data=validation_dataset,\n","                    callbacks=callbacks_list)\n","  # save on Drive only the best model\n","  shutil.copy(save_path, GLOBAL_MODELS_FOLDER + '/' + model_name + '.h5')\n","  show_training_and_validation_performance(history)\n","\n","def evaluate_model (model):\n","  '''\n","  evaluate_model is used to plot some statistics about the performance on the test set\n","  :param model: model to consider\n","  '''\n","  y_score = model.predict(test_dataset)\n","  y_pred = np.rint(y_score) # to have 0 or 1\n","  y_true = tf.concat([labels_batch for data_batch, labels_batch in test_dataset], axis = 0)\n","  print(\"Classification report: \")\n","  print(metrics.classification_report(y_true,y_pred,digits = 4))\n","  metrics.ConfusionMatrixDisplay.from_predictions(y_true, y_pred)\n","\n","  # ROC curve\n","  fpr,tpr,th = metrics.roc_curve(y_true,y_score)\n","  roc_auc = metrics.roc_auc_score(y_true,y_score)\n","\n","  plt.figure()\n","  plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n","  plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\n","  plt.xlim([0.0, 1.0])\n","  plt.ylim([0.0, 1.0])\n","  plt.xlabel('False Positive Rate')\n","  plt.ylabel('True Positive Rate')\n","  plt.title('ROC curve')\n","  plt.legend(loc=\"lower right\")\n","  plt.show()"],"metadata":{"id":"fSX3ug4Cgwww","executionInfo":{"status":"ok","timestamp":1642022248861,"user_tz":-60,"elapsed":47,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}}},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":["## Load Dataset\n","The dataset, properly preprocessed, is now located in the local machine."],"metadata":{"id":"C884lZO-YiB2"}},{"cell_type":"code","source":["set_seed()"],"metadata":{"id":"Geqbk8VwPP3v","executionInfo":{"status":"ok","timestamp":1642022248862,"user_tz":-60,"elapsed":44,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}}},"execution_count":5,"outputs":[]},{"cell_type":"markdown","source":["At this point we need to transform the images we have on disk into preprocessed tensors. \n","\n","We decided to use the size that ResNet-50 expects as input, which is 224x224, and to use batches of size 32."],"metadata":{"id":"KPZzj069gq-1"}},{"cell_type":"code","source":["train_dataset = load_training_set(IMAGE_HEIGHT, IMAGE_WIDTH, BATCH_SIZE)\n","validation_dataset = load_validation_set(IMAGE_HEIGHT, IMAGE_WIDTH, BATCH_SIZE)\n","test_dataset = load_test_set(IMAGE_HEIGHT, IMAGE_WIDTH, BATCH_SIZE)"],"metadata":{"id":"0poDX9f3XRaK","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1642022257465,"user_tz":-60,"elapsed":8642,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"456805d8-1548-411f-adf8-770b18abef24"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Found 6396 files belonging to 2 classes.\n","Found 2132 files belonging to 2 classes.\n","Found 2133 files belonging to 2 classes.\n"]}]},{"cell_type":"markdown","source":["## Feature extraction"],"metadata":{"id":"zbGkxCHHC8cX"}},{"cell_type":"code","source":["conv_base = tf.keras.applications.MobileNetV2(\n","    input_shape=(IMAGE_HEIGHT,IMAGE_WIDTH,3),\n","    alpha=1,\n","    include_top= False,\n","    weights=\"imagenet\",\n",")"],"metadata":{"id":"8ryJvwgmC-U-","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1642020792021,"user_tz":-60,"elapsed":988,"user":{"displayName":"DANIELE CIOFFO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10062146559806518037"}},"outputId":"79faeb60-b483-414c-a9bb-d6af7f5d2b46"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n","9412608/9406464 [==============================] - 0s 0us/step\n","9420800/9406464 [==============================] - 0s 0us/step\n"]}]},{"cell_type":"code","source":["conv_base.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WFIQ47uGDTR5","executionInfo":{"status":"ok","timestamp":1641801184815,"user_tz":-60,"elapsed":417,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"39c957f7-b6f1-47c5-c91c-88814d1ec3e6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"mobilenetv2_1.00_224\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               \n","                                )]                                                                \n","                                                                                                  \n"," Conv1 (Conv2D)                 (None, 112, 112, 32  864         ['input_1[0][0]']                \n","                                )                                                                 \n","                                                                                                  \n"," bn_Conv1 (BatchNormalization)  (None, 112, 112, 32  128         ['Conv1[0][0]']                  \n","                                )                                                                 \n","                                                                                                  \n"," Conv1_relu (ReLU)              (None, 112, 112, 32  0           ['bn_Conv1[0][0]']               \n","                                )                                                                 \n","                                                                                                  \n"," expanded_conv_depthwise (Depth  (None, 112, 112, 32  288        ['Conv1_relu[0][0]']             \n"," wiseConv2D)                    )                                                                 \n","                                                                                                  \n"," expanded_conv_depthwise_BN (Ba  (None, 112, 112, 32  128        ['expanded_conv_depthwise[0][0]']\n"," tchNormalization)              )                                                                 \n","                                                                                                  \n"," expanded_conv_depthwise_relu (  (None, 112, 112, 32  0          ['expanded_conv_depthwise_BN[0][0\n"," ReLU)                          )                                ]']                              \n","                                                                                                  \n"," expanded_conv_project (Conv2D)  (None, 112, 112, 16  512        ['expanded_conv_depthwise_relu[0]\n","                                )                                [0]']                            \n","                                                                                                  \n"," expanded_conv_project_BN (Batc  (None, 112, 112, 16  64         ['expanded_conv_project[0][0]']  \n"," hNormalization)                )                                                                 \n","                                                                                                  \n"," block_1_expand (Conv2D)        (None, 112, 112, 96  1536        ['expanded_conv_project_BN[0][0]'\n","                                )                                ]                                \n","                                                                                                  \n"," block_1_expand_BN (BatchNormal  (None, 112, 112, 96  384        ['block_1_expand[0][0]']         \n"," ization)                       )                                                                 \n","                                                                                                  \n"," block_1_expand_relu (ReLU)     (None, 112, 112, 96  0           ['block_1_expand_BN[0][0]']      \n","                                )                                                                 \n","                                                                                                  \n"," block_1_pad (ZeroPadding2D)    (None, 113, 113, 96  0           ['block_1_expand_relu[0][0]']    \n","                                )                                                                 \n","                                                                                                  \n"," block_1_depthwise (DepthwiseCo  (None, 56, 56, 96)  864         ['block_1_pad[0][0]']            \n"," nv2D)                                                                                            \n","                                                                                                  \n"," block_1_depthwise_BN (BatchNor  (None, 56, 56, 96)  384         ['block_1_depthwise[0][0]']      \n"," malization)                                                                                      \n","                                                                                                  \n"," block_1_depthwise_relu (ReLU)  (None, 56, 56, 96)   0           ['block_1_depthwise_BN[0][0]']   \n","                                                                                                  \n"," block_1_project (Conv2D)       (None, 56, 56, 24)   2304        ['block_1_depthwise_relu[0][0]'] \n","                                                                                                  \n"," block_1_project_BN (BatchNorma  (None, 56, 56, 24)  96          ['block_1_project[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_2_expand (Conv2D)        (None, 56, 56, 144)  3456        ['block_1_project_BN[0][0]']     \n","                                                                                                  \n"," block_2_expand_BN (BatchNormal  (None, 56, 56, 144)  576        ['block_2_expand[0][0]']         \n"," ization)                                                                                         \n","                                                                                                  \n"," block_2_expand_relu (ReLU)     (None, 56, 56, 144)  0           ['block_2_expand_BN[0][0]']      \n","                                                                                                  \n"," block_2_depthwise (DepthwiseCo  (None, 56, 56, 144)  1296       ['block_2_expand_relu[0][0]']    \n"," nv2D)                                                                                            \n","                                                                                                  \n"," block_2_depthwise_BN (BatchNor  (None, 56, 56, 144)  576        ['block_2_depthwise[0][0]']      \n"," malization)                                                                                      \n","                                                                                                  \n"," block_2_depthwise_relu (ReLU)  (None, 56, 56, 144)  0           ['block_2_depthwise_BN[0][0]']   \n","                                                                                                  \n"," block_2_project (Conv2D)       (None, 56, 56, 24)   3456        ['block_2_depthwise_relu[0][0]'] \n","                                                                                                  \n"," block_2_project_BN (BatchNorma  (None, 56, 56, 24)  96          ['block_2_project[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_2_add (Add)              (None, 56, 56, 24)   0           ['block_1_project_BN[0][0]',     \n","                                                                  'block_2_project_BN[0][0]']     \n","                                                                                                  \n"," block_3_expand (Conv2D)        (None, 56, 56, 144)  3456        ['block_2_add[0][0]']            \n","                                                                                                  \n"," block_3_expand_BN (BatchNormal  (None, 56, 56, 144)  576        ['block_3_expand[0][0]']         \n"," ization)                                                                                         \n","                                                                                                  \n"," block_3_expand_relu (ReLU)     (None, 56, 56, 144)  0           ['block_3_expand_BN[0][0]']      \n","                                                                                                  \n"," block_3_pad (ZeroPadding2D)    (None, 57, 57, 144)  0           ['block_3_expand_relu[0][0]']    \n","                                                                                                  \n"," block_3_depthwise (DepthwiseCo  (None, 28, 28, 144)  1296       ['block_3_pad[0][0]']            \n"," nv2D)                                                                                            \n","                                                                                                  \n"," block_3_depthwise_BN (BatchNor  (None, 28, 28, 144)  576        ['block_3_depthwise[0][0]']      \n"," malization)                                                                                      \n","                                                                                                  \n"," block_3_depthwise_relu (ReLU)  (None, 28, 28, 144)  0           ['block_3_depthwise_BN[0][0]']   \n","                                                                                                  \n"," block_3_project (Conv2D)       (None, 28, 28, 32)   4608        ['block_3_depthwise_relu[0][0]'] \n","                                                                                                  \n"," block_3_project_BN (BatchNorma  (None, 28, 28, 32)  128         ['block_3_project[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_4_expand (Conv2D)        (None, 28, 28, 192)  6144        ['block_3_project_BN[0][0]']     \n","                                                                                                  \n"," block_4_expand_BN (BatchNormal  (None, 28, 28, 192)  768        ['block_4_expand[0][0]']         \n"," ization)                                                                                         \n","                                                                                                  \n"," block_4_expand_relu (ReLU)     (None, 28, 28, 192)  0           ['block_4_expand_BN[0][0]']      \n","                                                                                                  \n"," block_4_depthwise (DepthwiseCo  (None, 28, 28, 192)  1728       ['block_4_expand_relu[0][0]']    \n"," nv2D)                                                                                            \n","                                                                                                  \n"," block_4_depthwise_BN (BatchNor  (None, 28, 28, 192)  768        ['block_4_depthwise[0][0]']      \n"," malization)                                                                                      \n","                                                                                                  \n"," block_4_depthwise_relu (ReLU)  (None, 28, 28, 192)  0           ['block_4_depthwise_BN[0][0]']   \n","                                                                                                  \n"," block_4_project (Conv2D)       (None, 28, 28, 32)   6144        ['block_4_depthwise_relu[0][0]'] \n","                                                                                                  \n"," block_4_project_BN (BatchNorma  (None, 28, 28, 32)  128         ['block_4_project[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_4_add (Add)              (None, 28, 28, 32)   0           ['block_3_project_BN[0][0]',     \n","                                                                  'block_4_project_BN[0][0]']     \n","                                                                                                  \n"," block_5_expand (Conv2D)        (None, 28, 28, 192)  6144        ['block_4_add[0][0]']            \n","                                                                                                  \n"," block_5_expand_BN (BatchNormal  (None, 28, 28, 192)  768        ['block_5_expand[0][0]']         \n"," ization)                                                                                         \n","                                                                                                  \n"," block_5_expand_relu (ReLU)     (None, 28, 28, 192)  0           ['block_5_expand_BN[0][0]']      \n","                                                                                                  \n"," block_5_depthwise (DepthwiseCo  (None, 28, 28, 192)  1728       ['block_5_expand_relu[0][0]']    \n"," nv2D)                                                                                            \n","                                                                                                  \n"," block_5_depthwise_BN (BatchNor  (None, 28, 28, 192)  768        ['block_5_depthwise[0][0]']      \n"," malization)                                                                                      \n","                                                                                                  \n"," block_5_depthwise_relu (ReLU)  (None, 28, 28, 192)  0           ['block_5_depthwise_BN[0][0]']   \n","                                                                                                  \n"," block_5_project (Conv2D)       (None, 28, 28, 32)   6144        ['block_5_depthwise_relu[0][0]'] \n","                                                                                                  \n"," block_5_project_BN (BatchNorma  (None, 28, 28, 32)  128         ['block_5_project[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_5_add (Add)              (None, 28, 28, 32)   0           ['block_4_add[0][0]',            \n","                                                                  'block_5_project_BN[0][0]']     \n","                                                                                                  \n"," block_6_expand (Conv2D)        (None, 28, 28, 192)  6144        ['block_5_add[0][0]']            \n","                                                                                                  \n"," block_6_expand_BN (BatchNormal  (None, 28, 28, 192)  768        ['block_6_expand[0][0]']         \n"," ization)                                                                                         \n","                                                                                                  \n"," block_6_expand_relu (ReLU)     (None, 28, 28, 192)  0           ['block_6_expand_BN[0][0]']      \n","                                                                                                  \n"," block_6_pad (ZeroPadding2D)    (None, 29, 29, 192)  0           ['block_6_expand_relu[0][0]']    \n","                                                                                                  \n"," block_6_depthwise (DepthwiseCo  (None, 14, 14, 192)  1728       ['block_6_pad[0][0]']            \n"," nv2D)                                                                                            \n","                                                                                                  \n"," block_6_depthwise_BN (BatchNor  (None, 14, 14, 192)  768        ['block_6_depthwise[0][0]']      \n"," malization)                                                                                      \n","                                                                                                  \n"," block_6_depthwise_relu (ReLU)  (None, 14, 14, 192)  0           ['block_6_depthwise_BN[0][0]']   \n","                                                                                                  \n"," block_6_project (Conv2D)       (None, 14, 14, 64)   12288       ['block_6_depthwise_relu[0][0]'] \n","                                                                                                  \n"," block_6_project_BN (BatchNorma  (None, 14, 14, 64)  256         ['block_6_project[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_7_expand (Conv2D)        (None, 14, 14, 384)  24576       ['block_6_project_BN[0][0]']     \n","                                                                                                  \n"," block_7_expand_BN (BatchNormal  (None, 14, 14, 384)  1536       ['block_7_expand[0][0]']         \n"," ization)                                                                                         \n","                                                                                                  \n"," block_7_expand_relu (ReLU)     (None, 14, 14, 384)  0           ['block_7_expand_BN[0][0]']      \n","                                                                                                  \n"," block_7_depthwise (DepthwiseCo  (None, 14, 14, 384)  3456       ['block_7_expand_relu[0][0]']    \n"," nv2D)                                                                                            \n","                                                                                                  \n"," block_7_depthwise_BN (BatchNor  (None, 14, 14, 384)  1536       ['block_7_depthwise[0][0]']      \n"," malization)                                                                                      \n","                                                                                                  \n"," block_7_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           ['block_7_depthwise_BN[0][0]']   \n","                                                                                                  \n"," block_7_project (Conv2D)       (None, 14, 14, 64)   24576       ['block_7_depthwise_relu[0][0]'] \n","                                                                                                  \n"," block_7_project_BN (BatchNorma  (None, 14, 14, 64)  256         ['block_7_project[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_7_add (Add)              (None, 14, 14, 64)   0           ['block_6_project_BN[0][0]',     \n","                                                                  'block_7_project_BN[0][0]']     \n","                                                                                                  \n"," block_8_expand (Conv2D)        (None, 14, 14, 384)  24576       ['block_7_add[0][0]']            \n","                                                                                                  \n"," block_8_expand_BN (BatchNormal  (None, 14, 14, 384)  1536       ['block_8_expand[0][0]']         \n"," ization)                                                                                         \n","                                                                                                  \n"," block_8_expand_relu (ReLU)     (None, 14, 14, 384)  0           ['block_8_expand_BN[0][0]']      \n","                                                                                                  \n"," block_8_depthwise (DepthwiseCo  (None, 14, 14, 384)  3456       ['block_8_expand_relu[0][0]']    \n"," nv2D)                                                                                            \n","                                                                                                  \n"," block_8_depthwise_BN (BatchNor  (None, 14, 14, 384)  1536       ['block_8_depthwise[0][0]']      \n"," malization)                                                                                      \n","                                                                                                  \n"," block_8_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           ['block_8_depthwise_BN[0][0]']   \n","                                                                                                  \n"," block_8_project (Conv2D)       (None, 14, 14, 64)   24576       ['block_8_depthwise_relu[0][0]'] \n","                                                                                                  \n"," block_8_project_BN (BatchNorma  (None, 14, 14, 64)  256         ['block_8_project[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_8_add (Add)              (None, 14, 14, 64)   0           ['block_7_add[0][0]',            \n","                                                                  'block_8_project_BN[0][0]']     \n","                                                                                                  \n"," block_9_expand (Conv2D)        (None, 14, 14, 384)  24576       ['block_8_add[0][0]']            \n","                                                                                                  \n"," block_9_expand_BN (BatchNormal  (None, 14, 14, 384)  1536       ['block_9_expand[0][0]']         \n"," ization)                                                                                         \n","                                                                                                  \n"," block_9_expand_relu (ReLU)     (None, 14, 14, 384)  0           ['block_9_expand_BN[0][0]']      \n","                                                                                                  \n"," block_9_depthwise (DepthwiseCo  (None, 14, 14, 384)  3456       ['block_9_expand_relu[0][0]']    \n"," nv2D)                                                                                            \n","                                                                                                  \n"," block_9_depthwise_BN (BatchNor  (None, 14, 14, 384)  1536       ['block_9_depthwise[0][0]']      \n"," malization)                                                                                      \n","                                                                                                  \n"," block_9_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           ['block_9_depthwise_BN[0][0]']   \n","                                                                                                  \n"," block_9_project (Conv2D)       (None, 14, 14, 64)   24576       ['block_9_depthwise_relu[0][0]'] \n","                                                                                                  \n"," block_9_project_BN (BatchNorma  (None, 14, 14, 64)  256         ['block_9_project[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_9_add (Add)              (None, 14, 14, 64)   0           ['block_8_add[0][0]',            \n","                                                                  'block_9_project_BN[0][0]']     \n","                                                                                                  \n"," block_10_expand (Conv2D)       (None, 14, 14, 384)  24576       ['block_9_add[0][0]']            \n","                                                                                                  \n"," block_10_expand_BN (BatchNorma  (None, 14, 14, 384)  1536       ['block_10_expand[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_10_expand_relu (ReLU)    (None, 14, 14, 384)  0           ['block_10_expand_BN[0][0]']     \n","                                                                                                  \n"," block_10_depthwise (DepthwiseC  (None, 14, 14, 384)  3456       ['block_10_expand_relu[0][0]']   \n"," onv2D)                                                                                           \n","                                                                                                  \n"," block_10_depthwise_BN (BatchNo  (None, 14, 14, 384)  1536       ['block_10_depthwise[0][0]']     \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block_10_depthwise_relu (ReLU)  (None, 14, 14, 384)  0          ['block_10_depthwise_BN[0][0]']  \n","                                                                                                  \n"," block_10_project (Conv2D)      (None, 14, 14, 96)   36864       ['block_10_depthwise_relu[0][0]']\n","                                                                                                  \n"," block_10_project_BN (BatchNorm  (None, 14, 14, 96)  384         ['block_10_project[0][0]']       \n"," alization)                                                                                       \n","                                                                                                  \n"," block_11_expand (Conv2D)       (None, 14, 14, 576)  55296       ['block_10_project_BN[0][0]']    \n","                                                                                                  \n"," block_11_expand_BN (BatchNorma  (None, 14, 14, 576)  2304       ['block_11_expand[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_11_expand_relu (ReLU)    (None, 14, 14, 576)  0           ['block_11_expand_BN[0][0]']     \n","                                                                                                  \n"," block_11_depthwise (DepthwiseC  (None, 14, 14, 576)  5184       ['block_11_expand_relu[0][0]']   \n"," onv2D)                                                                                           \n","                                                                                                  \n"," block_11_depthwise_BN (BatchNo  (None, 14, 14, 576)  2304       ['block_11_depthwise[0][0]']     \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block_11_depthwise_relu (ReLU)  (None, 14, 14, 576)  0          ['block_11_depthwise_BN[0][0]']  \n","                                                                                                  \n"," block_11_project (Conv2D)      (None, 14, 14, 96)   55296       ['block_11_depthwise_relu[0][0]']\n","                                                                                                  \n"," block_11_project_BN (BatchNorm  (None, 14, 14, 96)  384         ['block_11_project[0][0]']       \n"," alization)                                                                                       \n","                                                                                                  \n"," block_11_add (Add)             (None, 14, 14, 96)   0           ['block_10_project_BN[0][0]',    \n","                                                                  'block_11_project_BN[0][0]']    \n","                                                                                                  \n"," block_12_expand (Conv2D)       (None, 14, 14, 576)  55296       ['block_11_add[0][0]']           \n","                                                                                                  \n"," block_12_expand_BN (BatchNorma  (None, 14, 14, 576)  2304       ['block_12_expand[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_12_expand_relu (ReLU)    (None, 14, 14, 576)  0           ['block_12_expand_BN[0][0]']     \n","                                                                                                  \n"," block_12_depthwise (DepthwiseC  (None, 14, 14, 576)  5184       ['block_12_expand_relu[0][0]']   \n"," onv2D)                                                                                           \n","                                                                                                  \n"," block_12_depthwise_BN (BatchNo  (None, 14, 14, 576)  2304       ['block_12_depthwise[0][0]']     \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block_12_depthwise_relu (ReLU)  (None, 14, 14, 576)  0          ['block_12_depthwise_BN[0][0]']  \n","                                                                                                  \n"," block_12_project (Conv2D)      (None, 14, 14, 96)   55296       ['block_12_depthwise_relu[0][0]']\n","                                                                                                  \n"," block_12_project_BN (BatchNorm  (None, 14, 14, 96)  384         ['block_12_project[0][0]']       \n"," alization)                                                                                       \n","                                                                                                  \n"," block_12_add (Add)             (None, 14, 14, 96)   0           ['block_11_add[0][0]',           \n","                                                                  'block_12_project_BN[0][0]']    \n","                                                                                                  \n"," block_13_expand (Conv2D)       (None, 14, 14, 576)  55296       ['block_12_add[0][0]']           \n","                                                                                                  \n"," block_13_expand_BN (BatchNorma  (None, 14, 14, 576)  2304       ['block_13_expand[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_13_expand_relu (ReLU)    (None, 14, 14, 576)  0           ['block_13_expand_BN[0][0]']     \n","                                                                                                  \n"," block_13_pad (ZeroPadding2D)   (None, 15, 15, 576)  0           ['block_13_expand_relu[0][0]']   \n","                                                                                                  \n"," block_13_depthwise (DepthwiseC  (None, 7, 7, 576)   5184        ['block_13_pad[0][0]']           \n"," onv2D)                                                                                           \n","                                                                                                  \n"," block_13_depthwise_BN (BatchNo  (None, 7, 7, 576)   2304        ['block_13_depthwise[0][0]']     \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block_13_depthwise_relu (ReLU)  (None, 7, 7, 576)   0           ['block_13_depthwise_BN[0][0]']  \n","                                                                                                  \n"," block_13_project (Conv2D)      (None, 7, 7, 160)    92160       ['block_13_depthwise_relu[0][0]']\n","                                                                                                  \n"," block_13_project_BN (BatchNorm  (None, 7, 7, 160)   640         ['block_13_project[0][0]']       \n"," alization)                                                                                       \n","                                                                                                  \n"," block_14_expand (Conv2D)       (None, 7, 7, 960)    153600      ['block_13_project_BN[0][0]']    \n","                                                                                                  \n"," block_14_expand_BN (BatchNorma  (None, 7, 7, 960)   3840        ['block_14_expand[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_14_expand_relu (ReLU)    (None, 7, 7, 960)    0           ['block_14_expand_BN[0][0]']     \n","                                                                                                  \n"," block_14_depthwise (DepthwiseC  (None, 7, 7, 960)   8640        ['block_14_expand_relu[0][0]']   \n"," onv2D)                                                                                           \n","                                                                                                  \n"," block_14_depthwise_BN (BatchNo  (None, 7, 7, 960)   3840        ['block_14_depthwise[0][0]']     \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block_14_depthwise_relu (ReLU)  (None, 7, 7, 960)   0           ['block_14_depthwise_BN[0][0]']  \n","                                                                                                  \n"," block_14_project (Conv2D)      (None, 7, 7, 160)    153600      ['block_14_depthwise_relu[0][0]']\n","                                                                                                  \n"," block_14_project_BN (BatchNorm  (None, 7, 7, 160)   640         ['block_14_project[0][0]']       \n"," alization)                                                                                       \n","                                                                                                  \n"," block_14_add (Add)             (None, 7, 7, 160)    0           ['block_13_project_BN[0][0]',    \n","                                                                  'block_14_project_BN[0][0]']    \n","                                                                                                  \n"," block_15_expand (Conv2D)       (None, 7, 7, 960)    153600      ['block_14_add[0][0]']           \n","                                                                                                  \n"," block_15_expand_BN (BatchNorma  (None, 7, 7, 960)   3840        ['block_15_expand[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_15_expand_relu (ReLU)    (None, 7, 7, 960)    0           ['block_15_expand_BN[0][0]']     \n","                                                                                                  \n"," block_15_depthwise (DepthwiseC  (None, 7, 7, 960)   8640        ['block_15_expand_relu[0][0]']   \n"," onv2D)                                                                                           \n","                                                                                                  \n"," block_15_depthwise_BN (BatchNo  (None, 7, 7, 960)   3840        ['block_15_depthwise[0][0]']     \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block_15_depthwise_relu (ReLU)  (None, 7, 7, 960)   0           ['block_15_depthwise_BN[0][0]']  \n","                                                                                                  \n"," block_15_project (Conv2D)      (None, 7, 7, 160)    153600      ['block_15_depthwise_relu[0][0]']\n","                                                                                                  \n"," block_15_project_BN (BatchNorm  (None, 7, 7, 160)   640         ['block_15_project[0][0]']       \n"," alization)                                                                                       \n","                                                                                                  \n"," block_15_add (Add)             (None, 7, 7, 160)    0           ['block_14_add[0][0]',           \n","                                                                  'block_15_project_BN[0][0]']    \n","                                                                                                  \n"," block_16_expand (Conv2D)       (None, 7, 7, 960)    153600      ['block_15_add[0][0]']           \n","                                                                                                  \n"," block_16_expand_BN (BatchNorma  (None, 7, 7, 960)   3840        ['block_16_expand[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_16_expand_relu (ReLU)    (None, 7, 7, 960)    0           ['block_16_expand_BN[0][0]']     \n","                                                                                                  \n"," block_16_depthwise (DepthwiseC  (None, 7, 7, 960)   8640        ['block_16_expand_relu[0][0]']   \n"," onv2D)                                                                                           \n","                                                                                                  \n"," block_16_depthwise_BN (BatchNo  (None, 7, 7, 960)   3840        ['block_16_depthwise[0][0]']     \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block_16_depthwise_relu (ReLU)  (None, 7, 7, 960)   0           ['block_16_depthwise_BN[0][0]']  \n","                                                                                                  \n"," block_16_project (Conv2D)      (None, 7, 7, 320)    307200      ['block_16_depthwise_relu[0][0]']\n","                                                                                                  \n"," block_16_project_BN (BatchNorm  (None, 7, 7, 320)   1280        ['block_16_project[0][0]']       \n"," alization)                                                                                       \n","                                                                                                  \n"," Conv_1 (Conv2D)                (None, 7, 7, 1280)   409600      ['block_16_project_BN[0][0]']    \n","                                                                                                  \n"," Conv_1_bn (BatchNormalization)  (None, 7, 7, 1280)  5120        ['Conv_1[0][0]']                 \n","                                                                                                  \n"," out_relu (ReLU)                (None, 7, 7, 1280)   0           ['Conv_1_bn[0][0]']              \n","                                                                                                  \n","==================================================================================================\n","Total params: 2,257,984\n","Trainable params: 2,223,872\n","Non-trainable params: 34,112\n","__________________________________________________________________________________________________\n"]}]},{"cell_type":"code","source":["conv_base.trainable = False"],"metadata":{"id":"H5nPQ3IBDkXr"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Experiment 1\n","In this first experiment we try a simple configuration with a single dense layer before the output layer, without regularization techniques."],"metadata":{"id":"vWhE7LMaDokI"}},{"cell_type":"code","source":["inputs = keras.Input(shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3))\n","x = tf.keras.applications.mobilenet.preprocess_input(inputs)\n","x = conv_base(x) # feature extraction\n","x = layers.Flatten()(x)\n","outputs = layers.Dense(1, activation='sigmoid')(x)\n","model1 = keras.Model(inputs=inputs, outputs=outputs)\n","compile_model(model1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8aOOWkBnDqWJ","executionInfo":{"status":"ok","timestamp":1641393073795,"user_tz":-60,"elapsed":794,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"5c3d26c5-c8ef-4714-ae9b-1e0839c031cb"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model_6\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input_11 (InputLayer)       [(None, 224, 224, 3)]     0         \n","                                                                 \n"," tf.math.truediv_6 (TFOpLamb  (None, 224, 224, 3)      0         \n"," da)                                                             \n","                                                                 \n"," tf.math.subtract_6 (TFOpLam  (None, 224, 224, 3)      0         \n"," bda)                                                            \n","                                                                 \n"," mobilenetv2_1.00_224 (Funct  (None, 7, 7, 1280)       2257984   \n"," ional)                                                          \n","                                                                 \n"," flatten_4 (Flatten)         (None, 62720)             0         \n","                                                                 \n"," dense_6 (Dense)             (None, 1)                 62721     \n","                                                                 \n","=================================================================\n","Total params: 2,320,705\n","Trainable params: 62,721\n","Non-trainable params: 2,257,984\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["run_model(model1, 'experiment1_feature_extraction', epochs = 30, patience=5)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"AKIa2jE9E3Lf","executionInfo":{"status":"ok","timestamp":1641393351253,"user_tz":-60,"elapsed":274712,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"60e7ff09-dd41-4289-b6d2-b6d18d6fc8eb"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/30\n","200/200 [==============================] - ETA: 0s - loss: 1.1425 - accuracy: 0.7097\n","Epoch 00001: val_loss improved from inf to 0.65521, saving model to /content/models/experiment1_feature_extraction.h5\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n","  layer_config = serialize_layer_fn(layer)\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r200/200 [==============================] - 29s 129ms/step - loss: 1.1425 - accuracy: 0.7097 - val_loss: 0.6552 - val_accuracy: 0.8194\n","Epoch 2/30\n","200/200 [==============================] - ETA: 0s - loss: 0.6546 - accuracy: 0.8007\n","Epoch 00002: val_loss improved from 0.65521 to 0.64712, saving model to /content/models/experiment1_feature_extraction.h5\n","200/200 [==============================] - 25s 125ms/step - loss: 0.6546 - accuracy: 0.8007 - val_loss: 0.6471 - val_accuracy: 0.8194\n","Epoch 3/30\n","200/200 [==============================] - ETA: 0s - loss: 0.3878 - accuracy: 0.8643\n","Epoch 00003: val_loss did not improve from 0.64712\n","200/200 [==============================] - 25s 123ms/step - loss: 0.3878 - accuracy: 0.8643 - val_loss: 0.7316 - val_accuracy: 0.8269\n","Epoch 4/30\n","200/200 [==============================] - ETA: 0s - loss: 0.3217 - accuracy: 0.8870\n","Epoch 00004: val_loss did not improve from 0.64712\n","200/200 [==============================] - 25s 122ms/step - loss: 0.3217 - accuracy: 0.8870 - val_loss: 0.6711 - val_accuracy: 0.8321\n","Epoch 5/30\n","200/200 [==============================] - ETA: 0s - loss: 0.1603 - accuracy: 0.9350\n","Epoch 00005: val_loss did not improve from 0.64712\n","200/200 [==============================] - 25s 122ms/step - loss: 0.1603 - accuracy: 0.9350 - val_loss: 0.7472 - val_accuracy: 0.8232\n","Epoch 6/30\n","200/200 [==============================] - ETA: 0s - loss: 0.2207 - accuracy: 0.9206\n","Epoch 00006: val_loss did not improve from 0.64712\n","200/200 [==============================] - 25s 123ms/step - loss: 0.2207 - accuracy: 0.9206 - val_loss: 0.6761 - val_accuracy: 0.8269\n","Epoch 7/30\n","200/200 [==============================] - ETA: 0s - loss: 0.1179 - accuracy: 0.9548\n","Epoch 00007: val_loss did not improve from 0.64712\n","200/200 [==============================] - 25s 122ms/step - loss: 0.1179 - accuracy: 0.9548 - val_loss: 0.8050 - val_accuracy: 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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["evaluate_model(tf.keras.models.load_model(GLOBAL_MODELS_FOLDER + '/experiment1_feature_extraction.h5'))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":730},"id":"hHIye421Jlrh","executionInfo":{"status":"ok","timestamp":1642022279884,"user_tz":-60,"elapsed":21918,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"7202e8c6-fc24-4955-e6b7-8fbdebb3f340"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["Classification report: \n","              precision    recall  f1-score   support\n","\n","           0     0.7487    0.6293    0.6838       696\n","           1     0.8333    0.8977    0.8643      1437\n","\n","    accuracy                         0.8101      2133\n","   macro avg     0.7910    0.7635    0.7741      2133\n","weighted avg     0.8057    0.8101    0.8054      2133\n","\n"]},{"output_type":"display_data","data":{"image/png":"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size 432x288 with 2 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["### Experiment 2"],"metadata":{"id":"-tODvMNaq9nU"}},{"cell_type":"code","source":["inputs = keras.Input(shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3))\n","x = tf.keras.applications.mobilenet.preprocess_input(inputs)\n","x = conv_base(x) # feature extraction\n","x = layers.GlobalAveragePooling2D()(x)\n","x = layers.Dense(256)(x)\n","x = layers.Dropout(0.5)(x)\n","outputs = layers.Dense(1, activation='sigmoid')(x)\n","model2 = keras.Model(inputs=inputs, outputs=outputs)\n","compile_model(model2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SZYjhBRyrAQN","executionInfo":{"status":"ok","timestamp":1641393645364,"user_tz":-60,"elapsed":1029,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"9508b5ab-9857-41f9-d7ff-c9de71604147"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model_10\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input_15 (InputLayer)       [(None, 224, 224, 3)]     0         \n","                                                                 \n"," tf.math.truediv_10 (TFOpLam  (None, 224, 224, 3)      0         \n"," bda)                                                            \n","                                                                 \n"," tf.math.subtract_10 (TFOpLa  (None, 224, 224, 3)      0         \n"," mbda)                                                           \n","                                                                 \n"," mobilenetv2_1.00_224 (Funct  (None, 7, 7, 1280)       2257984   \n"," ional)                                                          \n","                                                                 \n"," global_average_pooling2d_1   (None, 1280)             0         \n"," (GlobalAveragePooling2D)                                        \n","                                                                 \n"," dense_13 (Dense)            (None, 256)               327936    \n","                                                                 \n"," dropout_3 (Dropout)         (None, 256)               0         \n","                                                                 \n"," dense_14 (Dense)            (None, 1)                 257       \n","                                                                 \n","=================================================================\n","Total params: 2,586,177\n","Trainable params: 328,193\n","Non-trainable params: 2,257,984\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["run_model(model2, 'experiment2_feature_extraction', epochs = 30, patience=5)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"x3w4jhTurogJ","executionInfo":{"status":"ok","timestamp":1641394026015,"user_tz":-60,"elapsed":321214,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"cc763ed1-bbfd-4367-b296-3fb043aa4a23"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/30\n","200/200 [==============================] - ETA: 0s - loss: 0.7645 - accuracy: 0.6873\n","Epoch 00001: val_loss improved from inf to 0.46384, saving model to /content/models/experiment2_feature_extraction.h5\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n","  layer_config = serialize_layer_fn(layer)\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r200/200 [==============================] - 65s 310ms/step - loss: 0.7645 - accuracy: 0.6873 - val_loss: 0.4638 - val_accuracy: 0.8077\n","Epoch 2/30\n","200/200 [==============================] - ETA: 0s - loss: 0.5459 - accuracy: 0.7356\n","Epoch 00002: val_loss improved from 0.46384 to 0.45688, saving model to /content/models/experiment2_feature_extraction.h5\n","200/200 [==============================] - 26s 126ms/step - loss: 0.5459 - accuracy: 0.7356 - val_loss: 0.4569 - val_accuracy: 0.7791\n","Epoch 3/30\n","200/200 [==============================] - ETA: 0s - loss: 0.5148 - accuracy: 0.7552\n","Epoch 00003: val_loss improved from 0.45688 to 0.41699, saving model to /content/models/experiment2_feature_extraction.h5\n","200/200 [==============================] - 26s 127ms/step - loss: 0.5148 - accuracy: 0.7552 - val_loss: 0.4170 - val_accuracy: 0.8124\n","Epoch 4/30\n","200/200 [==============================] - ETA: 0s - loss: 0.4925 - accuracy: 0.7633\n","Epoch 00004: val_loss did not improve from 0.41699\n","200/200 [==============================] - 25s 124ms/step - loss: 0.4925 - accuracy: 0.7633 - val_loss: 0.5779 - val_accuracy: 0.6928\n","Epoch 5/30\n","200/200 [==============================] - ETA: 0s - loss: 0.4919 - accuracy: 0.7631\n","Epoch 00005: val_loss did not improve from 0.41699\n","200/200 [==============================] - 25s 124ms/step - loss: 0.4919 - accuracy: 0.7631 - val_loss: 0.4391 - val_accuracy: 0.7969\n","Epoch 6/30\n","200/200 [==============================] - ETA: 0s - loss: 0.4552 - accuracy: 0.7786\n","Epoch 00006: val_loss did not improve from 0.41699\n","200/200 [==============================] - 25s 124ms/step - loss: 0.4552 - accuracy: 0.7786 - val_loss: 0.4678 - val_accuracy: 0.7725\n","Epoch 7/30\n","200/200 [==============================] - ETA: 0s - loss: 0.4678 - accuracy: 0.7789\n","Epoch 00007: val_loss did not improve from 0.41699\n","200/200 [==============================] - 25s 124ms/step - loss: 0.4678 - accuracy: 0.7789 - val_loss: 0.5231 - val_accuracy: 0.7359\n","Epoch 8/30\n","200/200 [==============================] - ETA: 0s - loss: 0.4625 - accuracy: 0.7844\n","Epoch 00008: val_loss did not improve from 0.41699\n","200/200 [==============================] - 25s 125ms/step - loss: 0.4625 - accuracy: 0.7844 - val_loss: 0.4566 - val_accuracy: 0.8110\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["evaluate_model(tf.keras.models.load_model(GLOBAL_MODELS_FOLDER + '/experiment2_feature_extraction.h5'))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":730},"id":"k5D5ngJXJo98","executionInfo":{"status":"ok","timestamp":1642022292394,"user_tz":-60,"elapsed":12515,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"80edc1b4-7257-4b04-b964-20a8190f4ab5"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["Classification report: \n","              precision    recall  f1-score   support\n","\n","           0     0.7358    0.6681    0.7003       696\n","           1     0.8461    0.8838    0.8645      1437\n","\n","    accuracy                         0.8134      2133\n","   macro avg     0.7909    0.7759    0.7824      2133\n","weighted avg     0.8101    0.8134    0.8109      2133\n","\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["### Experiment 3"],"metadata":{"id":"-37iw3LHywAO"}},{"cell_type":"code","source":["inputs = keras.Input(shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3))\n","x = tf.keras.applications.mobilenet.preprocess_input(inputs)\n","x = conv_base(x) # feature extraction\n","x = layers.GlobalAveragePooling2D()(x)\n","x = layers.Dropout(0.5)(x)\n","outputs = layers.Dense(1, activation='sigmoid')(x)\n","model3 = keras.Model(inputs=inputs, outputs=outputs)\n","compile_model(model3)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RVaPklsjyo86","executionInfo":{"status":"ok","timestamp":1641395451237,"user_tz":-60,"elapsed":1286,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"d9961a04-9504-4c72-f798-2872467179fc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model_12\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input_17 (InputLayer)       [(None, 224, 224, 3)]     0         \n","                                                                 \n"," tf.math.truediv_12 (TFOpLam  (None, 224, 224, 3)      0         \n"," bda)                                                            \n","                                                                 \n"," tf.math.subtract_12 (TFOpLa  (None, 224, 224, 3)      0         \n"," mbda)                                                           \n","                                                                 \n"," mobilenetv2_1.00_224 (Funct  (None, 7, 7, 1280)       2257984   \n"," ional)                                                          \n","                                                                 \n"," global_average_pooling2d_3   (None, 1280)             0         \n"," (GlobalAveragePooling2D)                                        \n","                                                                 \n"," dropout_5 (Dropout)         (None, 1280)              0         \n","                                                                 \n"," dense_16 (Dense)            (None, 1)                 1281      \n","                                                                 \n","=================================================================\n","Total params: 2,259,265\n","Trainable params: 1,281\n","Non-trainable params: 2,257,984\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["run_model(model3, 'experiment3_feature_extraction', epochs = 50, patience=5)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"LGsJme8JytZO","executionInfo":{"status":"ok","timestamp":1641396011472,"user_tz":-60,"elapsed":538401,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"12569c73-1a6f-46d4-b83c-95022ba4bee5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/50\n","200/200 [==============================] - ETA: 0s - loss: 0.6595 - accuracy: 0.6560\n","Epoch 00001: val_loss improved from inf to 0.52367, saving model to /content/models/experiment3_feature_extraction.h5\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n","  layer_config = serialize_layer_fn(layer)\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r200/200 [==============================] - 29s 128ms/step - loss: 0.6595 - accuracy: 0.6560 - val_loss: 0.5237 - val_accuracy: 0.7402\n","Epoch 2/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5606 - accuracy: 0.7145\n","Epoch 00002: val_loss improved from 0.52367 to 0.50284, saving model to /content/models/experiment3_feature_extraction.h5\n","200/200 [==============================] - 25s 124ms/step - loss: 0.5606 - accuracy: 0.7145 - val_loss: 0.5028 - val_accuracy: 0.7650\n","Epoch 3/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5325 - accuracy: 0.7358\n","Epoch 00003: val_loss did not improve from 0.50284\n","200/200 [==============================] - 25s 122ms/step - loss: 0.5325 - accuracy: 0.7358 - val_loss: 0.5296 - val_accuracy: 0.7284\n","Epoch 4/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5298 - accuracy: 0.7403\n","Epoch 00004: val_loss improved from 0.50284 to 0.48687, saving model to /content/models/experiment3_feature_extraction.h5\n","200/200 [==============================] - 29s 145ms/step - loss: 0.5298 - accuracy: 0.7403 - val_loss: 0.4869 - val_accuracy: 0.7749\n","Epoch 5/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5254 - accuracy: 0.7433\n","Epoch 00005: val_loss did not improve from 0.48687\n","200/200 [==============================] - 25s 123ms/step - loss: 0.5254 - accuracy: 0.7433 - val_loss: 0.4931 - val_accuracy: 0.7697\n","Epoch 6/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5198 - accuracy: 0.7436\n","Epoch 00006: val_loss did not improve from 0.48687\n","200/200 [==============================] - 25s 123ms/step - loss: 0.5198 - accuracy: 0.7436 - val_loss: 0.5110 - val_accuracy: 0.7542\n","Epoch 7/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5087 - accuracy: 0.7506\n","Epoch 00007: val_loss did not improve from 0.48687\n","200/200 [==============================] - 25s 124ms/step - loss: 0.5087 - accuracy: 0.7506 - val_loss: 0.5014 - val_accuracy: 0.7598\n","Epoch 8/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5118 - accuracy: 0.7452\n","Epoch 00008: val_loss did not improve from 0.48687\n","200/200 [==============================] - 25s 123ms/step - loss: 0.5118 - accuracy: 0.7452 - val_loss: 0.4905 - val_accuracy: 0.7650\n","Epoch 9/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5163 - accuracy: 0.7447\n","Epoch 00009: val_loss improved from 0.48687 to 0.46365, saving model to /content/models/experiment3_feature_extraction.h5\n","200/200 [==============================] - 25s 125ms/step - loss: 0.5163 - accuracy: 0.7447 - val_loss: 0.4636 - val_accuracy: 0.7852\n","Epoch 10/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5161 - accuracy: 0.7431\n","Epoch 00010: val_loss improved from 0.46365 to 0.46112, saving model to /content/models/experiment3_feature_extraction.h5\n","200/200 [==============================] - 25s 125ms/step - loss: 0.5161 - accuracy: 0.7431 - val_loss: 0.4611 - val_accuracy: 0.7880\n","Epoch 11/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5134 - accuracy: 0.7505\n","Epoch 00011: val_loss did not improve from 0.46112\n","200/200 [==============================] - 25s 123ms/step - loss: 0.5134 - accuracy: 0.7505 - val_loss: 0.4704 - val_accuracy: 0.7838\n","Epoch 12/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5037 - accuracy: 0.7553\n","Epoch 00012: val_loss did not improve from 0.46112\n","200/200 [==============================] - 25s 123ms/step - loss: 0.5037 - accuracy: 0.7553 - val_loss: 0.4760 - val_accuracy: 0.7805\n","Epoch 13/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5069 - accuracy: 0.7523\n","Epoch 00013: val_loss improved from 0.46112 to 0.44884, saving model to /content/models/experiment3_feature_extraction.h5\n","200/200 [==============================] - 25s 125ms/step - loss: 0.5069 - accuracy: 0.7523 - val_loss: 0.4488 - val_accuracy: 0.7974\n","Epoch 14/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5068 - accuracy: 0.7505\n","Epoch 00014: val_loss did not improve from 0.44884\n","200/200 [==============================] - 25s 123ms/step - loss: 0.5068 - accuracy: 0.7505 - val_loss: 0.4938 - val_accuracy: 0.7627\n","Epoch 15/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5046 - accuracy: 0.7553\n","Epoch 00015: val_loss did not improve from 0.44884\n","200/200 [==============================] - 25s 123ms/step - loss: 0.5046 - accuracy: 0.7553 - val_loss: 0.4569 - val_accuracy: 0.7917\n","Epoch 16/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5021 - accuracy: 0.7533\n","Epoch 00016: val_loss did not improve from 0.44884\n","200/200 [==============================] - 25s 123ms/step - loss: 0.5021 - accuracy: 0.7533 - val_loss: 0.4668 - val_accuracy: 0.7861\n","Epoch 17/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5023 - accuracy: 0.7548\n","Epoch 00017: val_loss did not improve from 0.44884\n","200/200 [==============================] - 25s 123ms/step - loss: 0.5023 - accuracy: 0.7548 - val_loss: 0.4833 - val_accuracy: 0.7702\n","Epoch 18/50\n","200/200 [==============================] - ETA: 0s - loss: 0.4996 - accuracy: 0.7558\n","Epoch 00018: val_loss did not improve from 0.44884\n","200/200 [==============================] - 25s 123ms/step - loss: 0.4996 - accuracy: 0.7558 - val_loss: 0.4546 - val_accuracy: 0.7922\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["evaluate_model(tf.keras.models.load_model(GLOBAL_MODELS_FOLDER + '/experiment3_feature_extraction.h5'))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":730},"id":"eHRObPfJZ6v-","executionInfo":{"status":"ok","timestamp":1641925837690,"user_tz":-60,"elapsed":13524,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"f1828a4f-674b-4f94-9b4b-8dc683d6e9c7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Classification report: \n","              precision    recall  f1-score   support\n","\n","           0     0.6902    0.6753    0.6826       696\n","           1     0.8444    0.8532    0.8487      1437\n","\n","    accuracy                         0.7951      2133\n","   macro avg     0.7673    0.7642    0.7657      2133\n","weighted avg     0.7940    0.7951    0.7945      2133\n","\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["### Experiment 4"],"metadata":{"id":"m339vakHgaSZ"}},{"cell_type":"code","source":["inputs = keras.Input(shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3))\n","x = tf.keras.applications.mobilenet.preprocess_input(inputs)\n","x = conv_base(x)\n","x = layers.GlobalAveragePooling2D()(x)\n","x = layers.Dense(512, activation='relu')(x)\n","x = layers.Dense(512, activation='relu')(x)\n","outputs = layers.Dense(1, activation='sigmoid')(x)\n","model4 = keras.Model(inputs=inputs, outputs=outputs)\n","compile_model(model4, optimizer='rmsprop')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QXrFLiJ9gcqB","executionInfo":{"status":"ok","timestamp":1641894248466,"user_tz":-60,"elapsed":809,"user":{"displayName":"DANIELE CIOFFO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10062146559806518037"}},"outputId":"dc17511f-f7d2-4cad-8953-1e6ad2353be3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input_2 (InputLayer)        [(None, 224, 224, 3)]     0         \n","                                                                 \n"," tf.math.truediv (TFOpLambda  (None, 224, 224, 3)      0         \n"," )                                                               \n","                                                                 \n"," tf.math.subtract (TFOpLambd  (None, 224, 224, 3)      0         \n"," a)                                                              \n","                                                                 \n"," mobilenetv2_1.00_224 (Funct  (None, 7, 7, 1280)       2257984   \n"," ional)                                                          \n","                                                                 \n"," global_average_pooling2d (G  (None, 1280)             0         \n"," lobalAveragePooling2D)                                          \n","                                                                 \n"," dense (Dense)               (None, 512)               655872    \n","                                                                 \n"," dense_1 (Dense)             (None, 512)               262656    \n","                                                                 \n"," dense_2 (Dense)             (None, 1)                 513       \n","                                                                 \n","=================================================================\n","Total params: 3,177,025\n","Trainable params: 919,041\n","Non-trainable params: 2,257,984\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["run_model(model4, \"Experiment4_feature_extraction\", epochs=50)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"Ka3WzZGChhI4","executionInfo":{"status":"ok","timestamp":1641894715586,"user_tz":-60,"elapsed":430595,"user":{"displayName":"DANIELE CIOFFO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10062146559806518037"}},"outputId":"796ef03b-4c13-4054-c46a-21292ba0333e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/50\n","200/200 [==============================] - ETA: 0s - loss: 0.6229 - accuracy: 0.7076\n","Epoch 00001: val_loss improved from inf to 0.49482, saving model to /content/models/Experiment4_feature_extraction.h5\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n","  layer_config = serialize_layer_fn(layer)\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r200/200 [==============================] - 40s 138ms/step - loss: 0.6229 - accuracy: 0.7076 - val_loss: 0.4948 - val_accuracy: 0.7805\n","Epoch 2/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5071 - accuracy: 0.7541\n","Epoch 00002: val_loss did not improve from 0.49482\n","200/200 [==============================] - 25s 125ms/step - loss: 0.5071 - accuracy: 0.7541 - val_loss: 0.5201 - val_accuracy: 0.7720\n","Epoch 3/50\n","200/200 [==============================] - ETA: 0s - loss: 0.4843 - accuracy: 0.7652\n","Epoch 00003: val_loss improved from 0.49482 to 0.42817, saving model to /content/models/Experiment4_feature_extraction.h5\n","200/200 [==============================] - 26s 127ms/step - loss: 0.4843 - accuracy: 0.7652 - val_loss: 0.4282 - val_accuracy: 0.8119\n","Epoch 4/50\n","200/200 [==============================] - ETA: 0s - loss: 0.4578 - accuracy: 0.7849\n","Epoch 00004: val_loss did not improve from 0.42817\n","200/200 [==============================] - 26s 126ms/step - loss: 0.4578 - accuracy: 0.7849 - val_loss: 0.4405 - val_accuracy: 0.8007\n","Epoch 5/50\n","200/200 [==============================] - ETA: 0s - loss: 0.4446 - accuracy: 0.7919\n","Epoch 00005: val_loss improved from 0.42817 to 0.42122, saving model to /content/models/Experiment4_feature_extraction.h5\n","200/200 [==============================] - 26s 127ms/step - loss: 0.4446 - accuracy: 0.7919 - val_loss: 0.4212 - val_accuracy: 0.8166\n","Epoch 6/50\n","200/200 [==============================] - ETA: 0s - loss: 0.4291 - accuracy: 0.8022\n","Epoch 00006: val_loss did not improve from 0.42122\n","200/200 [==============================] - 25s 126ms/step - loss: 0.4291 - accuracy: 0.8022 - val_loss: 0.4387 - val_accuracy: 0.8082\n","Epoch 7/50\n","200/200 [==============================] - ETA: 0s - loss: 0.4117 - accuracy: 0.8069\n","Epoch 00007: val_loss improved from 0.42122 to 0.41254, saving model to /content/models/Experiment4_feature_extraction.h5\n","200/200 [==============================] - 26s 127ms/step - loss: 0.4117 - accuracy: 0.8069 - val_loss: 0.4125 - val_accuracy: 0.8330\n","Epoch 8/50\n","200/200 [==============================] - ETA: 0s - loss: 0.4027 - accuracy: 0.8119\n","Epoch 00008: val_loss did not improve from 0.41254\n","200/200 [==============================] - 25s 125ms/step - loss: 0.4027 - accuracy: 0.8119 - val_loss: 0.4172 - val_accuracy: 0.8335\n","Epoch 9/50\n","200/200 [==============================] - ETA: 0s - loss: 0.3850 - accuracy: 0.8258\n","Epoch 00009: val_loss did not improve from 0.41254\n","200/200 [==============================] - 25s 126ms/step - loss: 0.3850 - accuracy: 0.8258 - val_loss: 0.5451 - val_accuracy: 0.8279\n","Epoch 10/50\n","200/200 [==============================] - ETA: 0s - loss: 0.3699 - accuracy: 0.8358\n","Epoch 00010: val_loss did not improve from 0.41254\n","200/200 [==============================] - 25s 125ms/step - loss: 0.3699 - accuracy: 0.8358 - val_loss: 0.4260 - val_accuracy: 0.8293\n","Epoch 11/50\n","200/200 [==============================] - ETA: 0s - loss: 0.3519 - accuracy: 0.8410\n","Epoch 00011: val_loss did not improve from 0.41254\n","200/200 [==============================] - 25s 125ms/step - loss: 0.3519 - accuracy: 0.8410 - val_loss: 0.4506 - val_accuracy: 0.8368\n","Epoch 12/50\n","200/200 [==============================] - ETA: 0s - loss: 0.3381 - accuracy: 0.8501\n","Epoch 00012: val_loss did not improve from 0.41254\n","200/200 [==============================] - 25s 124ms/step - loss: 0.3381 - accuracy: 0.8501 - val_loss: 0.4626 - val_accuracy: 0.8208\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["evaluate_model(tf.keras.models.load_model(GLOBAL_MODELS_FOLDER + '/Experiment4_feature_extraction.h5'))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":730},"id":"akT3ONLXJu2Q","executionInfo":{"status":"ok","timestamp":1642022310371,"user_tz":-60,"elapsed":18025,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"6780366c-7e01-4470-9b45-7a9c60f8cbfd"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["Classification report: \n","              precision    recall  f1-score   support\n","\n","           0     0.7858    0.6537    0.7137       696\n","           1     0.8449    0.9137    0.8780      1437\n","\n","    accuracy                         0.8289      2133\n","   macro avg     0.8154    0.7837    0.7958      2133\n","weighted avg     0.8256    0.8289    0.8244      2133\n","\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["## Fine tuning"],"metadata":{"id":"5ne7nBKrtD3O"}},{"cell_type":"markdown","source":["### Experiment 5"],"metadata":{"id":"UxIuUuLvgydG"}},{"cell_type":"code","source":["fine_tuning = models.load_model(os.path.join(GLOBAL_MODELS_FOLDER, 'experiment3_feature_extraction.h5'))\n","\n","setTrainable = False\n","for layer in fine_tuning.get_layer('mobilenetv2_1.00_224').layers:\n","    if layer.name in ('block_16_expand'):\n","        setTrainable = True\n","    if setTrainable == True:\n","        layer.trainable = True\n","    else:\n","      layer.trainable = False\n","\n","compile_model(fine_tuning, learning_rate=1e-5)\n","\n","for i, layer in enumerate(fine_tuning.get_layer('mobilenetv2_1.00_224').layers):\n","    print(i, layer.name, layer.trainable)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1641810203171,"user_tz":-60,"elapsed":2287,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"1813f23f-4ab8-4858-eb7c-e926f9f9ba95","id":"aWw8In3FgydM"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model_12\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input_17 (InputLayer)       [(None, 224, 224, 3)]     0         \n","                                                                 \n"," tf.math.truediv_12 (TFOpLam  (None, 224, 224, 3)      0         \n"," bda)                                                            \n","                                                                 \n"," tf.math.subtract_12 (TFOpLa  (None, 224, 224, 3)      0         \n"," mbda)                                                           \n","                                                                 \n"," mobilenetv2_1.00_224 (Funct  (None, 7, 7, 1280)       2257984   \n"," ional)                                                          \n","                                                                 \n"," global_average_pooling2d_3   (None, 1280)             0         \n"," (GlobalAveragePooling2D)                                        \n","                                                                 \n"," dropout_5 (Dropout)         (None, 1280)              0         \n","                                                                 \n"," dense_16 (Dense)            (None, 1)                 1281      \n","                                                                 \n","=================================================================\n","Total params: 2,259,265\n","Trainable params: 887,361\n","Non-trainable params: 1,371,904\n","_________________________________________________________________\n","0 input_9 False\n","1 Conv1 False\n","2 bn_Conv1 False\n","3 Conv1_relu False\n","4 expanded_conv_depthwise False\n","5 expanded_conv_depthwise_BN False\n","6 expanded_conv_depthwise_relu False\n","7 expanded_conv_project False\n","8 expanded_conv_project_BN False\n","9 block_1_expand False\n","10 block_1_expand_BN False\n","11 block_1_expand_relu False\n","12 block_1_pad False\n","13 block_1_depthwise False\n","14 block_1_depthwise_BN False\n","15 block_1_depthwise_relu False\n","16 block_1_project False\n","17 block_1_project_BN False\n","18 block_2_expand False\n","19 block_2_expand_BN False\n","20 block_2_expand_relu False\n","21 block_2_depthwise False\n","22 block_2_depthwise_BN False\n","23 block_2_depthwise_relu False\n","24 block_2_project False\n","25 block_2_project_BN False\n","26 block_2_add False\n","27 block_3_expand False\n","28 block_3_expand_BN False\n","29 block_3_expand_relu False\n","30 block_3_pad False\n","31 block_3_depthwise False\n","32 block_3_depthwise_BN False\n","33 block_3_depthwise_relu False\n","34 block_3_project False\n","35 block_3_project_BN False\n","36 block_4_expand False\n","37 block_4_expand_BN False\n","38 block_4_expand_relu False\n","39 block_4_depthwise False\n","40 block_4_depthwise_BN False\n","41 block_4_depthwise_relu False\n","42 block_4_project False\n","43 block_4_project_BN False\n","44 block_4_add False\n","45 block_5_expand False\n","46 block_5_expand_BN False\n","47 block_5_expand_relu False\n","48 block_5_depthwise False\n","49 block_5_depthwise_BN False\n","50 block_5_depthwise_relu False\n","51 block_5_project False\n","52 block_5_project_BN False\n","53 block_5_add False\n","54 block_6_expand False\n","55 block_6_expand_BN False\n","56 block_6_expand_relu False\n","57 block_6_pad False\n","58 block_6_depthwise False\n","59 block_6_depthwise_BN False\n","60 block_6_depthwise_relu False\n","61 block_6_project False\n","62 block_6_project_BN False\n","63 block_7_expand False\n","64 block_7_expand_BN False\n","65 block_7_expand_relu False\n","66 block_7_depthwise False\n","67 block_7_depthwise_BN False\n","68 block_7_depthwise_relu False\n","69 block_7_project False\n","70 block_7_project_BN False\n","71 block_7_add False\n","72 block_8_expand False\n","73 block_8_expand_BN False\n","74 block_8_expand_relu False\n","75 block_8_depthwise False\n","76 block_8_depthwise_BN False\n","77 block_8_depthwise_relu False\n","78 block_8_project False\n","79 block_8_project_BN False\n","80 block_8_add False\n","81 block_9_expand False\n","82 block_9_expand_BN False\n","83 block_9_expand_relu False\n","84 block_9_depthwise False\n","85 block_9_depthwise_BN False\n","86 block_9_depthwise_relu False\n","87 block_9_project False\n","88 block_9_project_BN False\n","89 block_9_add False\n","90 block_10_expand False\n","91 block_10_expand_BN False\n","92 block_10_expand_relu False\n","93 block_10_depthwise False\n","94 block_10_depthwise_BN False\n","95 block_10_depthwise_relu False\n","96 block_10_project False\n","97 block_10_project_BN False\n","98 block_11_expand False\n","99 block_11_expand_BN False\n","100 block_11_expand_relu False\n","101 block_11_depthwise False\n","102 block_11_depthwise_BN False\n","103 block_11_depthwise_relu False\n","104 block_11_project False\n","105 block_11_project_BN False\n","106 block_11_add False\n","107 block_12_expand False\n","108 block_12_expand_BN False\n","109 block_12_expand_relu False\n","110 block_12_depthwise False\n","111 block_12_depthwise_BN False\n","112 block_12_depthwise_relu False\n","113 block_12_project False\n","114 block_12_project_BN False\n","115 block_12_add False\n","116 block_13_expand False\n","117 block_13_expand_BN False\n","118 block_13_expand_relu False\n","119 block_13_pad False\n","120 block_13_depthwise False\n","121 block_13_depthwise_BN False\n","122 block_13_depthwise_relu False\n","123 block_13_project False\n","124 block_13_project_BN False\n","125 block_14_expand False\n","126 block_14_expand_BN False\n","127 block_14_expand_relu False\n","128 block_14_depthwise False\n","129 block_14_depthwise_BN False\n","130 block_14_depthwise_relu False\n","131 block_14_project False\n","132 block_14_project_BN False\n","133 block_14_add False\n","134 block_15_expand False\n","135 block_15_expand_BN False\n","136 block_15_expand_relu False\n","137 block_15_depthwise False\n","138 block_15_depthwise_BN False\n","139 block_15_depthwise_relu False\n","140 block_15_project False\n","141 block_15_project_BN False\n","142 block_15_add False\n","143 block_16_expand True\n","144 block_16_expand_BN True\n","145 block_16_expand_relu True\n","146 block_16_depthwise True\n","147 block_16_depthwise_BN True\n","148 block_16_depthwise_relu True\n","149 block_16_project True\n","150 block_16_project_BN True\n","151 Conv_1 True\n","152 Conv_1_bn True\n","153 out_relu True\n"]}]},{"cell_type":"code","source":["run_model(fine_tuning, 'experiment5_finetuning', epochs = 100, patience=5)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"e3ddd6c3-48c7-4b90-da38-d5027b28506a","executionInfo":{"status":"ok","timestamp":1641811299742,"user_tz":-60,"elapsed":1090411,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"id":"8q5kMie1gydN"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","200/200 [==============================] - ETA: 0s - loss: 0.6813 - accuracy: 0.6873\n","Epoch 00001: val_loss improved from inf to 0.49195, saving model to /content/models/fine_tuning_4.h5\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n","  layer_config = serialize_layer_fn(layer)\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r200/200 [==============================] - 31s 136ms/step - loss: 0.6813 - accuracy: 0.6873 - val_loss: 0.4920 - val_accuracy: 0.7641\n","Epoch 2/100\n","200/200 [==============================] - ETA: 0s - loss: 0.5827 - accuracy: 0.7362\n","Epoch 00002: val_loss improved from 0.49195 to 0.48780, saving model to /content/models/fine_tuning_4.h5\n","200/200 [==============================] - 26s 129ms/step - loss: 0.5827 - accuracy: 0.7362 - val_loss: 0.4878 - val_accuracy: 0.7716\n","Epoch 3/100\n","200/200 [==============================] - ETA: 0s - loss: 0.5488 - accuracy: 0.7341\n","Epoch 00003: val_loss did not improve from 0.48780\n","200/200 [==============================] - 26s 129ms/step - loss: 0.5488 - accuracy: 0.7341 - val_loss: 0.5014 - val_accuracy: 0.7669\n","Epoch 4/100\n","200/200 [==============================] - ETA: 0s - loss: 0.5357 - accuracy: 0.7453\n","Epoch 00004: val_loss improved from 0.48780 to 0.48232, saving model to /content/models/fine_tuning_4.h5\n","200/200 [==============================] - 26s 131ms/step - loss: 0.5357 - accuracy: 0.7453 - val_loss: 0.4823 - val_accuracy: 0.7749\n","Epoch 5/100\n","200/200 [==============================] - ETA: 0s - loss: 0.5028 - accuracy: 0.7636\n","Epoch 00005: val_loss did not improve from 0.48232\n","200/200 [==============================] - 26s 129ms/step - loss: 0.5028 - accuracy: 0.7636 - val_loss: 0.5063 - val_accuracy: 0.7598\n","Epoch 6/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4868 - accuracy: 0.7636\n","Epoch 00006: val_loss did not improve from 0.48232\n","200/200 [==============================] - 26s 129ms/step - loss: 0.4868 - accuracy: 0.7636 - val_loss: 0.4907 - val_accuracy: 0.7688\n","Epoch 7/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4695 - accuracy: 0.7760\n","Epoch 00007: val_loss improved from 0.48232 to 0.47745, saving model to /content/models/fine_tuning_4.h5\n","200/200 [==============================] - 26s 129ms/step - loss: 0.4695 - accuracy: 0.7760 - val_loss: 0.4775 - val_accuracy: 0.7739\n","Epoch 8/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4601 - accuracy: 0.7785\n","Epoch 00008: val_loss did not improve from 0.47745\n","200/200 [==============================] - 26s 129ms/step - loss: 0.4601 - accuracy: 0.7785 - val_loss: 0.4914 - val_accuracy: 0.7674\n","Epoch 9/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4443 - accuracy: 0.7933\n","Epoch 00009: val_loss improved from 0.47745 to 0.47465, saving model to /content/models/fine_tuning_4.h5\n","200/200 [==============================] - 26s 130ms/step - loss: 0.4443 - accuracy: 0.7933 - val_loss: 0.4746 - val_accuracy: 0.7749\n","Epoch 10/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4384 - accuracy: 0.7913\n","Epoch 00010: val_loss did not improve from 0.47465\n","200/200 [==============================] - 26s 128ms/step - loss: 0.4384 - accuracy: 0.7913 - val_loss: 0.4807 - val_accuracy: 0.7744\n","Epoch 11/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4249 - accuracy: 0.7997\n","Epoch 00011: val_loss improved from 0.47465 to 0.45627, saving model to /content/models/fine_tuning_4.h5\n","200/200 [==============================] - 26s 130ms/step - loss: 0.4249 - accuracy: 0.7997 - val_loss: 0.4563 - val_accuracy: 0.7889\n","Epoch 12/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4179 - accuracy: 0.8053\n","Epoch 00012: val_loss improved from 0.45627 to 0.42261, saving model to /content/models/fine_tuning_4.h5\n","200/200 [==============================] - 26s 130ms/step - loss: 0.4179 - accuracy: 0.8053 - val_loss: 0.4226 - val_accuracy: 0.8044\n","Epoch 13/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4055 - accuracy: 0.8155\n","Epoch 00013: val_loss did not improve from 0.42261\n","200/200 [==============================] - 26s 128ms/step - loss: 0.4055 - accuracy: 0.8155 - val_loss: 0.4341 - val_accuracy: 0.7922\n","Epoch 14/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4007 - accuracy: 0.8186\n","Epoch 00014: val_loss did not improve from 0.42261\n","200/200 [==============================] - 26s 129ms/step - loss: 0.4007 - accuracy: 0.8186 - val_loss: 0.4458 - val_accuracy: 0.7932\n","Epoch 15/100\n","200/200 [==============================] - ETA: 0s - loss: 0.3921 - accuracy: 0.8163\n","Epoch 00015: val_loss improved from 0.42261 to 0.41763, saving model to /content/models/fine_tuning_4.h5\n","200/200 [==============================] - 26s 130ms/step - loss: 0.3921 - accuracy: 0.8163 - val_loss: 0.4176 - val_accuracy: 0.8068\n","Epoch 16/100\n","200/200 [==============================] - ETA: 0s - loss: 0.3777 - accuracy: 0.8271\n","Epoch 00016: val_loss improved from 0.41763 to 0.41537, saving model to /content/models/fine_tuning_4.h5\n","200/200 [==============================] - 26s 130ms/step - loss: 0.3777 - accuracy: 0.8271 - val_loss: 0.4154 - val_accuracy: 0.8063\n","Epoch 17/100\n","200/200 [==============================] - ETA: 0s - loss: 0.3728 - accuracy: 0.8322\n","Epoch 00017: val_loss did not improve from 0.41537\n","200/200 [==============================] - 26s 129ms/step - loss: 0.3728 - accuracy: 0.8322 - val_loss: 0.4241 - val_accuracy: 0.8016\n","Epoch 18/100\n","200/200 [==============================] - ETA: 0s - loss: 0.3668 - accuracy: 0.8308\n","Epoch 00018: val_loss did not improve from 0.41537\n","200/200 [==============================] - 26s 129ms/step - loss: 0.3668 - accuracy: 0.8308 - val_loss: 0.4190 - val_accuracy: 0.8068\n","Epoch 19/100\n","200/200 [==============================] - ETA: 0s - loss: 0.3638 - accuracy: 0.8338\n","Epoch 00019: val_loss improved from 0.41537 to 0.39874, saving model to /content/models/fine_tuning_4.h5\n","200/200 [==============================] - 26s 130ms/step - loss: 0.3638 - accuracy: 0.8338 - val_loss: 0.3987 - val_accuracy: 0.8175\n","Epoch 20/100\n","200/200 [==============================] - ETA: 0s - loss: 0.3542 - accuracy: 0.8379\n","Epoch 00020: val_loss improved from 0.39874 to 0.39524, saving model to /content/models/fine_tuning_4.h5\n","200/200 [==============================] - 26s 131ms/step - loss: 0.3542 - accuracy: 0.8379 - val_loss: 0.3952 - val_accuracy: 0.8227\n","Epoch 21/100\n","200/200 [==============================] - ETA: 0s - loss: 0.3505 - accuracy: 0.8451\n","Epoch 00021: val_loss improved from 0.39524 to 0.38660, saving model to /content/models/fine_tuning_4.h5\n","200/200 [==============================] - 26s 130ms/step - loss: 0.3505 - accuracy: 0.8451 - val_loss: 0.3866 - val_accuracy: 0.8241\n","Epoch 22/100\n","200/200 [==============================] - ETA: 0s - loss: 0.3393 - accuracy: 0.8463\n","Epoch 00022: val_loss did not improve from 0.38660\n","200/200 [==============================] - 26s 128ms/step - loss: 0.3393 - accuracy: 0.8463 - val_loss: 0.3943 - val_accuracy: 0.8194\n","Epoch 23/100\n","200/200 [==============================] - ETA: 0s - loss: 0.3349 - accuracy: 0.8510\n","Epoch 00023: val_loss improved from 0.38660 to 0.37375, saving model to /content/models/fine_tuning_4.h5\n","200/200 [==============================] - 26s 130ms/step - loss: 0.3349 - accuracy: 0.8510 - val_loss: 0.3738 - val_accuracy: 0.8340\n","Epoch 24/100\n","200/200 [==============================] - ETA: 0s - loss: 0.3332 - accuracy: 0.8502\n","Epoch 00024: val_loss did not improve from 0.37375\n","200/200 [==============================] - 26s 129ms/step - loss: 0.3332 - accuracy: 0.8502 - val_loss: 0.3798 - val_accuracy: 0.8326\n","Epoch 25/100\n","200/200 [==============================] - ETA: 0s - loss: 0.3117 - accuracy: 0.8663\n","Epoch 00025: val_loss improved from 0.37375 to 0.36343, saving model to /content/models/fine_tuning_4.h5\n","200/200 [==============================] - 27s 133ms/step - loss: 0.3117 - accuracy: 0.8663 - val_loss: 0.3634 - val_accuracy: 0.8429\n","Epoch 26/100\n","200/200 [==============================] - ETA: 0s - loss: 0.3204 - accuracy: 0.8612\n","Epoch 00026: val_loss did not improve from 0.36343\n","200/200 [==============================] - 26s 129ms/step - loss: 0.3204 - accuracy: 0.8612 - val_loss: 0.3692 - val_accuracy: 0.8391\n","Epoch 27/100\n","200/200 [==============================] - ETA: 0s - loss: 0.3074 - accuracy: 0.8676\n","Epoch 00027: val_loss improved from 0.36343 to 0.36089, saving model to /content/models/fine_tuning_4.h5\n","200/200 [==============================] - 26s 130ms/step - loss: 0.3074 - accuracy: 0.8676 - val_loss: 0.3609 - val_accuracy: 0.8447\n","Epoch 28/100\n","200/200 [==============================] - ETA: 0s - loss: 0.3029 - accuracy: 0.8690\n","Epoch 00028: val_loss improved from 0.36089 to 0.35982, saving model to /content/models/fine_tuning_4.h5\n","200/200 [==============================] - 26s 131ms/step - loss: 0.3029 - accuracy: 0.8690 - val_loss: 0.3598 - val_accuracy: 0.8424\n","Epoch 29/100\n","200/200 [==============================] - ETA: 0s - loss: 0.3001 - accuracy: 0.8713\n","Epoch 00029: val_loss did not improve from 0.35982\n","200/200 [==============================] - 26s 130ms/step - loss: 0.3001 - accuracy: 0.8713 - val_loss: 0.3815 - val_accuracy: 0.8340\n","Epoch 30/100\n","200/200 [==============================] - ETA: 0s - loss: 0.2986 - accuracy: 0.8757\n","Epoch 00030: val_loss did not improve from 0.35982\n","200/200 [==============================] - 26s 131ms/step - loss: 0.2986 - accuracy: 0.8757 - val_loss: 0.3633 - val_accuracy: 0.8405\n","Epoch 31/100\n","200/200 [==============================] - ETA: 0s - loss: 0.2809 - accuracy: 0.8813\n","Epoch 00031: val_loss did not improve from 0.35982\n","200/200 [==============================] - 26s 130ms/step - loss: 0.2809 - accuracy: 0.8813 - val_loss: 0.3600 - val_accuracy: 0.8452\n","Epoch 32/100\n","200/200 [==============================] - ETA: 0s - loss: 0.2817 - accuracy: 0.8806\n","Epoch 00032: val_loss did not improve from 0.35982\n","200/200 [==============================] - 26s 129ms/step - loss: 0.2817 - accuracy: 0.8806 - val_loss: 0.3616 - val_accuracy: 0.8452\n","Epoch 33/100\n","200/200 [==============================] - ETA: 0s - loss: 0.2724 - accuracy: 0.8848\n","Epoch 00033: val_loss did not improve from 0.35982\n","200/200 [==============================] - 26s 130ms/step - loss: 0.2724 - accuracy: 0.8848 - val_loss: 0.3685 - val_accuracy: 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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["evaluate_model(tf.keras.models.load_model(GLOBAL_MODELS_FOLDER + '/experiment5_finetuning.h5'))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":730},"id":"W4RpLJxLnm_r","executionInfo":{"status":"ok","timestamp":1641811988653,"user_tz":-60,"elapsed":11410,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"dabac846-fbd7-4199-af27-22d3060626e9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Classification report: \n","              precision    recall  f1-score   support\n","\n","           0     0.7579    0.7601    0.7590       696\n","           1     0.8836    0.8824    0.8830      1437\n","\n","    accuracy                         0.8425      2133\n","   macro avg     0.8208    0.8212    0.8210      2133\n","weighted avg     0.8426    0.8425    0.8425      2133\n","\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["### Experiment 6"],"metadata":{"id":"PHd09dzI2vzP"}},{"cell_type":"code","source":["fine_tuning = models.load_model(os.path.join(GLOBAL_MODELS_FOLDER, 'experiment2_feature_extraction.h5'))\n","\n","setTrainable = False\n","for layer in fine_tuning.get_layer('mobilenetv2_1.00_224').layers:\n","    if layer.name in ('block_10_expand'):\n","        setTrainable = True\n","    if setTrainable == True:\n","        layer.trainable = True\n","    else:\n","      layer.trainable = False\n","\n","compile_model(fine_tuning, learning_rate=1e-5)\n","\n","for i, layer in enumerate(fine_tuning.get_layer('mobilenetv2_1.00_224').layers):\n","    print(i, layer.name, layer.trainable)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YBbGtnGFtF51","executionInfo":{"status":"ok","timestamp":1641394425680,"user_tz":-60,"elapsed":2649,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"80d3dff1-bb59-4687-c62c-0a3e892b98ac"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model_10\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input_15 (InputLayer)       [(None, 224, 224, 3)]     0         \n","                                                                 \n"," tf.math.truediv_10 (TFOpLam  (None, 224, 224, 3)      0         \n"," bda)                                                            \n","                                                                 \n"," tf.math.subtract_10 (TFOpLa  (None, 224, 224, 3)      0         \n"," mbda)                                                           \n","                                                                 \n"," mobilenetv2_1.00_224 (Funct  (None, 7, 7, 1280)       2257984   \n"," ional)                                                          \n","                                                                 \n"," global_average_pooling2d_1   (None, 1280)             0         \n"," (GlobalAveragePooling2D)                                        \n","                                                                 \n"," dense_13 (Dense)            (None, 256)               327936    \n","                                                                 \n"," dropout_3 (Dropout)         (None, 256)               0         \n","                                                                 \n"," dense_14 (Dense)            (None, 1)                 257       \n","                                                                 \n","=================================================================\n","Total params: 2,586,177\n","Trainable params: 2,312,705\n","Non-trainable params: 273,472\n","_________________________________________________________________\n","0 input_9 False\n","1 Conv1 False\n","2 bn_Conv1 False\n","3 Conv1_relu False\n","4 expanded_conv_depthwise False\n","5 expanded_conv_depthwise_BN False\n","6 expanded_conv_depthwise_relu False\n","7 expanded_conv_project False\n","8 expanded_conv_project_BN False\n","9 block_1_expand False\n","10 block_1_expand_BN False\n","11 block_1_expand_relu False\n","12 block_1_pad False\n","13 block_1_depthwise False\n","14 block_1_depthwise_BN False\n","15 block_1_depthwise_relu False\n","16 block_1_project False\n","17 block_1_project_BN False\n","18 block_2_expand False\n","19 block_2_expand_BN False\n","20 block_2_expand_relu False\n","21 block_2_depthwise False\n","22 block_2_depthwise_BN False\n","23 block_2_depthwise_relu False\n","24 block_2_project False\n","25 block_2_project_BN False\n","26 block_2_add False\n","27 block_3_expand False\n","28 block_3_expand_BN False\n","29 block_3_expand_relu False\n","30 block_3_pad False\n","31 block_3_depthwise False\n","32 block_3_depthwise_BN False\n","33 block_3_depthwise_relu False\n","34 block_3_project False\n","35 block_3_project_BN False\n","36 block_4_expand False\n","37 block_4_expand_BN False\n","38 block_4_expand_relu False\n","39 block_4_depthwise False\n","40 block_4_depthwise_BN False\n","41 block_4_depthwise_relu False\n","42 block_4_project False\n","43 block_4_project_BN False\n","44 block_4_add False\n","45 block_5_expand False\n","46 block_5_expand_BN False\n","47 block_5_expand_relu False\n","48 block_5_depthwise False\n","49 block_5_depthwise_BN False\n","50 block_5_depthwise_relu False\n","51 block_5_project False\n","52 block_5_project_BN False\n","53 block_5_add False\n","54 block_6_expand False\n","55 block_6_expand_BN False\n","56 block_6_expand_relu False\n","57 block_6_pad False\n","58 block_6_depthwise False\n","59 block_6_depthwise_BN False\n","60 block_6_depthwise_relu False\n","61 block_6_project False\n","62 block_6_project_BN False\n","63 block_7_expand False\n","64 block_7_expand_BN False\n","65 block_7_expand_relu False\n","66 block_7_depthwise False\n","67 block_7_depthwise_BN False\n","68 block_7_depthwise_relu False\n","69 block_7_project False\n","70 block_7_project_BN False\n","71 block_7_add False\n","72 block_8_expand False\n","73 block_8_expand_BN False\n","74 block_8_expand_relu False\n","75 block_8_depthwise False\n","76 block_8_depthwise_BN False\n","77 block_8_depthwise_relu False\n","78 block_8_project False\n","79 block_8_project_BN False\n","80 block_8_add False\n","81 block_9_expand False\n","82 block_9_expand_BN False\n","83 block_9_expand_relu False\n","84 block_9_depthwise False\n","85 block_9_depthwise_BN False\n","86 block_9_depthwise_relu False\n","87 block_9_project False\n","88 block_9_project_BN False\n","89 block_9_add False\n","90 block_10_expand True\n","91 block_10_expand_BN True\n","92 block_10_expand_relu True\n","93 block_10_depthwise True\n","94 block_10_depthwise_BN True\n","95 block_10_depthwise_relu True\n","96 block_10_project True\n","97 block_10_project_BN True\n","98 block_11_expand True\n","99 block_11_expand_BN True\n","100 block_11_expand_relu True\n","101 block_11_depthwise True\n","102 block_11_depthwise_BN True\n","103 block_11_depthwise_relu True\n","104 block_11_project True\n","105 block_11_project_BN True\n","106 block_11_add True\n","107 block_12_expand True\n","108 block_12_expand_BN True\n","109 block_12_expand_relu True\n","110 block_12_depthwise True\n","111 block_12_depthwise_BN True\n","112 block_12_depthwise_relu True\n","113 block_12_project True\n","114 block_12_project_BN True\n","115 block_12_add True\n","116 block_13_expand True\n","117 block_13_expand_BN True\n","118 block_13_expand_relu True\n","119 block_13_pad True\n","120 block_13_depthwise True\n","121 block_13_depthwise_BN True\n","122 block_13_depthwise_relu True\n","123 block_13_project True\n","124 block_13_project_BN True\n","125 block_14_expand True\n","126 block_14_expand_BN True\n","127 block_14_expand_relu True\n","128 block_14_depthwise True\n","129 block_14_depthwise_BN True\n","130 block_14_depthwise_relu True\n","131 block_14_project True\n","132 block_14_project_BN True\n","133 block_14_add True\n","134 block_15_expand True\n","135 block_15_expand_BN True\n","136 block_15_expand_relu True\n","137 block_15_depthwise True\n","138 block_15_depthwise_BN True\n","139 block_15_depthwise_relu True\n","140 block_15_project True\n","141 block_15_project_BN True\n","142 block_15_add True\n","143 block_16_expand True\n","144 block_16_expand_BN True\n","145 block_16_expand_relu True\n","146 block_16_depthwise True\n","147 block_16_depthwise_BN True\n","148 block_16_depthwise_relu True\n","149 block_16_project True\n","150 block_16_project_BN True\n","151 Conv_1 True\n","152 Conv_1_bn True\n","153 out_relu True\n"]}]},{"cell_type":"code","source":["run_model(fine_tuning, 'experiment6_finetuning', epochs = 100, patience=5)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"wZ8YcYctu6y3","executionInfo":{"status":"ok","timestamp":1641395235182,"user_tz":-60,"elapsed":756562,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"6c2839fa-514b-45f3-9df3-2d067dad2d32"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","200/200 [==============================] - ETA: 0s - loss: 0.5711 - accuracy: 0.7383\n","Epoch 00001: val_loss improved from inf to 0.76478, saving model to /content/models/fine_tuning.h5\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n","  layer_config = serialize_layer_fn(layer)\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r200/200 [==============================] - 35s 174ms/step - loss: 0.5711 - accuracy: 0.7383 - val_loss: 0.7648 - val_accuracy: 0.6285\n","Epoch 2/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4293 - accuracy: 0.8022\n","Epoch 00002: val_loss improved from 0.76478 to 0.71810, saving model to /content/models/fine_tuning.h5\n","200/200 [==============================] - 34s 168ms/step - loss: 0.4293 - accuracy: 0.8022 - val_loss: 0.7181 - val_accuracy: 0.6520\n","Epoch 3/100\n","200/200 [==============================] - ETA: 0s - loss: 0.3439 - accuracy: 0.8452\n","Epoch 00003: val_loss improved from 0.71810 to 0.67859, saving model to /content/models/fine_tuning.h5\n","200/200 [==============================] - 34s 168ms/step - loss: 0.3439 - accuracy: 0.8452 - val_loss: 0.6786 - val_accuracy: 0.6759\n","Epoch 4/100\n","200/200 [==============================] - ETA: 0s - loss: 0.2818 - accuracy: 0.8816\n","Epoch 00004: val_loss did not improve from 0.67859\n","200/200 [==============================] - 33s 165ms/step - loss: 0.2818 - accuracy: 0.8816 - val_loss: 0.7457 - val_accuracy: 0.6581\n","Epoch 5/100\n","200/200 [==============================] - ETA: 0s - loss: 0.2411 - accuracy: 0.9013\n","Epoch 00005: val_loss improved from 0.67859 to 0.66168, saving model to /content/models/fine_tuning.h5\n","200/200 [==============================] - 34s 168ms/step - loss: 0.2411 - accuracy: 0.9013 - val_loss: 0.6617 - val_accuracy: 0.6928\n","Epoch 6/100\n","200/200 [==============================] - ETA: 0s - loss: 0.2052 - accuracy: 0.9126\n","Epoch 00006: val_loss improved from 0.66168 to 0.62838, saving model to /content/models/fine_tuning.h5\n","200/200 [==============================] - 34s 169ms/step - loss: 0.2052 - accuracy: 0.9126 - val_loss: 0.6284 - val_accuracy: 0.7120\n","Epoch 7/100\n","200/200 [==============================] - ETA: 0s - loss: 0.1659 - accuracy: 0.9425\n","Epoch 00007: val_loss improved from 0.62838 to 0.60161, saving model to /content/models/fine_tuning.h5\n","200/200 [==============================] - 34s 168ms/step - loss: 0.1659 - accuracy: 0.9425 - val_loss: 0.6016 - val_accuracy: 0.7303\n","Epoch 8/100\n","200/200 [==============================] - ETA: 0s - loss: 0.1346 - accuracy: 0.9575\n","Epoch 00008: val_loss improved from 0.60161 to 0.56459, saving model to /content/models/fine_tuning.h5\n","200/200 [==============================] - 34s 168ms/step - loss: 0.1346 - accuracy: 0.9575 - val_loss: 0.5646 - val_accuracy: 0.7547\n","Epoch 9/100\n","200/200 [==============================] - ETA: 0s - loss: 0.1191 - accuracy: 0.9608\n","Epoch 00009: val_loss improved from 0.56459 to 0.47716, saving model to /content/models/fine_tuning.h5\n","200/200 [==============================] - 34s 168ms/step - loss: 0.1191 - accuracy: 0.9608 - val_loss: 0.4772 - val_accuracy: 0.7941\n","Epoch 10/100\n","200/200 [==============================] - ETA: 0s - loss: 0.0907 - accuracy: 0.9773\n","Epoch 00010: val_loss improved from 0.47716 to 0.47667, saving model to /content/models/fine_tuning.h5\n","200/200 [==============================] - 34s 168ms/step - loss: 0.0907 - accuracy: 0.9773 - val_loss: 0.4767 - val_accuracy: 0.8007\n","Epoch 11/100\n","200/200 [==============================] - ETA: 0s - loss: 0.0822 - accuracy: 0.9794\n","Epoch 00011: val_loss improved from 0.47667 to 0.43409, saving model to /content/models/fine_tuning.h5\n","200/200 [==============================] - 34s 168ms/step - loss: 0.0822 - accuracy: 0.9794 - val_loss: 0.4341 - val_accuracy: 0.8227\n","Epoch 12/100\n","200/200 [==============================] - ETA: 0s - loss: 0.0683 - accuracy: 0.9845\n","Epoch 00012: val_loss improved from 0.43409 to 0.39728, saving model to /content/models/fine_tuning.h5\n","200/200 [==============================] - 34s 167ms/step - loss: 0.0683 - accuracy: 0.9845 - val_loss: 0.3973 - val_accuracy: 0.8447\n","Epoch 13/100\n","200/200 [==============================] - ETA: 0s - loss: 0.0626 - accuracy: 0.9837\n","Epoch 00013: val_loss did not improve from 0.39728\n","200/200 [==============================] - 33s 166ms/step - loss: 0.0626 - accuracy: 0.9837 - val_loss: 0.4109 - val_accuracy: 0.8415\n","Epoch 14/100\n","200/200 [==============================] - ETA: 0s - loss: 0.0486 - accuracy: 0.9900\n","Epoch 00014: val_loss improved from 0.39728 to 0.38919, saving model to /content/models/fine_tuning.h5\n","200/200 [==============================] - 34s 168ms/step - loss: 0.0486 - accuracy: 0.9900 - val_loss: 0.3892 - val_accuracy: 0.8518\n","Epoch 15/100\n","200/200 [==============================] - ETA: 0s - loss: 0.0408 - accuracy: 0.9922\n","Epoch 00015: val_loss did not improve from 0.38919\n","200/200 [==============================] - 33s 165ms/step - loss: 0.0408 - accuracy: 0.9922 - val_loss: 0.4056 - val_accuracy: 0.8494\n","Epoch 16/100\n","200/200 [==============================] - ETA: 0s - loss: 0.0324 - accuracy: 0.9953\n","Epoch 00016: val_loss did not improve from 0.38919\n","200/200 [==============================] - 33s 166ms/step - loss: 0.0324 - accuracy: 0.9953 - val_loss: 0.4000 - val_accuracy: 0.8598\n","Epoch 17/100\n","200/200 [==============================] - ETA: 0s - loss: 0.0352 - accuracy: 0.9925\n","Epoch 00017: val_loss did not improve from 0.38919\n","200/200 [==============================] - 33s 165ms/step - loss: 0.0352 - accuracy: 0.9925 - val_loss: 0.4333 - val_accuracy: 0.8490\n","Epoch 18/100\n","200/200 [==============================] - ETA: 0s - loss: 0.0279 - accuracy: 0.9962\n","Epoch 00018: val_loss did not improve from 0.38919\n","200/200 [==============================] - 34s 166ms/step - loss: 0.0279 - accuracy: 0.9962 - val_loss: 0.4458 - val_accuracy: 0.8513\n","Epoch 19/100\n","200/200 [==============================] - ETA: 0s - loss: 0.0265 - accuracy: 0.9955\n","Epoch 00019: val_loss did not improve from 0.38919\n","200/200 [==============================] - 33s 165ms/step - loss: 0.0265 - accuracy: 0.9955 - val_loss: 0.4127 - val_accuracy: 0.8583\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["evaluate_model(tf.keras.models.load_model(GLOBAL_MODELS_FOLDER + '/experiment6_finetuning.h5'))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":730},"id":"CXjtLNlDKanH","executionInfo":{"status":"ok","timestamp":1642022447190,"user_tz":-60,"elapsed":12196,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"4566d289-4fe3-4c28-f321-a105e106b7a7"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["Classification report: \n","              precision    recall  f1-score   support\n","\n","           0     0.7026    0.8420    0.7660       696\n","           1     0.9153    0.8274    0.8692      1437\n","\n","    accuracy                         0.8322      2133\n","   macro avg     0.8090    0.8347    0.8176      2133\n","weighted avg     0.8459    0.8322    0.8355      2133\n","\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["### Experiment 7"],"metadata":{"id":"vbmnB_mf2poo"}},{"cell_type":"code","source":["fine_tuning = models.load_model(os.path.join(GLOBAL_MODELS_FOLDER, 'experiment3_feature_extraction.h5'))\n","\n","setTrainable = False\n","for layer in fine_tuning.get_layer('mobilenetv2_1.00_224').layers:\n","    if layer.name in ('block_10_expand'):\n","        setTrainable = True\n","    if setTrainable == True:\n","        layer.trainable = True\n","    else:\n","      layer.trainable = False\n","\n","compile_model(fine_tuning, learning_rate=1e-5)\n","\n","for i, layer in enumerate(fine_tuning.get_layer('mobilenetv2_1.00_224').layers):\n","    print(i, layer.name, layer.trainable)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1641396513111,"user_tz":-60,"elapsed":2204,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"6504d286-5d84-4767-8532-8571177a2f3b","id":"BvUKpI0K2nw3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model_12\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input_17 (InputLayer)       [(None, 224, 224, 3)]     0         \n","                                                                 \n"," tf.math.truediv_12 (TFOpLam  (None, 224, 224, 3)      0         \n"," bda)                                                            \n","                                                                 \n"," tf.math.subtract_12 (TFOpLa  (None, 224, 224, 3)      0         \n"," mbda)                                                           \n","                                                                 \n"," mobilenetv2_1.00_224 (Funct  (None, 7, 7, 1280)       2257984   \n"," ional)                                                          \n","                                                                 \n"," global_average_pooling2d_3   (None, 1280)             0         \n"," (GlobalAveragePooling2D)                                        \n","                                                                 \n"," dropout_5 (Dropout)         (None, 1280)              0         \n","                                                                 \n"," dense_16 (Dense)            (None, 1)                 1281      \n","                                                                 \n","=================================================================\n","Total params: 2,259,265\n","Trainable params: 1,985,793\n","Non-trainable params: 273,472\n","_________________________________________________________________\n","0 input_9 False\n","1 Conv1 False\n","2 bn_Conv1 False\n","3 Conv1_relu False\n","4 expanded_conv_depthwise False\n","5 expanded_conv_depthwise_BN False\n","6 expanded_conv_depthwise_relu False\n","7 expanded_conv_project False\n","8 expanded_conv_project_BN False\n","9 block_1_expand False\n","10 block_1_expand_BN False\n","11 block_1_expand_relu False\n","12 block_1_pad False\n","13 block_1_depthwise False\n","14 block_1_depthwise_BN False\n","15 block_1_depthwise_relu False\n","16 block_1_project False\n","17 block_1_project_BN False\n","18 block_2_expand False\n","19 block_2_expand_BN False\n","20 block_2_expand_relu False\n","21 block_2_depthwise False\n","22 block_2_depthwise_BN False\n","23 block_2_depthwise_relu False\n","24 block_2_project False\n","25 block_2_project_BN False\n","26 block_2_add False\n","27 block_3_expand False\n","28 block_3_expand_BN False\n","29 block_3_expand_relu False\n","30 block_3_pad False\n","31 block_3_depthwise False\n","32 block_3_depthwise_BN False\n","33 block_3_depthwise_relu False\n","34 block_3_project False\n","35 block_3_project_BN False\n","36 block_4_expand False\n","37 block_4_expand_BN False\n","38 block_4_expand_relu False\n","39 block_4_depthwise False\n","40 block_4_depthwise_BN False\n","41 block_4_depthwise_relu False\n","42 block_4_project False\n","43 block_4_project_BN False\n","44 block_4_add False\n","45 block_5_expand False\n","46 block_5_expand_BN False\n","47 block_5_expand_relu False\n","48 block_5_depthwise False\n","49 block_5_depthwise_BN False\n","50 block_5_depthwise_relu False\n","51 block_5_project False\n","52 block_5_project_BN False\n","53 block_5_add False\n","54 block_6_expand False\n","55 block_6_expand_BN False\n","56 block_6_expand_relu False\n","57 block_6_pad False\n","58 block_6_depthwise False\n","59 block_6_depthwise_BN False\n","60 block_6_depthwise_relu False\n","61 block_6_project False\n","62 block_6_project_BN False\n","63 block_7_expand False\n","64 block_7_expand_BN False\n","65 block_7_expand_relu False\n","66 block_7_depthwise False\n","67 block_7_depthwise_BN False\n","68 block_7_depthwise_relu False\n","69 block_7_project False\n","70 block_7_project_BN False\n","71 block_7_add False\n","72 block_8_expand False\n","73 block_8_expand_BN False\n","74 block_8_expand_relu False\n","75 block_8_depthwise False\n","76 block_8_depthwise_BN False\n","77 block_8_depthwise_relu False\n","78 block_8_project False\n","79 block_8_project_BN False\n","80 block_8_add False\n","81 block_9_expand False\n","82 block_9_expand_BN False\n","83 block_9_expand_relu False\n","84 block_9_depthwise False\n","85 block_9_depthwise_BN False\n","86 block_9_depthwise_relu False\n","87 block_9_project False\n","88 block_9_project_BN False\n","89 block_9_add False\n","90 block_10_expand True\n","91 block_10_expand_BN True\n","92 block_10_expand_relu True\n","93 block_10_depthwise True\n","94 block_10_depthwise_BN True\n","95 block_10_depthwise_relu True\n","96 block_10_project True\n","97 block_10_project_BN True\n","98 block_11_expand True\n","99 block_11_expand_BN True\n","100 block_11_expand_relu True\n","101 block_11_depthwise True\n","102 block_11_depthwise_BN True\n","103 block_11_depthwise_relu True\n","104 block_11_project True\n","105 block_11_project_BN True\n","106 block_11_add True\n","107 block_12_expand True\n","108 block_12_expand_BN True\n","109 block_12_expand_relu True\n","110 block_12_depthwise True\n","111 block_12_depthwise_BN True\n","112 block_12_depthwise_relu True\n","113 block_12_project True\n","114 block_12_project_BN True\n","115 block_12_add True\n","116 block_13_expand True\n","117 block_13_expand_BN True\n","118 block_13_expand_relu True\n","119 block_13_pad True\n","120 block_13_depthwise True\n","121 block_13_depthwise_BN True\n","122 block_13_depthwise_relu True\n","123 block_13_project True\n","124 block_13_project_BN True\n","125 block_14_expand True\n","126 block_14_expand_BN True\n","127 block_14_expand_relu True\n","128 block_14_depthwise True\n","129 block_14_depthwise_BN True\n","130 block_14_depthwise_relu True\n","131 block_14_project True\n","132 block_14_project_BN True\n","133 block_14_add True\n","134 block_15_expand True\n","135 block_15_expand_BN True\n","136 block_15_expand_relu True\n","137 block_15_depthwise True\n","138 block_15_depthwise_BN True\n","139 block_15_depthwise_relu True\n","140 block_15_project True\n","141 block_15_project_BN True\n","142 block_15_add True\n","143 block_16_expand True\n","144 block_16_expand_BN True\n","145 block_16_expand_relu True\n","146 block_16_depthwise True\n","147 block_16_depthwise_BN True\n","148 block_16_depthwise_relu True\n","149 block_16_project True\n","150 block_16_project_BN True\n","151 Conv_1 True\n","152 Conv_1_bn True\n","153 out_relu True\n"]}]},{"cell_type":"code","source":["run_model(fine_tuning, 'experiment7_finetuning', epochs = 100, patience=5)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"HLHeRE6E21HC","executionInfo":{"status":"ok","timestamp":1641397348565,"user_tz":-60,"elapsed":804713,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"d01e3c03-5729-4c46-fa7a-30125238f44d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","200/200 [==============================] - ETA: 0s - loss: 0.7863 - accuracy: 0.6831\n","Epoch 00001: val_loss improved from inf to 0.53581, saving model to /content/models/fine_tuning_2.h5\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n","  layer_config = serialize_layer_fn(layer)\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r200/200 [==============================] - 38s 170ms/step - loss: 0.7863 - accuracy: 0.6831 - val_loss: 0.5358 - val_accuracy: 0.7331\n","Epoch 2/100\n","200/200 [==============================] - ETA: 0s - loss: 0.5918 - accuracy: 0.7473\n","Epoch 00002: val_loss did not improve from 0.53581\n","200/200 [==============================] - 33s 164ms/step - loss: 0.5918 - accuracy: 0.7473 - val_loss: 0.6016 - val_accuracy: 0.7036\n","Epoch 3/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4917 - accuracy: 0.7805\n","Epoch 00003: val_loss did not improve from 0.53581\n","200/200 [==============================] - 33s 164ms/step - loss: 0.4917 - accuracy: 0.7805 - val_loss: 0.6172 - val_accuracy: 0.7054\n","Epoch 4/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4419 - accuracy: 0.7983\n","Epoch 00004: val_loss did not improve from 0.53581\n","200/200 [==============================] - 33s 165ms/step - loss: 0.4419 - accuracy: 0.7983 - val_loss: 0.5883 - val_accuracy: 0.7256\n","Epoch 5/100\n","200/200 [==============================] - ETA: 0s - loss: 0.3957 - accuracy: 0.8224\n","Epoch 00005: val_loss improved from 0.53581 to 0.53569, saving model to /content/models/fine_tuning_2.h5\n","200/200 [==============================] - 34s 166ms/step - loss: 0.3957 - accuracy: 0.8224 - val_loss: 0.5357 - val_accuracy: 0.7519\n","Epoch 6/100\n","200/200 [==============================] - ETA: 0s - loss: 0.3614 - accuracy: 0.8354\n","Epoch 00006: val_loss did not improve from 0.53569\n","200/200 [==============================] - 33s 164ms/step - loss: 0.3614 - accuracy: 0.8354 - val_loss: 0.5402 - val_accuracy: 0.7495\n","Epoch 7/100\n","200/200 [==============================] - ETA: 0s - loss: 0.3128 - accuracy: 0.8618\n","Epoch 00007: val_loss did not improve from 0.53569\n","200/200 [==============================] - 33s 165ms/step - loss: 0.3128 - accuracy: 0.8618 - val_loss: 0.5800 - val_accuracy: 0.7345\n","Epoch 8/100\n","200/200 [==============================] - ETA: 0s - loss: 0.2810 - accuracy: 0.8793\n","Epoch 00008: val_loss improved from 0.53569 to 0.53538, saving model to /content/models/fine_tuning_2.h5\n","200/200 [==============================] - 34s 167ms/step - loss: 0.2810 - accuracy: 0.8793 - val_loss: 0.5354 - val_accuracy: 0.7575\n","Epoch 9/100\n","200/200 [==============================] - ETA: 0s - loss: 0.2529 - accuracy: 0.8885\n","Epoch 00009: val_loss improved from 0.53538 to 0.46610, saving model to /content/models/fine_tuning_2.h5\n","200/200 [==============================] - 34s 167ms/step - loss: 0.2529 - accuracy: 0.8885 - val_loss: 0.4661 - val_accuracy: 0.7903\n","Epoch 10/100\n","200/200 [==============================] - ETA: 0s - loss: 0.2256 - accuracy: 0.9037\n","Epoch 00010: val_loss did not improve from 0.46610\n","200/200 [==============================] - 33s 165ms/step - loss: 0.2256 - accuracy: 0.9037 - val_loss: 0.4833 - val_accuracy: 0.7861\n","Epoch 11/100\n","200/200 [==============================] - ETA: 0s - loss: 0.1972 - accuracy: 0.9201\n","Epoch 00011: val_loss improved from 0.46610 to 0.41173, saving model to /content/models/fine_tuning_2.h5\n","200/200 [==============================] - 34s 167ms/step - loss: 0.1972 - accuracy: 0.9201 - val_loss: 0.4117 - val_accuracy: 0.8265\n","Epoch 12/100\n","200/200 [==============================] - ETA: 0s - loss: 0.1871 - accuracy: 0.9257\n","Epoch 00012: val_loss improved from 0.41173 to 0.38745, saving model to /content/models/fine_tuning_2.h5\n","200/200 [==============================] - 34s 167ms/step - loss: 0.1871 - accuracy: 0.9257 - val_loss: 0.3875 - val_accuracy: 0.8401\n","Epoch 13/100\n","200/200 [==============================] - ETA: 0s - loss: 0.1565 - accuracy: 0.9361\n","Epoch 00013: val_loss improved from 0.38745 to 0.34291, saving model to /content/models/fine_tuning_2.h5\n","200/200 [==============================] - 34s 167ms/step - loss: 0.1565 - accuracy: 0.9361 - val_loss: 0.3429 - val_accuracy: 0.8612\n","Epoch 14/100\n","200/200 [==============================] - ETA: 0s - loss: 0.1419 - accuracy: 0.9464\n","Epoch 00014: val_loss did not improve from 0.34291\n","200/200 [==============================] - 33s 164ms/step - loss: 0.1419 - accuracy: 0.9464 - val_loss: 0.3641 - val_accuracy: 0.8555\n","Epoch 15/100\n","200/200 [==============================] - ETA: 0s - loss: 0.1249 - accuracy: 0.9497\n","Epoch 00015: val_loss improved from 0.34291 to 0.34103, saving model to /content/models/fine_tuning_2.h5\n","200/200 [==============================] - 34s 167ms/step - loss: 0.1249 - accuracy: 0.9497 - val_loss: 0.3410 - val_accuracy: 0.8663\n","Epoch 16/100\n","200/200 [==============================] - ETA: 0s - loss: 0.1064 - accuracy: 0.9612\n","Epoch 00016: val_loss improved from 0.34103 to 0.34000, saving model to /content/models/fine_tuning_2.h5\n","200/200 [==============================] - 34s 169ms/step - loss: 0.1064 - accuracy: 0.9612 - val_loss: 0.3400 - val_accuracy: 0.8715\n","Epoch 17/100\n","200/200 [==============================] - ETA: 0s - loss: 0.0903 - accuracy: 0.9678\n","Epoch 00017: val_loss did not improve from 0.34000\n","200/200 [==============================] - 33s 165ms/step - loss: 0.0903 - accuracy: 0.9678 - val_loss: 0.3545 - val_accuracy: 0.8715\n","Epoch 18/100\n","200/200 [==============================] - ETA: 0s - loss: 0.0858 - accuracy: 0.9678\n","Epoch 00018: val_loss did not improve from 0.34000\n","200/200 [==============================] - 33s 166ms/step - loss: 0.0858 - accuracy: 0.9678 - val_loss: 0.3622 - val_accuracy: 0.8654\n","Epoch 19/100\n","200/200 [==============================] - ETA: 0s - loss: 0.0736 - accuracy: 0.9733\n","Epoch 00019: val_loss did not improve from 0.34000\n","200/200 [==============================] - 33s 166ms/step - loss: 0.0736 - accuracy: 0.9733 - val_loss: 0.3600 - val_accuracy: 0.8724\n","Epoch 20/100\n","200/200 [==============================] - ETA: 0s - loss: 0.0614 - accuracy: 0.9795\n","Epoch 00020: val_loss did not improve from 0.34000\n","200/200 [==============================] - 33s 165ms/step - loss: 0.0614 - accuracy: 0.9795 - val_loss: 0.3753 - val_accuracy: 0.8673\n","Epoch 21/100\n","200/200 [==============================] - ETA: 0s - loss: 0.0485 - accuracy: 0.9870\n","Epoch 00021: val_loss did not improve from 0.34000\n","200/200 [==============================] - 33s 166ms/step - loss: 0.0485 - accuracy: 0.9870 - val_loss: 0.3646 - val_accuracy: 0.8771\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["evaluate_model(tf.keras.models.load_model(GLOBAL_MODELS_FOLDER + '/experiment7_finetuning.h5'))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":730},"id":"RMdgc5626d-T","executionInfo":{"status":"ok","timestamp":1641397509290,"user_tz":-60,"elapsed":10074,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"b2121628-4ee0-4c85-fa61-f5403dcf8386"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Classification report: \n","              precision    recall  f1-score   support\n","\n","           0     0.7823    0.8362    0.8083       696\n","           1     0.9179    0.8873    0.9023      1437\n","\n","    accuracy                         0.8706      2133\n","   macro avg     0.8501    0.8617    0.8553      2133\n","weighted avg     0.8737    0.8706    0.8717      2133\n","\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["## Remove last block"],"metadata":{"id":"MsfqX3PU_dX6"}},{"cell_type":"code","source":["def get_index_by_layer_name(model, layer_name):\n","  '''\n","  get_index_by_layer_name is used to retrieve the index of a specific layer\n","  :param model: model to check\n","  :param layer_name: name of the layer we want to get the index of\n","  :return: the index of the layer named as defined in layer_name\n","  '''\n","  for index, layer in enumerate(model.layers):\n","      if layer.name == layer_name:\n","          return index"],"metadata":{"id":"6XJywktEWESX"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["conv_base = tf.keras.applications.MobileNetV2(\n","    input_shape=(IMAGE_HEIGHT,IMAGE_WIDTH,3),\n","    alpha=1,\n","    include_top= False,\n","    weights=\"imagenet\",\n",")"],"metadata":{"id":"xsprMjyzAXZB"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["base = Model(conv_base.input, conv_base.layers[get_index_by_layer_name(conv_base, 'block_15_add')].output, name='base')\n","base.trainable = False\n","base.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"aUdVNHXdAacc","executionInfo":{"status":"ok","timestamp":1642021015116,"user_tz":-60,"elapsed":590,"user":{"displayName":"DANIELE CIOFFO","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10062146559806518037"}},"outputId":"9eb6f8a0-64f0-4695-99b6-c71286881689"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"base\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               \n","                                )]                                                                \n","                                                                                                  \n"," Conv1 (Conv2D)                 (None, 112, 112, 32  864         ['input_1[0][0]']                \n","                                )                                                                 \n","                                                                                                  \n"," bn_Conv1 (BatchNormalization)  (None, 112, 112, 32  128         ['Conv1[0][0]']                  \n","                                )                                                                 \n","                                                                                                  \n"," Conv1_relu (ReLU)              (None, 112, 112, 32  0           ['bn_Conv1[0][0]']               \n","                                )                                                                 \n","                                                                                                  \n"," expanded_conv_depthwise (Depth  (None, 112, 112, 32  288        ['Conv1_relu[0][0]']             \n"," wiseConv2D)                    )                                                                 \n","                                                                                                  \n"," expanded_conv_depthwise_BN (Ba  (None, 112, 112, 32  128        ['expanded_conv_depthwise[0][0]']\n"," tchNormalization)              )                                                                 \n","                                                                                                  \n"," expanded_conv_depthwise_relu (  (None, 112, 112, 32  0          ['expanded_conv_depthwise_BN[0][0\n"," ReLU)                          )                                ]']                              \n","                                                                                                  \n"," expanded_conv_project (Conv2D)  (None, 112, 112, 16  512        ['expanded_conv_depthwise_relu[0]\n","                                )                                [0]']                            \n","                                                                                                  \n"," expanded_conv_project_BN (Batc  (None, 112, 112, 16  64         ['expanded_conv_project[0][0]']  \n"," hNormalization)                )                                                                 \n","                                                                                                  \n"," block_1_expand (Conv2D)        (None, 112, 112, 96  1536        ['expanded_conv_project_BN[0][0]'\n","                                )                                ]                                \n","                                                                                                  \n"," block_1_expand_BN (BatchNormal  (None, 112, 112, 96  384        ['block_1_expand[0][0]']         \n"," ization)                       )                                                                 \n","                                                                                                  \n"," block_1_expand_relu (ReLU)     (None, 112, 112, 96  0           ['block_1_expand_BN[0][0]']      \n","                                )                                                                 \n","                                                                                                  \n"," block_1_pad (ZeroPadding2D)    (None, 113, 113, 96  0           ['block_1_expand_relu[0][0]']    \n","                                )                                                                 \n","                                                                                                  \n"," block_1_depthwise (DepthwiseCo  (None, 56, 56, 96)  864         ['block_1_pad[0][0]']            \n"," nv2D)                                                                                            \n","                                                                                                  \n"," block_1_depthwise_BN (BatchNor  (None, 56, 56, 96)  384         ['block_1_depthwise[0][0]']      \n"," malization)                                                                                      \n","                                                                                                  \n"," block_1_depthwise_relu (ReLU)  (None, 56, 56, 96)   0           ['block_1_depthwise_BN[0][0]']   \n","                                                                                                  \n"," block_1_project (Conv2D)       (None, 56, 56, 24)   2304        ['block_1_depthwise_relu[0][0]'] \n","                                                                                                  \n"," block_1_project_BN (BatchNorma  (None, 56, 56, 24)  96          ['block_1_project[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_2_expand (Conv2D)        (None, 56, 56, 144)  3456        ['block_1_project_BN[0][0]']     \n","                                                                                                  \n"," block_2_expand_BN (BatchNormal  (None, 56, 56, 144)  576        ['block_2_expand[0][0]']         \n"," ization)                                                                                         \n","                                                                                                  \n"," block_2_expand_relu (ReLU)     (None, 56, 56, 144)  0           ['block_2_expand_BN[0][0]']      \n","                                                                                                  \n"," block_2_depthwise (DepthwiseCo  (None, 56, 56, 144)  1296       ['block_2_expand_relu[0][0]']    \n"," nv2D)                                                                                            \n","                                                                                                  \n"," block_2_depthwise_BN (BatchNor  (None, 56, 56, 144)  576        ['block_2_depthwise[0][0]']      \n"," malization)                                                                                      \n","                                                                                                  \n"," block_2_depthwise_relu (ReLU)  (None, 56, 56, 144)  0           ['block_2_depthwise_BN[0][0]']   \n","                                                                                                  \n"," block_2_project (Conv2D)       (None, 56, 56, 24)   3456        ['block_2_depthwise_relu[0][0]'] \n","                                                                                                  \n"," block_2_project_BN (BatchNorma  (None, 56, 56, 24)  96          ['block_2_project[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_2_add (Add)              (None, 56, 56, 24)   0           ['block_1_project_BN[0][0]',     \n","                                                                  'block_2_project_BN[0][0]']     \n","                                                                                                  \n"," block_3_expand (Conv2D)        (None, 56, 56, 144)  3456        ['block_2_add[0][0]']            \n","                                                                                                  \n"," block_3_expand_BN (BatchNormal  (None, 56, 56, 144)  576        ['block_3_expand[0][0]']         \n"," ization)                                                                                         \n","                                                                                                  \n"," block_3_expand_relu (ReLU)     (None, 56, 56, 144)  0           ['block_3_expand_BN[0][0]']      \n","                                                                                                  \n"," block_3_pad (ZeroPadding2D)    (None, 57, 57, 144)  0           ['block_3_expand_relu[0][0]']    \n","                                                                                                  \n"," block_3_depthwise (DepthwiseCo  (None, 28, 28, 144)  1296       ['block_3_pad[0][0]']            \n"," nv2D)                                                                                            \n","                                                                                                  \n"," block_3_depthwise_BN (BatchNor  (None, 28, 28, 144)  576        ['block_3_depthwise[0][0]']      \n"," malization)                                                                                      \n","                                                                                                  \n"," block_3_depthwise_relu (ReLU)  (None, 28, 28, 144)  0           ['block_3_depthwise_BN[0][0]']   \n","                                                                                                  \n"," block_3_project (Conv2D)       (None, 28, 28, 32)   4608        ['block_3_depthwise_relu[0][0]'] \n","                                                                                                  \n"," block_3_project_BN (BatchNorma  (None, 28, 28, 32)  128         ['block_3_project[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_4_expand (Conv2D)        (None, 28, 28, 192)  6144        ['block_3_project_BN[0][0]']     \n","                                                                                                  \n"," block_4_expand_BN (BatchNormal  (None, 28, 28, 192)  768        ['block_4_expand[0][0]']         \n"," ization)                                                                                         \n","                                                                                                  \n"," block_4_expand_relu (ReLU)     (None, 28, 28, 192)  0           ['block_4_expand_BN[0][0]']      \n","                                                                                                  \n"," block_4_depthwise (DepthwiseCo  (None, 28, 28, 192)  1728       ['block_4_expand_relu[0][0]']    \n"," nv2D)                                                                                            \n","                                                                                                  \n"," block_4_depthwise_BN (BatchNor  (None, 28, 28, 192)  768        ['block_4_depthwise[0][0]']      \n"," malization)                                                                                      \n","                                                                                                  \n"," block_4_depthwise_relu (ReLU)  (None, 28, 28, 192)  0           ['block_4_depthwise_BN[0][0]']   \n","                                                                                                  \n"," block_4_project (Conv2D)       (None, 28, 28, 32)   6144        ['block_4_depthwise_relu[0][0]'] \n","                                                                                                  \n"," block_4_project_BN (BatchNorma  (None, 28, 28, 32)  128         ['block_4_project[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_4_add (Add)              (None, 28, 28, 32)   0           ['block_3_project_BN[0][0]',     \n","                                                                  'block_4_project_BN[0][0]']     \n","                                                                                                  \n"," block_5_expand (Conv2D)        (None, 28, 28, 192)  6144        ['block_4_add[0][0]']            \n","                                                                                                  \n"," block_5_expand_BN (BatchNormal  (None, 28, 28, 192)  768        ['block_5_expand[0][0]']         \n"," ization)                                                                                         \n","                                                                                                  \n"," block_5_expand_relu (ReLU)     (None, 28, 28, 192)  0           ['block_5_expand_BN[0][0]']      \n","                                                                                                  \n"," block_5_depthwise (DepthwiseCo  (None, 28, 28, 192)  1728       ['block_5_expand_relu[0][0]']    \n"," nv2D)                                                                                            \n","                                                                                                  \n"," block_5_depthwise_BN (BatchNor  (None, 28, 28, 192)  768        ['block_5_depthwise[0][0]']      \n"," malization)                                                                                      \n","                                                                                                  \n"," block_5_depthwise_relu (ReLU)  (None, 28, 28, 192)  0           ['block_5_depthwise_BN[0][0]']   \n","                                                                                                  \n"," block_5_project (Conv2D)       (None, 28, 28, 32)   6144        ['block_5_depthwise_relu[0][0]'] \n","                                                                                                  \n"," block_5_project_BN (BatchNorma  (None, 28, 28, 32)  128         ['block_5_project[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_5_add (Add)              (None, 28, 28, 32)   0           ['block_4_add[0][0]',            \n","                                                                  'block_5_project_BN[0][0]']     \n","                                                                                                  \n"," block_6_expand (Conv2D)        (None, 28, 28, 192)  6144        ['block_5_add[0][0]']            \n","                                                                                                  \n"," block_6_expand_BN (BatchNormal  (None, 28, 28, 192)  768        ['block_6_expand[0][0]']         \n"," ization)                                                                                         \n","                                                                                                  \n"," block_6_expand_relu (ReLU)     (None, 28, 28, 192)  0           ['block_6_expand_BN[0][0]']      \n","                                                                                                  \n"," block_6_pad (ZeroPadding2D)    (None, 29, 29, 192)  0           ['block_6_expand_relu[0][0]']    \n","                                                                                                  \n"," block_6_depthwise (DepthwiseCo  (None, 14, 14, 192)  1728       ['block_6_pad[0][0]']            \n"," nv2D)                                                                                            \n","                                                                                                  \n"," block_6_depthwise_BN (BatchNor  (None, 14, 14, 192)  768        ['block_6_depthwise[0][0]']      \n"," malization)                                                                                      \n","                                                                                                  \n"," block_6_depthwise_relu (ReLU)  (None, 14, 14, 192)  0           ['block_6_depthwise_BN[0][0]']   \n","                                                                                                  \n"," block_6_project (Conv2D)       (None, 14, 14, 64)   12288       ['block_6_depthwise_relu[0][0]'] \n","                                                                                                  \n"," block_6_project_BN (BatchNorma  (None, 14, 14, 64)  256         ['block_6_project[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_7_expand (Conv2D)        (None, 14, 14, 384)  24576       ['block_6_project_BN[0][0]']     \n","                                                                                                  \n"," block_7_expand_BN (BatchNormal  (None, 14, 14, 384)  1536       ['block_7_expand[0][0]']         \n"," ization)                                                                                         \n","                                                                                                  \n"," block_7_expand_relu (ReLU)     (None, 14, 14, 384)  0           ['block_7_expand_BN[0][0]']      \n","                                                                                                  \n"," block_7_depthwise (DepthwiseCo  (None, 14, 14, 384)  3456       ['block_7_expand_relu[0][0]']    \n"," nv2D)                                                                                            \n","                                                                                                  \n"," block_7_depthwise_BN (BatchNor  (None, 14, 14, 384)  1536       ['block_7_depthwise[0][0]']      \n"," malization)                                                                                      \n","                                                                                                  \n"," block_7_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           ['block_7_depthwise_BN[0][0]']   \n","                                                                                                  \n"," block_7_project (Conv2D)       (None, 14, 14, 64)   24576       ['block_7_depthwise_relu[0][0]'] \n","                                                                                                  \n"," block_7_project_BN (BatchNorma  (None, 14, 14, 64)  256         ['block_7_project[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_7_add (Add)              (None, 14, 14, 64)   0           ['block_6_project_BN[0][0]',     \n","                                                                  'block_7_project_BN[0][0]']     \n","                                                                                                  \n"," block_8_expand (Conv2D)        (None, 14, 14, 384)  24576       ['block_7_add[0][0]']            \n","                                                                                                  \n"," block_8_expand_BN (BatchNormal  (None, 14, 14, 384)  1536       ['block_8_expand[0][0]']         \n"," ization)                                                                                         \n","                                                                                                  \n"," block_8_expand_relu (ReLU)     (None, 14, 14, 384)  0           ['block_8_expand_BN[0][0]']      \n","                                                                                                  \n"," block_8_depthwise (DepthwiseCo  (None, 14, 14, 384)  3456       ['block_8_expand_relu[0][0]']    \n"," nv2D)                                                                                            \n","                                                                                                  \n"," block_8_depthwise_BN (BatchNor  (None, 14, 14, 384)  1536       ['block_8_depthwise[0][0]']      \n"," malization)                                                                                      \n","                                                                                                  \n"," block_8_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           ['block_8_depthwise_BN[0][0]']   \n","                                                                                                  \n"," block_8_project (Conv2D)       (None, 14, 14, 64)   24576       ['block_8_depthwise_relu[0][0]'] \n","                                                                                                  \n"," block_8_project_BN (BatchNorma  (None, 14, 14, 64)  256         ['block_8_project[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_8_add (Add)              (None, 14, 14, 64)   0           ['block_7_add[0][0]',            \n","                                                                  'block_8_project_BN[0][0]']     \n","                                                                                                  \n"," block_9_expand (Conv2D)        (None, 14, 14, 384)  24576       ['block_8_add[0][0]']            \n","                                                                                                  \n"," block_9_expand_BN (BatchNormal  (None, 14, 14, 384)  1536       ['block_9_expand[0][0]']         \n"," ization)                                                                                         \n","                                                                                                  \n"," block_9_expand_relu (ReLU)     (None, 14, 14, 384)  0           ['block_9_expand_BN[0][0]']      \n","                                                                                                  \n"," block_9_depthwise (DepthwiseCo  (None, 14, 14, 384)  3456       ['block_9_expand_relu[0][0]']    \n"," nv2D)                                                                                            \n","                                                                                                  \n"," block_9_depthwise_BN (BatchNor  (None, 14, 14, 384)  1536       ['block_9_depthwise[0][0]']      \n"," malization)                                                                                      \n","                                                                                                  \n"," block_9_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           ['block_9_depthwise_BN[0][0]']   \n","                                                                                                  \n"," block_9_project (Conv2D)       (None, 14, 14, 64)   24576       ['block_9_depthwise_relu[0][0]'] \n","                                                                                                  \n"," block_9_project_BN (BatchNorma  (None, 14, 14, 64)  256         ['block_9_project[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_9_add (Add)              (None, 14, 14, 64)   0           ['block_8_add[0][0]',            \n","                                                                  'block_9_project_BN[0][0]']     \n","                                                                                                  \n"," block_10_expand (Conv2D)       (None, 14, 14, 384)  24576       ['block_9_add[0][0]']            \n","                                                                                                  \n"," block_10_expand_BN (BatchNorma  (None, 14, 14, 384)  1536       ['block_10_expand[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_10_expand_relu (ReLU)    (None, 14, 14, 384)  0           ['block_10_expand_BN[0][0]']     \n","                                                                                                  \n"," block_10_depthwise (DepthwiseC  (None, 14, 14, 384)  3456       ['block_10_expand_relu[0][0]']   \n"," onv2D)                                                                                           \n","                                                                                                  \n"," block_10_depthwise_BN (BatchNo  (None, 14, 14, 384)  1536       ['block_10_depthwise[0][0]']     \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block_10_depthwise_relu (ReLU)  (None, 14, 14, 384)  0          ['block_10_depthwise_BN[0][0]']  \n","                                                                                                  \n"," block_10_project (Conv2D)      (None, 14, 14, 96)   36864       ['block_10_depthwise_relu[0][0]']\n","                                                                                                  \n"," block_10_project_BN (BatchNorm  (None, 14, 14, 96)  384         ['block_10_project[0][0]']       \n"," alization)                                                                                       \n","                                                                                                  \n"," block_11_expand (Conv2D)       (None, 14, 14, 576)  55296       ['block_10_project_BN[0][0]']    \n","                                                                                                  \n"," block_11_expand_BN (BatchNorma  (None, 14, 14, 576)  2304       ['block_11_expand[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_11_expand_relu (ReLU)    (None, 14, 14, 576)  0           ['block_11_expand_BN[0][0]']     \n","                                                                                                  \n"," block_11_depthwise (DepthwiseC  (None, 14, 14, 576)  5184       ['block_11_expand_relu[0][0]']   \n"," onv2D)                                                                                           \n","                                                                                                  \n"," block_11_depthwise_BN (BatchNo  (None, 14, 14, 576)  2304       ['block_11_depthwise[0][0]']     \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block_11_depthwise_relu (ReLU)  (None, 14, 14, 576)  0          ['block_11_depthwise_BN[0][0]']  \n","                                                                                                  \n"," block_11_project (Conv2D)      (None, 14, 14, 96)   55296       ['block_11_depthwise_relu[0][0]']\n","                                                                                                  \n"," block_11_project_BN (BatchNorm  (None, 14, 14, 96)  384         ['block_11_project[0][0]']       \n"," alization)                                                                                       \n","                                                                                                  \n"," block_11_add (Add)             (None, 14, 14, 96)   0           ['block_10_project_BN[0][0]',    \n","                                                                  'block_11_project_BN[0][0]']    \n","                                                                                                  \n"," block_12_expand (Conv2D)       (None, 14, 14, 576)  55296       ['block_11_add[0][0]']           \n","                                                                                                  \n"," block_12_expand_BN (BatchNorma  (None, 14, 14, 576)  2304       ['block_12_expand[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_12_expand_relu (ReLU)    (None, 14, 14, 576)  0           ['block_12_expand_BN[0][0]']     \n","                                                                                                  \n"," block_12_depthwise (DepthwiseC  (None, 14, 14, 576)  5184       ['block_12_expand_relu[0][0]']   \n"," onv2D)                                                                                           \n","                                                                                                  \n"," block_12_depthwise_BN (BatchNo  (None, 14, 14, 576)  2304       ['block_12_depthwise[0][0]']     \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block_12_depthwise_relu (ReLU)  (None, 14, 14, 576)  0          ['block_12_depthwise_BN[0][0]']  \n","                                                                                                  \n"," block_12_project (Conv2D)      (None, 14, 14, 96)   55296       ['block_12_depthwise_relu[0][0]']\n","                                                                                                  \n"," block_12_project_BN (BatchNorm  (None, 14, 14, 96)  384         ['block_12_project[0][0]']       \n"," alization)                                                                                       \n","                                                                                                  \n"," block_12_add (Add)             (None, 14, 14, 96)   0           ['block_11_add[0][0]',           \n","                                                                  'block_12_project_BN[0][0]']    \n","                                                                                                  \n"," block_13_expand (Conv2D)       (None, 14, 14, 576)  55296       ['block_12_add[0][0]']           \n","                                                                                                  \n"," block_13_expand_BN (BatchNorma  (None, 14, 14, 576)  2304       ['block_13_expand[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_13_expand_relu (ReLU)    (None, 14, 14, 576)  0           ['block_13_expand_BN[0][0]']     \n","                                                                                                  \n"," block_13_pad (ZeroPadding2D)   (None, 15, 15, 576)  0           ['block_13_expand_relu[0][0]']   \n","                                                                                                  \n"," block_13_depthwise (DepthwiseC  (None, 7, 7, 576)   5184        ['block_13_pad[0][0]']           \n"," onv2D)                                                                                           \n","                                                                                                  \n"," block_13_depthwise_BN (BatchNo  (None, 7, 7, 576)   2304        ['block_13_depthwise[0][0]']     \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block_13_depthwise_relu (ReLU)  (None, 7, 7, 576)   0           ['block_13_depthwise_BN[0][0]']  \n","                                                                                                  \n"," block_13_project (Conv2D)      (None, 7, 7, 160)    92160       ['block_13_depthwise_relu[0][0]']\n","                                                                                                  \n"," block_13_project_BN (BatchNorm  (None, 7, 7, 160)   640         ['block_13_project[0][0]']       \n"," alization)                                                                                       \n","                                                                                                  \n"," block_14_expand (Conv2D)       (None, 7, 7, 960)    153600      ['block_13_project_BN[0][0]']    \n","                                                                                                  \n"," block_14_expand_BN (BatchNorma  (None, 7, 7, 960)   3840        ['block_14_expand[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_14_expand_relu (ReLU)    (None, 7, 7, 960)    0           ['block_14_expand_BN[0][0]']     \n","                                                                                                  \n"," block_14_depthwise (DepthwiseC  (None, 7, 7, 960)   8640        ['block_14_expand_relu[0][0]']   \n"," onv2D)                                                                                           \n","                                                                                                  \n"," block_14_depthwise_BN (BatchNo  (None, 7, 7, 960)   3840        ['block_14_depthwise[0][0]']     \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block_14_depthwise_relu (ReLU)  (None, 7, 7, 960)   0           ['block_14_depthwise_BN[0][0]']  \n","                                                                                                  \n"," block_14_project (Conv2D)      (None, 7, 7, 160)    153600      ['block_14_depthwise_relu[0][0]']\n","                                                                                                  \n"," block_14_project_BN (BatchNorm  (None, 7, 7, 160)   640         ['block_14_project[0][0]']       \n"," alization)                                                                                       \n","                                                                                                  \n"," block_14_add (Add)             (None, 7, 7, 160)    0           ['block_13_project_BN[0][0]',    \n","                                                                  'block_14_project_BN[0][0]']    \n","                                                                                                  \n"," block_15_expand (Conv2D)       (None, 7, 7, 960)    153600      ['block_14_add[0][0]']           \n","                                                                                                  \n"," block_15_expand_BN (BatchNorma  (None, 7, 7, 960)   3840        ['block_15_expand[0][0]']        \n"," lization)                                                                                        \n","                                                                                                  \n"," block_15_expand_relu (ReLU)    (None, 7, 7, 960)    0           ['block_15_expand_BN[0][0]']     \n","                                                                                                  \n"," block_15_depthwise (DepthwiseC  (None, 7, 7, 960)   8640        ['block_15_expand_relu[0][0]']   \n"," onv2D)                                                                                           \n","                                                                                                  \n"," block_15_depthwise_BN (BatchNo  (None, 7, 7, 960)   3840        ['block_15_depthwise[0][0]']     \n"," rmalization)                                                                                     \n","                                                                                                  \n"," block_15_depthwise_relu (ReLU)  (None, 7, 7, 960)   0           ['block_15_depthwise_BN[0][0]']  \n","                                                                                                  \n"," block_15_project (Conv2D)      (None, 7, 7, 160)    153600      ['block_15_depthwise_relu[0][0]']\n","                                                                                                  \n"," block_15_project_BN (BatchNorm  (None, 7, 7, 160)   640         ['block_15_project[0][0]']       \n"," alization)                                                                                       \n","                                                                                                  \n"," block_15_add (Add)             (None, 7, 7, 160)    0           ['block_14_add[0][0]',           \n","                                                                  'block_15_project_BN[0][0]']    \n","                                                                                                  \n","==================================================================================================\n","Total params: 1,364,864\n","Trainable params: 0\n","Non-trainable params: 1,364,864\n","__________________________________________________________________________________________________\n"]}]},{"cell_type":"markdown","source":["### Feature Extraction"],"metadata":{"id":"NviwEtgsAoCU"}},{"cell_type":"markdown","source":["#### Experiment 8"],"metadata":{"id":"WdAfQDXHA_1c"}},{"cell_type":"code","source":["inputs = keras.Input(shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3))\n","x = tf.keras.applications.mobilenet.preprocess_input(inputs)\n","x = base(x) # feature extraction\n","x = layers.GlobalAveragePooling2D()(x)\n","x = layers.Dense(256)(x)\n","x = layers.Dropout(0.5)(x)\n","outputs = layers.Dense(1, activation='sigmoid')(x)\n","model2 = keras.Model(inputs=inputs, outputs=outputs)\n","compile_model(model2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VHsmUcQBAvMK","executionInfo":{"status":"ok","timestamp":1641801947075,"user_tz":-60,"elapsed":1042,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"61d77ca8-417b-4d9e-ef9a-d7810744b7cd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model_2\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input_5 (InputLayer)        [(None, 224, 224, 3)]     0         \n","                                                                 \n"," tf.math.truediv_2 (TFOpLamb  (None, 224, 224, 3)      0         \n"," da)                                                             \n","                                                                 \n"," tf.math.subtract_2 (TFOpLam  (None, 224, 224, 3)      0         \n"," bda)                                                            \n","                                                                 \n"," base (Functional)           (None, 7, 7, 160)         1364864   \n","                                                                 \n"," global_average_pooling2d_1   (None, 160)              0         \n"," (GlobalAveragePooling2D)                                        \n","                                                                 \n"," dense_3 (Dense)             (None, 256)               41216     \n","                                                                 \n"," dropout_1 (Dropout)         (None, 256)               0         \n","                                                                 \n"," dense_4 (Dense)             (None, 1)                 257       \n","                                                                 \n","=================================================================\n","Total params: 1,406,337\n","Trainable params: 41,473\n","Non-trainable params: 1,364,864\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["run_model(model2, 'experiment8_feature_extraction', epochs = 50, patience=5)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"hHaGEHCYA3DZ","executionInfo":{"status":"ok","timestamp":1641802498166,"user_tz":-60,"elapsed":548658,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"d8981630-170f-43fd-e3f2-998067f1b1a7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/50\n","200/200 [==============================] - ETA: 0s - loss: 0.6490 - accuracy: 0.6928\n","Epoch 00001: val_loss improved from inf to 0.87828, saving model to /content/models/experiment7_feature_extraction.h5\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n","  layer_config = serialize_layer_fn(layer)\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r200/200 [==============================] - 28s 122ms/step - loss: 0.6490 - accuracy: 0.6928 - val_loss: 0.8783 - val_accuracy: 0.5188\n","Epoch 2/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5603 - accuracy: 0.7275\n","Epoch 00002: val_loss improved from 0.87828 to 0.48281, saving model to /content/models/experiment7_feature_extraction.h5\n","200/200 [==============================] - 24s 119ms/step - loss: 0.5603 - accuracy: 0.7275 - val_loss: 0.4828 - val_accuracy: 0.7720\n","Epoch 3/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5346 - accuracy: 0.7406\n","Epoch 00003: val_loss improved from 0.48281 to 0.45200, saving model to /content/models/experiment7_feature_extraction.h5\n","200/200 [==============================] - 24s 119ms/step - loss: 0.5346 - accuracy: 0.7406 - val_loss: 0.4520 - val_accuracy: 0.7974\n","Epoch 4/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5182 - accuracy: 0.7472\n","Epoch 00004: val_loss did not improve from 0.45200\n","200/200 [==============================] - 24s 118ms/step - loss: 0.5182 - accuracy: 0.7472 - val_loss: 0.5543 - val_accuracy: 0.7111\n","Epoch 5/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5113 - accuracy: 0.7500\n","Epoch 00005: val_loss improved from 0.45200 to 0.44939, saving model to /content/models/experiment7_feature_extraction.h5\n","200/200 [==============================] - 24s 118ms/step - loss: 0.5113 - accuracy: 0.7500 - val_loss: 0.4494 - val_accuracy: 0.7983\n","Epoch 6/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5020 - accuracy: 0.7575\n","Epoch 00006: val_loss did not improve from 0.44939\n","200/200 [==============================] - 24s 116ms/step - loss: 0.5020 - accuracy: 0.7575 - val_loss: 0.4629 - val_accuracy: 0.7824\n","Epoch 7/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5035 - accuracy: 0.7561\n","Epoch 00007: val_loss did not improve from 0.44939\n","200/200 [==============================] - 24s 117ms/step - loss: 0.5035 - accuracy: 0.7561 - val_loss: 0.4578 - val_accuracy: 0.7889\n","Epoch 8/50\n","200/200 [==============================] - ETA: 0s - loss: 0.4960 - accuracy: 0.7611\n","Epoch 00008: val_loss improved from 0.44939 to 0.44769, saving model to /content/models/experiment7_feature_extraction.h5\n","200/200 [==============================] - 24s 119ms/step - loss: 0.4960 - accuracy: 0.7611 - val_loss: 0.4477 - val_accuracy: 0.7983\n","Epoch 9/50\n","200/200 [==============================] - ETA: 0s - loss: 0.4983 - accuracy: 0.7625\n","Epoch 00009: val_loss did not improve from 0.44769\n","200/200 [==============================] - 24s 117ms/step - loss: 0.4983 - accuracy: 0.7625 - val_loss: 0.4867 - val_accuracy: 0.7697\n","Epoch 10/50\n","200/200 [==============================] - ETA: 0s - loss: 0.4968 - accuracy: 0.7630\n","Epoch 00010: val_loss did not improve from 0.44769\n","200/200 [==============================] - 24s 117ms/step - loss: 0.4968 - accuracy: 0.7630 - val_loss: 0.4810 - val_accuracy: 0.7720\n","Epoch 11/50\n","200/200 [==============================] - ETA: 0s - loss: 0.4961 - accuracy: 0.7570\n","Epoch 00011: val_loss did not improve from 0.44769\n","200/200 [==============================] - 24s 118ms/step - loss: 0.4961 - accuracy: 0.7570 - val_loss: 0.5017 - val_accuracy: 0.7505\n","Epoch 12/50\n","200/200 [==============================] - ETA: 0s - loss: 0.4882 - accuracy: 0.7608\n","Epoch 00012: val_loss did not improve from 0.44769\n","200/200 [==============================] - 28s 140ms/step - loss: 0.4882 - accuracy: 0.7608 - val_loss: 0.4601 - val_accuracy: 0.7833\n","Epoch 13/50\n","200/200 [==============================] - ETA: 0s - loss: 0.4898 - accuracy: 0.7692\n","Epoch 00013: val_loss improved from 0.44769 to 0.44622, saving model to /content/models/experiment7_feature_extraction.h5\n","200/200 [==============================] - 24s 119ms/step - loss: 0.4898 - accuracy: 0.7692 - val_loss: 0.4462 - val_accuracy: 0.8011\n","Epoch 14/50\n","200/200 [==============================] - ETA: 0s - loss: 0.4906 - accuracy: 0.7658\n","Epoch 00014: val_loss did not improve from 0.44622\n","200/200 [==============================] - 24s 117ms/step - loss: 0.4906 - accuracy: 0.7658 - val_loss: 0.4544 - val_accuracy: 0.7875\n","Epoch 15/50\n","200/200 [==============================] - ETA: 0s - loss: 0.4933 - accuracy: 0.7636\n","Epoch 00015: val_loss improved from 0.44622 to 0.44360, saving model to /content/models/experiment7_feature_extraction.h5\n","200/200 [==============================] - 24s 118ms/step - loss: 0.4933 - accuracy: 0.7636 - val_loss: 0.4436 - val_accuracy: 0.7960\n","Epoch 16/50\n","200/200 [==============================] - ETA: 0s - loss: 0.4871 - accuracy: 0.7680\n","Epoch 00016: val_loss did not improve from 0.44360\n","200/200 [==============================] - 24s 117ms/step - loss: 0.4871 - accuracy: 0.7680 - val_loss: 0.5020 - val_accuracy: 0.7542\n","Epoch 17/50\n","200/200 [==============================] - ETA: 0s - loss: 0.4879 - accuracy: 0.7644\n","Epoch 00017: val_loss did not improve from 0.44360\n","200/200 [==============================] - 24s 117ms/step - loss: 0.4879 - accuracy: 0.7644 - val_loss: 0.4862 - val_accuracy: 0.7678\n","Epoch 18/50\n","200/200 [==============================] - ETA: 0s - loss: 0.4842 - accuracy: 0.7692\n","Epoch 00018: val_loss did not improve from 0.44360\n","200/200 [==============================] - 23s 116ms/step - loss: 0.4842 - accuracy: 0.7692 - val_loss: 0.4913 - val_accuracy: 0.7575\n","Epoch 19/50\n","200/200 [==============================] - ETA: 0s - loss: 0.4870 - accuracy: 0.7672\n","Epoch 00019: val_loss did not improve from 0.44360\n","200/200 [==============================] - 24s 117ms/step - loss: 0.4870 - accuracy: 0.7672 - val_loss: 0.4720 - val_accuracy: 0.7767\n","Epoch 20/50\n","200/200 [==============================] - ETA: 0s - loss: 0.4843 - accuracy: 0.7644\n","Epoch 00020: val_loss did not improve from 0.44360\n","200/200 [==============================] - 23s 116ms/step - loss: 0.4843 - accuracy: 0.7644 - val_loss: 0.4708 - val_accuracy: 0.7739\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["evaluate_model(tf.keras.models.load_model(GLOBAL_MODELS_FOLDER + '/experiment8_feature_extraction.h5'))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":734},"id":"SUau5hckGjea","executionInfo":{"status":"ok","timestamp":1641925761839,"user_tz":-60,"elapsed":20933,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"06ce9bcf-4317-4d1e-fb6d-719ff77601b7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Classification report: \n","              precision    recall  f1-score   support\n","\n","           0     0.6789    0.7198    0.6987       696\n","           1     0.8602    0.8351    0.8475      1437\n","\n","    accuracy                         0.7975      2133\n","   macro avg     0.7695    0.7775    0.7731      2133\n","weighted avg     0.8010    0.7975    0.7989      2133\n","\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["#### Experiment 9"],"metadata":{"id":"tG0v47YKBLLC"}},{"cell_type":"code","source":["inputs = keras.Input(shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3))\n","x = tf.keras.applications.mobilenet.preprocess_input(inputs)\n","x = base(x) # feature extraction\n","x = layers.GlobalAveragePooling2D()(x)\n","x = layers.Dropout(0.5)(x)\n","outputs = layers.Dense(1, activation='sigmoid')(x)\n","model3 = keras.Model(inputs=inputs, outputs=outputs)\n","compile_model(model3)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bK-6X-dpBNaJ","executionInfo":{"status":"ok","timestamp":1641802499943,"user_tz":-60,"elapsed":594,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"c3c2863e-23d3-4214-fc59-e7f16e0301df"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model_3\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input_6 (InputLayer)        [(None, 224, 224, 3)]     0         \n","                                                                 \n"," tf.math.truediv_3 (TFOpLamb  (None, 224, 224, 3)      0         \n"," da)                                                             \n","                                                                 \n"," tf.math.subtract_3 (TFOpLam  (None, 224, 224, 3)      0         \n"," bda)                                                            \n","                                                                 \n"," base (Functional)           (None, 7, 7, 160)         1364864   \n","                                                                 \n"," global_average_pooling2d_2   (None, 160)              0         \n"," (GlobalAveragePooling2D)                                        \n","                                                                 \n"," dropout_2 (Dropout)         (None, 160)               0         \n","                                                                 \n"," dense_5 (Dense)             (None, 1)                 161       \n","                                                                 \n","=================================================================\n","Total params: 1,365,025\n","Trainable params: 161\n","Non-trainable params: 1,364,864\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["run_model(model3, 'experiment9_feature_extraction', epochs = 50, patience=5)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"73lUuyWzBjY5","executionInfo":{"status":"ok","timestamp":1641802816995,"user_tz":-60,"elapsed":317069,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"f896c3fc-9e83-495e-feb6-15514bde2f0e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/50\n","200/200 [==============================] - ETA: 0s - loss: 0.8550 - accuracy: 0.5926\n","Epoch 00001: val_loss improved from inf to 0.54951, saving model to /content/models/experiment8_feature_extraction.h5\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n","  layer_config = serialize_layer_fn(layer)\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r200/200 [==============================] - 28s 123ms/step - loss: 0.8550 - accuracy: 0.5926 - val_loss: 0.5495 - val_accuracy: 0.7162\n","Epoch 2/50\n","200/200 [==============================] - ETA: 0s - loss: 0.6985 - accuracy: 0.6559\n","Epoch 00002: val_loss improved from 0.54951 to 0.52728, saving model to /content/models/experiment8_feature_extraction.h5\n","200/200 [==============================] - 24s 119ms/step - loss: 0.6985 - accuracy: 0.6559 - val_loss: 0.5273 - val_accuracy: 0.7341\n","Epoch 3/50\n","200/200 [==============================] - ETA: 0s - loss: 0.6108 - accuracy: 0.6892\n","Epoch 00003: val_loss did not improve from 0.52728\n","200/200 [==============================] - 24s 117ms/step - loss: 0.6108 - accuracy: 0.6892 - val_loss: 0.5307 - val_accuracy: 0.7317\n","Epoch 4/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5876 - accuracy: 0.7015\n","Epoch 00004: val_loss improved from 0.52728 to 0.51380, saving model to /content/models/experiment8_feature_extraction.h5\n","200/200 [==============================] - 24s 119ms/step - loss: 0.5876 - accuracy: 0.7015 - val_loss: 0.5138 - val_accuracy: 0.7519\n","Epoch 5/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5640 - accuracy: 0.7104\n","Epoch 00005: val_loss did not improve from 0.51380\n","200/200 [==============================] - 24s 117ms/step - loss: 0.5640 - accuracy: 0.7104 - val_loss: 0.5326 - val_accuracy: 0.7369\n","Epoch 6/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5690 - accuracy: 0.7128\n","Epoch 00006: val_loss improved from 0.51380 to 0.50090, saving model to /content/models/experiment8_feature_extraction.h5\n","200/200 [==============================] - 24s 118ms/step - loss: 0.5690 - accuracy: 0.7128 - val_loss: 0.5009 - val_accuracy: 0.7692\n","Epoch 7/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5665 - accuracy: 0.7151\n","Epoch 00007: val_loss did not improve from 0.50090\n","200/200 [==============================] - 24s 118ms/step - loss: 0.5665 - accuracy: 0.7151 - val_loss: 0.5065 - val_accuracy: 0.7645\n","Epoch 8/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5568 - accuracy: 0.7190\n","Epoch 00008: val_loss did not improve from 0.50090\n","200/200 [==============================] - 24s 117ms/step - loss: 0.5568 - accuracy: 0.7190 - val_loss: 0.5254 - val_accuracy: 0.7416\n","Epoch 9/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5668 - accuracy: 0.7139\n","Epoch 00009: val_loss did not improve from 0.50090\n","200/200 [==============================] - 24s 117ms/step - loss: 0.5668 - accuracy: 0.7139 - val_loss: 0.5041 - val_accuracy: 0.7664\n","Epoch 10/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5640 - accuracy: 0.7128\n","Epoch 00010: val_loss did not improve from 0.50090\n","200/200 [==============================] - 24s 117ms/step - loss: 0.5640 - accuracy: 0.7128 - val_loss: 0.5017 - val_accuracy: 0.7725\n","Epoch 11/50\n","200/200 [==============================] - ETA: 0s - loss: 0.5615 - accuracy: 0.7137\n","Epoch 00011: val_loss did not improve from 0.50090\n","200/200 [==============================] - 24s 117ms/step - loss: 0.5615 - accuracy: 0.7137 - val_loss: 0.5130 - val_accuracy: 0.7622\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["evaluate_model(tf.keras.models.load_model(GLOBAL_MODELS_FOLDER + '/experiment9_feature_extraction.h5'))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":730},"id":"zYnYIpGvKtTo","executionInfo":{"status":"ok","timestamp":1642022578309,"user_tz":-60,"elapsed":11287,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"ddd773d6-71ce-40b7-c2c3-99008dbcb58f"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["Classification report: \n","              precision    recall  f1-score   support\n","\n","           0     0.6356    0.6868    0.6602       696\n","           1     0.8421    0.8093    0.8254      1437\n","\n","    accuracy                         0.7693      2133\n","   macro avg     0.7389    0.7481    0.7428      2133\n","weighted avg     0.7748    0.7693    0.7715      2133\n","\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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5VEJKGq3+bxUXk9MA+HZ0K+6oW8bFUSl1WXj4GQIDrUX8FiwYQMeONfHz06f5M9NB6cqhZq0/nJEk9r51lyYJlWecO5fIiBELadDgc9auPQpA7971NElch95RKIc4F5/C3E1HmbxiHwB73ryLQj6eWd5AuZ8FC8J59NFlREcn8vLL7WjZ0rOK+OU2TRQqV6VbDJsORTNkxqaMdfc1q4yvh9bAUe5nxIiFzJmznWbNKvL77w/SpEkFV4eU52miULlq5OwtGcX7ihXyYcfr3fLdw0kq78lcxK9Nm6oEBJTh6afb4OOjre/2cOhVEpG7RGSviBwQkReus72aiKwWkW0iskNEejgyHuU4cclpjP9ua0aSWPBIa0Je6aJJQrnc4cMxdOv2LV9/vR2AsWOb8/zzd2iSyAGHXSkR8QamAHcDgcBgEbm6itZ/gB+NMU2BQcDnjopHOdbby3azbOdJ6+t7G9G8ur/2SSiXSk+38Mknm2jY8As2bozEdlOhboIjm55aAgeMMYcARGQe0BcIz7SPAS7VaigBHHdgPMpBktPSM+aj3vqfLpT2c+/5gZX72737DKNHL2bDhkjuvrsOU6f2olq1Eq4Oy205MlFUBo5lWo4EWl21z+vAChF5DCgKdLnegURkLDAWoFq1arkeqLp5byzZxVd/HwHg9lr+miRUnnDgwDn27o3mm2/u5YEHGmkT6C1ydWf2YGC2MeYDEWkNfCMiDY0xlsw7GWOmA9MBgoOD9QYyD9gReZ4XFuzMeNr60Y61uauB500BqdzH1q3H2b79FKNGNaV373ocPvw4xYvrF5fc4MhEEQVUzbRcxbYus9HAXQDGmA0i4guUAU47MC51k+KT02j65h+kpVuwZErXXzzQjLsbaZJQrpGYmMobb6xh8uR/qFq1BEOGWIv4aZLIPY5MFFuAuiJSE2uCGAQMuWqfCKAzMFtEAgBf4NqJkZXLPflDKL9su5znh7Sqxu21StOncSUXRqXyu7VrjzJmzGL27z/H6NFNmTxZi/g5gsOuqDEmTUQmAMsBb2CWMWaXiEwCQowxi4GngS9F5EmsHdsjjNGxCXlJYko6z/y0nV93nqBWmaK0q1uG13o3wMtL23yVa0VFXaBz56+pWrU4K1cOpXPnWq4OyWOJu30uBwcHm5CQkOx3VLckKTWdH0OO8eqiXRnrpgxpRs8gbWJSrrVz5ykaNSoPwNKl++jYsQZFi2p9puyIyFZjTPDNvFefOFHXNfH7bRlJol3dMhx8u4cmCeVSZ88mMHToLwQFTc0o4ter122aJJxAG/PUFRJS0mj77p/EJKQC8NO41rSo4e/iqFR+Zozhp5/CmTBhGTExSbz2WntatdIifs6kiUJlSEpNJ/DV5RnLc8e00iShXG748IV8880OgoMrsWpVn4xmJ+U8migUYP3WNvabrYC1mF/oa93w1g5r5SKZi/i1b1+doKDyPPHE7VqfyUU0USj+2nuaEV9tyVheOKGtJgnlMocOxfDQQ0t48MFGjBzZlNGjm7k6pHxPE0U+9sOWCPaejGPW34cBaFK1JDOHB2sZDuUS6ekWPv10My+//Cfe3sKwYUGuDknZaKLIp1aGn+L5BTsB8PYSbq/lz3djbndxVCq/Cg8/w6hRi9i0KYqePesydWovqlQpnv0blVNoosinxnxtfRblh7G306pWaRdHo/K7w4djOHgwhrlz72PQoIZaxC+P0USRT6SkWVi+6yRbj8awdMcJAFrW9NckoVxmy5YoQkNP8tBDzenZ8zYOHZpIsWLa7JkXaaLIJzYciuax77ddse7Z7vVcFI3KzxISUnn11dV89NFGqlcvwdChjfH19dEkkYdposgHjDE8/aN1GsivRrSg/W1ltVaTcom//jrCmDGLOXgwhocfbs5773XRIn5uQP8Pebj45DRGfLWZs3HJALSpU1qThHKJyMgLdO36DdWrl+DPP4fRsWNNV4ek7KSJwoPFxKfQ9M0/MpZ/nXiHzmOtnG779pM0blyBKlWKs2jRIDp0qEGRIgVcHZbKAX3M0YNFnU8EoKp/Yfa8eRcNKumcwcp5zpyJZ8iQBTRpMo01a44A0KNHXU0SbkjvKDzUqt2nGD3HOgT21V4N8C2gdxLKOYwxzJsXxsSJvxMbm8Qbb3Sgdeuq2b9R5VmaKDyMMYb3ft/L1DUHAegSUJ5WtbSwn3KeoUN/4bvvdtKqVWVmzuxDgwblXB2SukV2JwoRKWKMSXBkMOrWxCak0njSiozlPo0r8cngpi6MSOUXFotBxFrEr2PHGjRvXpGJE1vh7a2t254g20QhIm2AGYAfUE1EGgMPG2PGOzo4lTMjZm/OeB32Rnf8CukNo3K8AwfO8dBDSxg6NIhRo7SInyeyJ91/BHQHogGMMduBOx0ZlMqZvScvUuOFX9kWcR6Aw+/00CShHC4tzcLkyf/QqNEXbNt2goIFtR/MU9n1aWKMOXZV7ZV0x4Sj7BWXnMZ3G4/yzm97rli/7rmOWidHOVxY2GlGjlxESMhx+vatx+ef96RSpWKuDks5iD2J4pit+cmISAHgcWC3Y8NS2RkzZwsbD50DoEqpwrzWuwFdA3XmL+UcERGxHD16nnnz7mfAgAb65cTD2ZMoxgH/AyoDUcAKQPsnXGj/qYtsPHQOEVg2sR0BFbUcs3K8TZsi2b79FGPHNqdHj7ocOvQ4fn4FXR2WcgJ7+ijqGWMeMMaUN8aUM8Y8CAQ4OjB1ffHJaYz71jpl6Rt9GmiSUA4XH5/CU08tp3Xrmbz//t8kJ6cBaJLIR+xJFJ/auU45wY7IWA6eiQegZ6OKLo5Gebo//zxMUNBUPvpoI+PGBfPvvw9TSAdK5Ds3/D8uIq2BNkBZEXkq06bigA5vcJFLk87PG3u7TlmqHCoy8gLdu39LzZolWbNmBHfeWd3VISkXyeqrQUGsz074AJmHM1wA+jkyKHVjn/y5HwAfrQCrHGTbthM0bVqRKlWKs2TJYNq3r07hwlqfKT+7YaIwxqwB1ojIbGPMUSfGpG7AYjFsORIDQOOqJV0cjfI0p07FMXHi7/z44y7++ms47dvX4K676rg6LJUH2NPYmCAi/wUaAL6XVhpjOjksKnVda/efId1iqF+hGAW0NILKJcYYvvtuJ48//jtxcSm89VZH2rTRIn7qMnsSxXfAD0AvrENlhwNnHBmUsjLGEHrsPImp6ew9eZE3loQD1tFOSuWWIUN+Zt68MFq3rsLMmX0ICCjr6pBUHmNPoihtjJkpIo9nao7a4ujA8rtz8Sk0yzTp0CV3N6xAq1qlXRCR8iSZi/h161aL1q2r8OijLbSIn7ouexJFqu2/J0SkJ3Ac0LrVDmSMuSJJzBgWjJ+vD+WKFaJWWT8XRqY8wb590Tz00BKGDQti9OhmjBypFYZV1uxJFG+JSAngaazPTxQHnnBoVPncjyHHAOvMdGuf1dpNKnekpVn48MMNvPbaX/j6+uhIJmW3bBOFMWap7WUs0BFARNo6Mqj8Ljo+BYAF49poklC5YseOU4watYitW09w7731mTKlBxUrahE/ZZ+sHrjzBgZgrfH0uzEmTER6AS8BhQG9X3WQpFQLAMX1G5/KJZGRFzh27AI//dSf++8P0C8gKkey6rmaCYwBSgOfiMi3wGTgfWOMXUlCRO4Skb0ickBEXrjBPgNEJFxEdonI3Jz+AzxNQkoan6yyPlTnpX/M6hb8888xpk61zptuLeI3kX79AjVJqBzLqukpGAgyxlhExBc4CdQ2xkTbc2DbHckUoCsQCWwRkcXGmPBM+9QFXgTaGmNiRCTfT677y7YoAFrW8Kegj45AUTkXF5fCyy+v4tNPN1O7tj8jRzahUCEfihbVIn7q5mSVKFKMMRYAY0ySiByyN0nYtAQOGGMOAYjIPKAvEJ5pn4eAKcaYGNt5Tucoeg+RnJbO72En+W5TBJsPW+eYmD6suYujUu5oxYqDjB27hIiIWB59tAVvv91Zi/ipW5bVb1B9Edlhey1AbduyAMYYE5TNsSsDxzItRwKtrtrnNgAR+RtrocHXjTG/X30gERkLjAWoVq1aNqd1L19vOMKri3ZdsW7o7dUpof0TKoeOHYulZ8+51K5dirVrR3LHHZ71t6JcJ6tE4Yw5J3yAukAHoAqwVkQaGWPOZ97JGDMdmA4QHBxsnBCXU2yLiMlIEk2qluSDAY2pWbooXlrwT+XA1q3Had68ElWrlmDZsiG0a1cdX1+9i1C5J6uigLdaCDAKyFwwpoptXWaRwCZjTCpwWET2YU0cHv/kd0JKGvd+/g8Ab93TkAdv1xLOKmdOnozjscd+Y/788Iwifl271nZ1WMoDObK3dAtQV0RqikhBYBCw+Kp9FmK9m0BEymBtijrkwJjyjCfmhQJQzNeHB1ppE4GynzGGOXNCCQycwpIle3n77U5axE85lMPuT40xaSIyAViOtf9hljFml4hMAkKMMYtt27qJSDiQDjybww5ztxQWFcuK8FMAbH6piw5XVDkyaNACfvxxF23bVmXGjD7Ur1/G1SEpD2dXohCRwkA1Y8zenBzcGLMMWHbVulczvTbAU7affGPK6gMAPNGlLoUL6mSBKnuZi/j16FGHdu2qMX58C+3PUk6RbdOTiPQGQoHfbctNROTqJiRlp/MJKfwWdhL/ogV5osttrg5HuYE9e85y551fMXPmNgCGD2/ChAktNUkop7Gnj+J1rM9EnAcwxoQCNR0Yk0f7v193AxCgdXZUNlJT03n77XU0bjyV8PAz+PnpA3PKNewqM26Mib2qHd1jhqg6U2q6hZ+2RgLwzairHylR6rLQ0JOMHLmI0NCT9OsXyKef3k2FClpiXrmGPYlil4gMAbxtJTcmAv84NizPExmTwB3vrQYgqEoJbTZQWTp5Mo6TJ+NYsGAA993njEealLoxsfYnZ7GDSBHgZaCbbdVy4C1jTJKDY7uu4OBgExIS4opT35T45DSaTvqDlHRLxro9b96FbwHtxFZXWr8+gh07TjF+fAsAEhJSKVJEn9BXuUNEthpjgm/mvfbcUdQ3xryMNVmoHJqz4UhGkni9dyAj2mr3jrrSxYvJvPjiKqZM2ULduv6MHt2UQoV8NEmoPMOeRPGBiFQA5gM/GGPCHByTx9h94gLv/24dUax3Eep6li8/wNixSzl2LJbHH2/FW2910iJ+Ks+xZ4a7jrZEMQCYJiLFsSaMtxwenZvrO+VvAHoFVdQkoa5x7FgsvXp9T506/qxfP0qfrlZ5ll0lPIwxJ40xnwDjsD5T8Wo2b1FAsUI+NK1Wks+GNHN1KCqPMMawebO15FnVqiX47bcH2LbtYU0SKk+z54G7ABF5XUR2Ap9iHfFUxeGRuanUdAu/bIuk3xf/EB2fwm3l9HkJZXXixEXuv/9HWrWawZo1RwDo0qWWVnpVeZ49v6GzgB+A7saY4w6Ox63FJ6cxcPoGwqIuZKwb0EJzan5njGH27FCeemoFSUlpvPdeF9q21UKQyn3Y00fR2hmBeIJtEeczksTvT7SjXvliWvBPMWDAfObPD6ddu2rMmNGH224r7eqQlMqRGyYKEfnRGDPA1uSU+WELe2e4y3eM7TLNH9ea+hWKuzga5Urp6RZEBC8voXfv2+jUqQYPPxysD1oqt5TVHcXjtv/2ckYg7m7joWiGztzs6jBUHrB79xlGj17MyJFNeOih5gwb1tjVISl1S27YmW2MOWF7Od4YczTzDzDeOeG5h8iYBAZN3wiAf9GCBFbSu4n8KDU1nbfeWkuTJtPYuzeaEiV8XR2SUrnCnuGxXa+z7u7cDsSd9ftiAwAPt6/Fv690pUhBHcWS32zbdoLg4C955ZXV3HtvfXbvfpQBAxq4OiylckVWfRSPYL1zqCUiOzJtKgb87ejA3EXU+UROXrCWvXque30XR6Nc5dSpeM6eTWDhwoH07au/B8qzZPXVdy7wG/AO8EKm9ReNMeccGpUbWbP3DABv9m2At3ZU5itr1x5l585TPPpoS+66qw4HDjxG4cJan0l5nqyanowx5gjwKHAx0w8i4u/40NzDNxuPAtCypg55zC8uXEhm/Phfad9+Np98spnk5DQATRLKY2V3R9EL2Ip1eGzmr8sGqOXAuNzC72En2X3iAsV9fahXQZ/Azg+WLdvPww8v5fjxizz11O1MmtRRi/gpj3fD33BjTC/bf7Uu9nVcTEpl0pJdAHwyuKmLo1HOcOxYLH37zqNevdLMn9+fVoNZaJcAACAASURBVK30qXuVP9hT66mtiBS1vX5QRD4UkXxdf8BiMTR6fQXHY5OoVbYoHeqVc3VIykGMMWzcaJ2+tmrVEqxY8SD//vuwJgmVr9gzPPYLIEFEGgNPAweBbxwaVR4Wl5xG87f+AKCgjxc/PawVTjzV8eMXueeeH2jdemZGEb+OHWtSsKCWjFf5iz2JIs1Y50vtC3xmjJmCdYhsvjRo+gZiElIB2PJSF0r7FXJxRCq3GWOYMeNfAgOnsGLFQSZP7qpF/FS+Zk8v3EUReREYCrQTES8g3w3vOBodz4d/7Mso+nfw7R46HNZD9ev3Ez//vJv27aszY0Yf6tTRQX4qf7MnUQwEhgCjjDEnbf0T/3VsWHnLliPn6D/V+vR1Gb+CvN8vSJOEh8lcxO+ee+rRrVstHnqouRbxUwoQa6tSNjuJlAda2BY3G2NOOzSqLAQHB5uQkBCnnW/7sfMZU5pWKO7L3y900iThYcLCTjNmzGJGj27KQw81d3U4SjmEiGw1xgTfzHvtGfU0ANgM9Mc6b/YmEel3MydzR5/+uR+AF++uz8aXOmuS8CApKem88cZfNGs2jYMHYyhVqrCrQ1IqT7Kn6elloMWluwgRKQusBOY7MrC84IMVe1m523rz1D9Y5zT2JFu3HmfEiEWEhZ1myJBGfPxxd8qWLerqsJTKk+xJFF5XNTVFY99oKbc2Y90hPv3zAADLJrbDv2hBF0ekclN0dCLnzyexZMlgevW6zdXhKJWn2ZMofheR5cD3tuWBwDLHhZQ3/LnHmhs/f6CZzi/hIVavPszOnaeZOLEV3brVZv/+x/D11fIbSmUn2zsDY8yzwDQgyPYz3RjzvKMDywta1ChFj0YVXR2GukWxsUk8/PASOnX6mi++CMko4qdJQin7ZDUfRV1gMlAb2Ak8Y4yJclZgrnTsXAL/HIwmuHopV4eibtGSJXsZN+5XTp6M45lnWvPGG1rET6mcyuovZhbwNbAW6A18CtznjKBc7eWFYQCU0LLRbu3YsVjuv/9H6tcvw8KFA2nRorKrQ1LKLWWVKIoZY760vd4rIv86I6C8oICXUK5YIWYMv6khx8qFjDFs2BBJmzZVbUX8htKmTVWtz6TULciqj8JXRJqKSDMRaQYUvmo5WyJyl4jsFZEDIvJCFvvdLyJGRFz+yRwZk8CqPacpW6wQIvrMhDuJjLxAnz7zaNt2VkYRvw4damiSUOoWZXVHcQL4MNPyyUzLBuiU1YFFxBuYAnQFIoEtIrLYGBN+1X7FgMeBTTkLPfedjUvmjvdWA1CyiDY7uQuLxfDll1t59tk/SEuz8OGH3bjjDi3ip1RuyWrioo63eOyWwAFjzCEAEZmHtQJt+FX7vQm8Bzx7i+e7ZT+FWOcd6N24Eh8OaOziaJS97r//RxYu3EOnTjX58sve1KqlgxCUyk2OfHCuMnAs03KkbV0GWxNWVWPMr1kdSETGikiIiIScOXMm9yO1OXA6DoAX7q5PAW+Pf6bQraWlWbBYrHXK7r8/gC+/7M3KlUM1SSjlAC77NLSVK/8Q62RIWTLGTDfGBBtjgsuWLeuQeIwxLN4eRaUSvlQuqTV/8rIdO07RuvVMvvxyKwAPPhjEmDHNtE9JKQdxZKKIAjIXSKpiW3dJMaAh8JeIHAFuBxa7qkP7zMVkUtMNyWkWV5xe2SE5OY3XXltN8+bTOXr0vNZmUspJsn3ySKxf0x4AahljJtnmo6hgjNmczVu3AHVFpCbWBDEI67wWABhjYoEymc7zF9aH+pxXQzwTWysGz3Sv54rTq2xs2RLFiBGLCA8/w9ChQXz0UXdKly7i6rCUyhfseUT1c8CCdZTTJOAisIDL81NclzEmTUQmAMsBb2CWMWaXiEwCQowxi28p8lz2U4i1O0WriOdNMTFJxMWlsGzZEO6+u66rw1EqX7EnUbQyxjQTkW0AxpgYEbGrlKoxZhlXFRA0xrx6g3072HNMRzl1MQmAzgHlXRmGyuTPPw+zc+cpHn/8drp1q82+fRO0/IZSLmBPH0Wq7ZkIAxnzUXhcQ/6Gg9GUKFyAMn6FXB1Kvnf+fBIPPbSYzp2/Ztq0rRlF/DRJKOUa9iSKT4BfgHIi8n/AeuBth0blZHM3RXDwTDyxiamuDiXfW7RoD4GBU5g1K5TnnmvD1q1jNUEo5WLZ/gUaY74Tka1AZ0CAe4wxux0emZOciE3kpV92AjBliF2VSZSDRETE0r//TwQElGXx4sEEB1dydUhKKewb9VQNSACWZF5njIlwZGDO8sMWayf25P6N6Rmkc084mzGG9esjaNeuOtWqlWDlymHcfnsVrc+kVB5iT9PTr8BS239XAYeA3xwZlDNderq3X/MqLo4k/4mIiKVnz7nceefsjCJ+d95ZXZOEUnmMPU1PjTIv28pujHdYRC6gD/Q6l8VimDo1hOefX4kxhk8+uUuL+CmVh+W4l9AY86+ItHJEMCp/uO++H1i0aC9du9Zi+vTe1KhR0tUhKaWyYE8fxVOZFr2AZsBxh0WkPFJamgUvL8HLSxg4sAF9+9ZjxIgmWp9JKTdgTx9FsUw/hbD2VfR1ZFDOdDw2CWNcHYVn2779JK1azWD6dGsRv8GDGzFyZFNNEkq5iSzvKGwP2hUzxjzjpHicbv7WSFeH4LGSktJ46621vPfe3/j7F6ZCBT9Xh6SUugk3TBQi4mOr19TWmQG5Qo9GFVwdgsfZvDmK4cMXsmfPWYYPb8yHH3bH31/LtyvljrK6o9iMtT8iVEQWAz8B8Zc2GmN+dnBsDndpaGydsvpNN7dduJBMYmIqv//+AN2713F1OEqpW2DPqCdfIBpr9ViD9elsA7h1ojifkELvz9YDUNBHZ7PLDStWHGTXrtM8+WRrunSpxd69WsRPKU+Q1V9xOduIpzAuJ4hL3Lr7N91iaDLpj4zl4W1quC4YDxATk8hTT61g9uxQGjQoy/jxLShUyEeThFIeIqu/ZG/AjysTxCVunSiW7TwBgF8hHza/3JkiBfUD7Wb9/PNuHn10GWfOxPPii3fw6qvtNUEo5WGy+os+YYyZ5LRInCjOVrZ64aNtNUncgoiIWAYNmk/DhuVYtmwITZtqrSylPFFWn5IeOcjdGMOLP1urxRb31SSRU8YY1q49Svv2NahWrQR//jmcVq0qU6CA1mdSylNl1Yvb2WlRONGfe04D1vpOZYvpJEU5cfToee6++zs6dJiTUcTvjjuqaZJQysPd8Cu1MeacMwNxlgtJ1smJfhnfVp8MtpPFYvj88y288MJKAD799G7atavu4qiUUs6S79pePl11AIBSRQq4OBL3cc8981iyZB/du9dm2rReVK+uRfyUyk/yXaK4pGqpIq4OIU9LTU3H29sLLy9h8OCG9OsXyNChQXoXplQ+lO+eNBOBnkEV8fLSD7wb+fffE7RsOYOpU0MAaxG/YcMaa5JQKp/Kd4lC3VhiYiovvriSli2/5OTJOKpWLe7qkJRSeUC+anqyWAyHzsZTv6J+AF5t48ZIhg9fyL590Ywa1YTJk7tRqpQW8VNK5bNEMXdzBMZAaprF1aHkOfHxKaSmpvPHH0Pp0qWWq8NRSuUh+SpRzFp/GICnut3m4kjyht9/P8CuXad5+uk2dO5ciz17JlCwoD4ToZS6Ur7po1i//yyHzsbTsHJx6lfI301P0dEJDB++kLvv/o45c7aTkpIOoElCKXVdHp8ojDHMWn+YB2duAqB/86oujsh1jDHMnx9OYODnzJ27k//8px1btjykCUIplSWPb3r6YcsxJi0NB+CBVtXydUnxiIhYhgxZQFBQeVaseJDGjXVmP6VU9jw6UaSmW3jBVgBw/rjWBNfwd3FEzmeMYfXqI3TqVJPq1Uvy118jaNmyMj46WZNSyk4e/Wnxf7/uBqBptZL5MkkcPhxDt27f0rnz1xlF/Nq0qapJQimVIx57R7Fk+3Fm/3MEgLfvbeTaYJwsPd3CZ59t5qWX/sTbW/jii55axE8pddM8NlGs238GgLkPtSIgnz1g17fvPH79dT89etRl6tSeVK1awtUhKaXcmMcmCkGoUNyXNrXLuDoUp8hcxG/o0CAGD27IkCGNtD6TUuqWObSxWkTuEpG9InJARF64zvanRCRcRHaIyCoR0faRmxAScpzg4C/54ostAAwc2JAHHtBKr0qp3OGwRCEi3sAU4G4gEBgsIoFX7bYNCDbGBAHzgfdz49zpFsMPIcdINyY3DpdnJSam8vzzf9Cq1QzOnInXeSKUUg7hyKanlsABY8whABGZB/QFwi/tYIxZnWn/jcCDuXHi4+cTAShZ2HMnJ9qw4RjDhy9k//5zjBnTlP/+txslS/q6OiyllAdyZKKoDBzLtBwJtMpi/9HAb9fbICJjgbEA1apVy/Kk6/afYejMzQCM71jb/mjdTGJiGhaLYeXKoXTurEX8lFKOkyc6s0XkQSAYaH+97caY6cB0gODg4Czbk9YfOAtA9wbl6RVUKXcDdbFly/aza9dpnn22LZ061WT37kcpUEDLbyilHMuRndlRQObCSlVs664gIl2Al4E+xpjk3DhxIR8vpg0NpoC3ZzxYdvZsAg8++DM9e87lu+92ZhTx0yShlHIGR36SbgHqikhNESkIDAIWZ95BRJoC07AmidMOjMUtGWOYNy+MgIAp/PjjLl57rT2bN2sRP6WUczms6ckYkyYiE4DlgDcwyxizS0QmASHGmMXAfwE/4CfbUM4IY0yfmz2nxWKYtuZQLkSfN0RExDJ8+EIaNy7PzJl9aNSovKtDUkrlQw7tozDGLAOWXbXu1Uyvu+Tm+eZtsfadN6vmvsNEjTGsWnWYLl1qUb16SdasGUGLFpXw9pBmNKWU+/GoT59FodYukC+HBbs4kptz8OA5Onf+mq5dv8ko4nf77VU0SSilXCpPjHrKLZsOnwPAv2hBF0eSM+npFv73v0385z9/UqCAN9Om9dIifkqpPMOjEoUIjGpb0+1KV/Tu/T2//XaAXr1u44svelKlSv4qYqiUyts8KlH4eAmF3GSuhZSUdHx8rEX8RoxowtChQQwa1NDtkpxSyvO5x6eqh9m8OYrmzafz+efWIn4DBjRg8GCt9KqUyps0UThRQkIqTz+9nNatZxITk0jt2qVcHZJSSmXLY5qe3lm2m9R0Q16tF7t+fQTDhy/k0KEYHn64Oe+914USJbSIn1Iq7/OYRDHr78MAdAvMmw+lWScWElavHk6HDjVcHY5SStnNIxLF8fOJpKYbRrWtSdNqeac5Z8mSvezefZbnnmtLx441CQ9/FB836WxXSqlLPOJT64EZmwDwK5Q3aiCdORPPkCEL6NNnHt9/H5ZRxE+ThFLKHXnEJ9eFxFRqly3KIx3quDQOYwxz5+4kIGAK8+eHM2lSBzZtGqNF/JRSbs0jmp5S0i20rFmawi7+QI6IiGXkyEU0bVqBmTP70KBBOZfGo5RSucHtE8XafWe4mGSd7c0VLBbDH38cpHv3OlSvXpJ160bSvHlFrc+klPIYbv9p9vHKfQDc37yK08+9f380nTrN4a67vmPt2qMAtGxZWZOEUsqjuPUnWkqahX8jzgPQpKrzSounpVn473//JihoKqGhJ5k5sw/t2mU9l7dSSrkrt256euTbrQDc06QSBZ04oqhXr7ksX36Qvn3r8fnnPalUqZjTzq3cR2pqKpGRkSQlJbk6FJWP+Pr6UqVKFQoUKJBrx3TbRLH16DlW7bHOnvp6nwYOP19ychoFCnjj5SWMGdOMUaOa0r9/oNZnUjcUGRlJsWLFqFGjhv6eKKcwxhAdHU1kZCQ1a9bMteO6bdNTdFwKYJ2kqGQRx84/sXFjJM2aTWfKlM0A9OsXyIABDfSPX2UpKSmJ0qVL6++JchoRoXTp0rl+F+u2ieKSig6slxQfn8KTT/5OmzYzuXgxmbp1SzvsXMozaZJQzuaI3zm3bXpytHXrjjJ8+EIOHz7P+PHBvPNOF4oXL+TqsJRSyunc/o7CUdLSLBQo4M2aNSOYMqWnJgnllry9vWnSpAkNGzakd+/enD9/PmPbrl276NSpE/Xq1aNu3bq8+eabGHP5eaTffvuN4OBgAgMDadq0KU8//bQr/glZ2rZtG6NHj3Z1GDeUnJzMwIEDqVOnDq1ateLIkSPX3e+jjz6iQYMGNGzYkMGDB2c0HT3wwAPUq1ePhg0bMmrUKFJTUwFYunQpr776qrP+Ge6bKGISUnL9mAsX7uGdd9YB0LFjTXbtGs+dd+rc1cp9FS5cmNDQUMLCwvD392fKlCkAJCYm0qdPH1544QX27t3L9u3b+eeff/j8888BCAsLY8KECXz77beEh4cTEhJCnTq5WyInLS3tlo/x9ttvM3HiRKeeMydmzpxJqVKlOHDgAE8++STPP//8NftERUXxySefEBISQlhYGOnp6cybNw+wJoo9e/awc+dOEhMTmTFjBgA9e/ZkyZIlJCQkOOXf4bZNT9PWHAKgaKFb/yecOhXHY4/9xk8/hdOsWUWefroNBQt6axE/lWveWLKL8OMXcvWYgZWK81pv+0f8tW7dmh07dgAwd+5c2rZtS7du3QAoUqQIn332GR06dODRRx/l/fff5+WXX6Z+/fqA9c7kkUceueaYcXFxPPbYY4SEhCAivPbaa9x///34+fkRFxcHwPz581m6dCmzZ89mxIgR+Pr6sm3bNtq2bcvPP/9MaGgoJUtan4OqW7cu69evx8vLi3HjxhEREQHAxx9/TNu2ba8498WLF9mxYweNGzcGYPPmzTz++OMkJSVRuHBhvvrqK+rVq8fs2bP5+eefiYuLIz09nWXLlvHYY48RFhZGamoqr7/+On379uXIkSMMHTqU+Ph4AD777DPatGlj9/W9nkWLFvH6668D0K9fPyZMmIAx5pp+hLS0NBITEylQoAAJCQlUqlQJgB49emTs07JlSyIjIwFrP0SHDh1YunQpAwYMuKUY7eG2icLP14eKJXypWaboTR/DGMO33+7giSeWExeXwv/9XyeefbYNBQpoET/lWdLT01m1alVGM82uXbto3rz5FfvUrl2buLg4Lly4QFhYmF1NTW+++SYlSpRg586dAMTExGT7nsjISP755x+8vb1JT0/nl19+YeTIkWzatInq1atTvnx5hgwZwpNPPskdd9xBREQE3bt3Z/fu3VccJyQkhIYNG2Ys169fn3Xr1uHj48PKlSt56aWXWLBgAQD//vsvO3bswN/fn5deeolOnToxa9Yszp8/T8uWLenSpQvlypXjjz/+wNfXl/379zN48GBCQkKuib9du3ZcvHjxmvWTJ0+mS5cuV6yLioqiatWqAPj4+FCiRAmio6MpU6ZMxj6VK1fmmWeeoVq1ahQuXJhu3bplJPBLUlNT+eabb/jf//6XsS44OJh169ZposiKAPUq3NqDbhERsYwZs4Tg4ErMnNmH+vXLZP8mpW5CTr7556bExESaNGlCVFQUAQEBdO3aNVePv3LlyoxmEoBSpbKfD6Z///54e1u/jA0cOJBJkyYxcuRI5s2bx8CBAzOOGx4envGeCxcuEBcXh5+fX8a6EydOULZs2Yzl2NhYhg8fzv79+xGRjPZ8gK5du+Lv7w/AihUrWLx4MZMnTwasw5gjIiKoVKkSEyZMIDQ0FG9vb/bt23fd+NetW5ftvzEnYmJiWLRoEYcPH6ZkyZL079+fb7/9lgcffDBjn/Hjx3PnnXfSrl27jHXlypXj+PHjuRrLjeS7thWLxfDbb/sBqF69JH//PYq1a0doklAe6VIfxdGjRzHGZPRRBAYGsnXr1iv2PXToEH5+fhQvXpwGDRpcsz0nMjetXD2mv2jRy60ArVu35sCBA5w5c4aFCxdy3333AWCxWNi4cSOhoaGEhoYSFRV1RZK49G/LfOxXXnmFjh07EhYWxpIlS67YlvmcxhgWLFiQceyIiAgCAgL46KOPKF++PNu3byckJISUlOv3g7Zr144mTZpc87Ny5cpr9q1cuTLHjh0DrM1LsbGxlC595TD7lStXUrNmTcqWLUuBAgW47777+OeffzK2v/HGG5w5c4YPP/zwivddamJzhnyVKPbti6ZDh9n06DGXNWuOABAcXEmL+CmPV6RIET755BM++OAD0tLSeOCBB1i/fn3Gh1tiYiITJ07kueeeA+DZZ5/l7bffzvhWbbFYmDp16jXH7dq1a0bygctNT+XLl2f37t1YLBZ++eWXG8YlItx777089dRTBAQEZHyIduvWjU8//TRjv9DQ0GveGxAQwIEDBzKWY2NjqVy5MgCzZ8++4Tm7d+/Op59+mjHCa9u2bRnvr1ixIl5eXnzzzTekp6df9/3r1q3LSDKZf65udgLo06cPc+bMAax9NZ06dbqmf6JatWps3LiRhIQEjDGsWrWKgIAAAGbMmMHy5cv5/vvv8fK68nNq3759VzS9OVK++IRMS7Pw3nvrCQr6gp07T/PVV311NJPKd5o2bUpQUBDff/89hQsXZtGiRbz11lvUq1ePRo0a0aJFCyZMmABAUFAQH3/8MYMHDyYgIICGDRty6NCha475n//8h5iYGBo2bEjjxo1ZvXo1AO+++y69evWiTZs2VKxYMcu4Bg4cyLfffpvR7ARkjAIKCgoiMDDwukmqfv36xMbGZvQXPPfcc7z44os0bdo0y9FNr7zyCqmpqQQFBdGgQQNeeeUVwNq8M2fOHBo3bsyePXuuuAu5WaNHjyY6Opo6derw4Ycf8u677wJw/PjxjI7qVq1a0a9fP5o1a0ajRo2wWCyMHTsWgHHjxnHq1Clat25NkyZNmDRpUsaxV69eTc+ePW85RntI5nHT7iA4ONiEhITQ97P1lCpakNkjW2b7nu7dv2XFioPcd18AU6b0oEIFv2zfo9St2r17d8Y3Q+UYH330EcWKFWPMmDGuDsWpTp06xZAhQ1i1atV1t1/vd09Ethpjgm/mfG55R7Ey/BTbI2Oz3CcpKY30dAsAY8c2Y/78/ixYMECThFIe5JFHHqFQofz3MGxERAQffPCB087nlqOepq45CEDjKtefg+LvvyMYPXox48e3YOLEVtx/f6Azw1NKOYmvry9Dhw51dRhO16JFC6eez+3uKOKT0wg5GkOrmv482fW2K7bFxaUwceJvtGv3FUlJaQQE6Egm5Vru1rSr3J8jfufc7o7i0Nl4KgKNKpe4Yv2aNUcYPnwhERGxTJjQkrff7oyfn2PLjyuVFV9fX6Kjo7XUuHKaS/NR+PrmblVtt0sUAMNaV+c/va5tTipSpADr1o2kbVudllS5XpUqVYiMjOTMmTOuDkXlI5dmuMtNbjfqqVDFumbpqvV0DSzPzz/vZs+es7z0kvVpxfR0iz4ToZRS15FnRz2JyF0isldEDojIC9fZXkhEfrBt3yQiNew5riU+lX79fuT++3/kl1/2kJJifTBGk4RSSuU+hzU9iYg3MAXoCkQCW0RksTEmPNNuo4EYY0wdERkEvAcMvPZomSSlM6jbdyQmpvLOO515+unWWsRPKaUcyJFfwVsCB4wxh4wxKcA8oO9V+/QF5thezwc6Sza9finnk2nYsBzbt4/jhRfu0CShlFIO5sjO7MrAsUzLkUCrG+1jjEkTkVigNHA2804iMhYYa1tMXr9+VJitTH5+V4arrlU+ptfiMr0Wl+m1uKzezb7RLUY9GWOmA9MBRCTkZjtkPI1ei8v0Wlym1+IyvRaXici1k2vYyZFNT1FA1UzLVWzrrruPiPgAJYBoB8aklFIqhxyZKLYAdUWkpogUBAYBi6/aZzEw3Pa6H/Cncbfxukop5eEc1vRk63OYACwHvIFZxphdIjIJCDHGLAZmAt+IyAHgHNZkkp3pjorZDem1uEyvxWV6LS7Ta3HZTV8Lt3vgTimllHPpE2pKKaWypIlCKaVUlvJsonBU+Q93ZMe1eEpEwkVkh4isEhGPnec1u2uRab/7RcSIiMcOjbTnWojIANvvxi4RmevsGJ3Fjr+RaiKyWkS22f5OergiTkcTkVkiclpEwm6wXUTkE9t12iEizew6sDEmz/1g7fw+CNQCCgLbgcCr9hkPTLW9HgT84Oq4XXgtOgJFbK8fyc/XwrZfMWAtsBEIdnXcLvy9qAtsA0rZlsu5Om4XXovpwCO214HAEVfH7aBrcSfQDAi7wfYewG+AALcDm+w5bl69o3BI+Q83le21MMasNsYk2BY3Yn1mxRPZ83sB8CbWumFJzgzOyey5Fg8BU4wxMQDGmNNOjtFZ7LkWBihue10COO7E+JzGGLMW6wjSG+kLfG2sNgIlRaRidsfNq4nieuU/Kt9oH2NMGnCp/IensedaZDYa6zcGT5TttbDdSlc1xvzqzMBcwJ7fi9uA20TkbxHZKCJ3OS0657LnWrwOPCgikcAy4DHnhJbn5PTzBHCTEh7KPiLyIBAMtHd1LK4gIl7Ah8AIF4eSV/hgbX7qgPUuc62INDLGnHdpVK4xGJhtjPlARFpjfX6roTHG4urA3EFevaPQ8h+X2XMtEJEuwMtAH2NMspNic7bsrkUxoCHwl4gcwdoGu9hDO7Tt+b2IBBYbY1KNMYeBfVgTh6ex51qMBn4EMMZsAHyxFgzMb+z6PLlaXk0UWv7jsmyvhYg0BaZhTRKe2g4N2VwLY0ysMaaMMaaGMaYG1v6aPsaYmy6GlofZ8zeyEOvdBCJSBmtT1CFnBukk9lyLCKAzgIgEYE0U+XGO2sXAMNvop9uBWGPMiezelCebnozjyn+4HTuvxX8BP+AnW39+hDGmj8uCdhA7r0W+YOe1WA50E5FwIB141hjjcXfddl6Lp4EvReRJrB3bIzzxi6WIfI/1y0EZW3/Ma0ABAGPMVKz9Mz2AA0ACMNKu43rgtVJKKZWL8mrTk1JKqTxCE4VSSqksaaJQSimVJU0USimlsqSJQimlVJY0Uag8SUTSRSQ000+NLPaNy4XzzRaRw7Zz/Wt7ejenx5ghAAeJ0wAABCNJREFUIoG21y9dte2fW43RdpxL1yVMRJaISMls9m/iqZVSlfPo8FiVJ4lInDHGL7f3zeIYs4Glxpj5ItINmGyMCbqF491yTNkdV0TmAPuMMf+Xxf4jsFbQnZDbsaj8Q+8olFsQET/bXBv/ishOEbmmaqyIVBSRtZm+cbezre8mIhts7/1JRLL7AF8L1LG99ynbscJE5AnbuqIi8quIbLetH2hb/5eIBIvIu0BhWxzf2bbF2f47T0R6Zop5toj0ExFvEfmviGyxzRPwsB2XZQO2gm4i0tL2b9wmIv+ISD3bU8qTgIG2WAbaYp8lIptt+16v+q5SV3J1/XT90Z/r/WB9kjjU9vML1ioCxW3bymB9svTSHXGc7b9PAy/bXntjrf1UBusHf1Hb+ueBV69zvtlAP9vr/vx/e/cX2lUZx3H8/YE2nQPnjQReCF00IigW605KxVAxiEQhwohBEITMm+xuEMhQVBQMr1KkgRHR3zudJg5HUFOmbmrpjbdZF11YThj49eL7HDrY2ennVQ4+L/ixc8bz/M5zGPy+53me375f+BkYBGaBXvI/368DLwHbgGO1vn3l5wSl/kU1plqbaoxbgbFy3E1m8uwB3gdGyu+XAJeAZxrG+Vft/r4CNpfz5cBT5fg14JtyPAQcrfXfC7xTjleQ+Z96/++/t19P9uuJTOFhBsxFxEB1IqkL2CvpVeAB+ST9NPBbrc9F4ERp+31EXJG0lixU82NJb9JNPok3OShphMwB9B6ZG+i7iPi7jOFb4BXgNHBI0n5yuWryMe7rFHBE0hJgM3AhIubKcteLkraXdn1kAr/bj/TvkXSl3P8vwNla+zFJz5IpKroWuP5G4A1Ju8v5UmB1eS+zRg4UtljsAFYCgxExr8wOu7TeICIulEDyOvCZpMPAn8DZiHi7g2t8FBFfVyeSNjQ1iohbyroXW4BRSeciYk8nNxER9yVNAJuAt8giO5AVx4YjYvw/3mIuIgYkLSNzG+0EPiGLNZ2PiK1l439igf4CtkXEzU7Gawbeo7DFow/4vQSJ9cC/6oIra4XfiYhjwHGyJORPwBpJ1Z5Dr6T+Dq85CbwpaZmkXnLZaFLSKuBeRJwkEzI21R2eLzObJl+Sydiq2Qnkh/4HVR9J/eWajSIrGu4CPtQ/afardNFDtaZ3ySW4yjgwrDK9UmYeNmvlQGGLxefAy5JmgXeBXxvarAOuSrpMPq0fiYg/yA/OLyTNkMtOz3VywYiYJvcupsg9i+MRcRl4AZgqS0AfA6MN3T8FZqrN7EecIYtL/RBZuhMysN0ApiVdI9PGt874y1hmyKI8B4B95d7r/c4Dz1eb2eTMo6uM7Xo5N2vlr8eamVkrzyjMzKyVA4WZmbVyoDAzs1YOFGZm1sqBwszMWjlQmJlZKwcKMzNr9RB097AP9Br/swAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["#### Experiment 10"],"metadata":{"id":"BT6hWekXSJVs"}},{"cell_type":"code","source":["inputs = keras.Input(shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3))\n","x = tf.keras.applications.mobilenet.preprocess_input(inputs)\n","x = base(x) # feature extraction\n","x = layers.Flatten()(x)\n","x = layers.Dense(256)(x)\n","x = layers.Dropout(0.5)(x)\n","outputs = layers.Dense(1, activation='sigmoid')(x)\n","model2 = keras.Model(inputs=inputs, outputs=outputs)\n","compile_model(model2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1641806431094,"user_tz":-60,"elapsed":634,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"453a8797-69e3-40e7-c659-5de164012baf","id":"STMNmhdaSJVw"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model_5\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input_8 (InputLayer)        [(None, 224, 224, 3)]     0         \n","                                                                 \n"," tf.math.truediv_5 (TFOpLamb  (None, 224, 224, 3)      0         \n"," da)                                                             \n","                                                                 \n"," tf.math.subtract_5 (TFOpLam  (None, 224, 224, 3)      0         \n"," bda)                                                            \n","                                                                 \n"," base (Functional)           (None, 7, 7, 160)         1364864   \n","                                                                 \n"," flatten_1 (Flatten)         (None, 7840)              0         \n","                                                                 \n"," dense_8 (Dense)             (None, 256)               2007296   \n","                                                                 \n"," dropout_4 (Dropout)         (None, 256)               0         \n","                                                                 \n"," dense_9 (Dense)             (None, 1)                 257       \n","                                                                 \n","=================================================================\n","Total params: 3,372,417\n","Trainable params: 2,007,553\n","Non-trainable params: 1,364,864\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["run_model(model2, 'experiment9_feature_extraction', epochs = 50, patience=5)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1641806885279,"user_tz":-60,"elapsed":442619,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"2ed76469-7802-4aac-e740-c87989328587","id":"NJG701UuSJVy"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/50\n","200/200 [==============================] - ETA: 0s - loss: 12.8050 - accuracy: 0.6731\n","Epoch 00001: val_loss improved from inf to 5.60214, saving model to /content/models/experiment9_feature_extraction.h5\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n","  layer_config = serialize_layer_fn(layer)\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r200/200 [==============================] - 28s 124ms/step - loss: 12.8050 - accuracy: 0.6731 - val_loss: 5.6021 - val_accuracy: 0.7317\n","Epoch 2/50\n","200/200 [==============================] - ETA: 0s - loss: 6.2029 - accuracy: 0.7259\n","Epoch 00002: val_loss improved from 5.60214 to 3.44431, saving model to /content/models/experiment9_feature_extraction.h5\n","200/200 [==============================] - 24s 120ms/step - loss: 6.2029 - accuracy: 0.7259 - val_loss: 3.4443 - val_accuracy: 0.7233\n","Epoch 3/50\n","200/200 [==============================] - ETA: 0s - loss: 3.0295 - accuracy: 0.7534\n","Epoch 00003: val_loss improved from 3.44431 to 1.83257, saving model to /content/models/experiment9_feature_extraction.h5\n","200/200 [==============================] - 24s 119ms/step - loss: 3.0295 - accuracy: 0.7534 - val_loss: 1.8326 - val_accuracy: 0.7458\n","Epoch 4/50\n","200/200 [==============================] - ETA: 0s - loss: 1.4324 - accuracy: 0.7881\n","Epoch 00004: val_loss improved from 1.83257 to 0.85339, saving model to /content/models/experiment9_feature_extraction.h5\n","200/200 [==============================] - 24s 120ms/step - loss: 1.4324 - accuracy: 0.7881 - val_loss: 0.8534 - val_accuracy: 0.8218\n","Epoch 5/50\n","200/200 [==============================] - ETA: 0s - loss: 0.6933 - accuracy: 0.8121\n","Epoch 00005: val_loss improved from 0.85339 to 0.55833, saving model to /content/models/experiment9_feature_extraction.h5\n","200/200 [==============================] - 24s 120ms/step - loss: 0.6933 - accuracy: 0.8121 - val_loss: 0.5583 - val_accuracy: 0.8147\n","Epoch 6/50\n","200/200 [==============================] - ETA: 0s - loss: 0.4395 - accuracy: 0.8296\n","Epoch 00006: val_loss improved from 0.55833 to 0.41966, saving model to /content/models/experiment9_feature_extraction.h5\n","200/200 [==============================] - 24s 119ms/step - loss: 0.4395 - accuracy: 0.8296 - val_loss: 0.4197 - val_accuracy: 0.8246\n","Epoch 7/50\n","200/200 [==============================] - ETA: 0s - loss: 0.3754 - accuracy: 0.8382\n","Epoch 00007: val_loss did not improve from 0.41966\n","200/200 [==============================] - 24s 119ms/step - loss: 0.3754 - accuracy: 0.8382 - val_loss: 0.4506 - val_accuracy: 0.8049\n","Epoch 8/50\n","200/200 [==============================] - ETA: 0s - loss: 0.3716 - accuracy: 0.8401\n","Epoch 00008: val_loss did not improve from 0.41966\n","200/200 [==============================] - 24s 117ms/step - loss: 0.3716 - accuracy: 0.8401 - val_loss: 0.4761 - val_accuracy: 0.7894\n","Epoch 9/50\n","200/200 [==============================] - ETA: 0s - loss: 0.3482 - accuracy: 0.8463\n","Epoch 00009: val_loss improved from 0.41966 to 0.41552, saving model to /content/models/experiment9_feature_extraction.h5\n","200/200 [==============================] - 24s 120ms/step - loss: 0.3482 - accuracy: 0.8463 - val_loss: 0.4155 - val_accuracy: 0.8279\n","Epoch 10/50\n","200/200 [==============================] - ETA: 0s - loss: 0.3546 - accuracy: 0.8458\n","Epoch 00010: val_loss did not improve from 0.41552\n","200/200 [==============================] - 24s 119ms/step - loss: 0.3546 - accuracy: 0.8458 - val_loss: 0.4812 - val_accuracy: 0.7941\n","Epoch 11/50\n","200/200 [==============================] - ETA: 0s - loss: 0.3546 - accuracy: 0.8487\n","Epoch 00011: val_loss did not improve from 0.41552\n","200/200 [==============================] - 24s 118ms/step - loss: 0.3546 - accuracy: 0.8487 - val_loss: 0.4473 - val_accuracy: 0.8114\n","Epoch 12/50\n","200/200 [==============================] - ETA: 0s - loss: 0.3707 - accuracy: 0.8379\n","Epoch 00012: val_loss did not improve from 0.41552\n","200/200 [==============================] - 24s 118ms/step - loss: 0.3707 - accuracy: 0.8379 - val_loss: 0.5971 - val_accuracy: 0.7472\n","Epoch 13/50\n","200/200 [==============================] - ETA: 0s - loss: 0.3634 - accuracy: 0.8424\n","Epoch 00013: val_loss did not improve from 0.41552\n","200/200 [==============================] - 24s 118ms/step - loss: 0.3634 - accuracy: 0.8424 - val_loss: 0.5170 - val_accuracy: 0.7763\n","Epoch 14/50\n","200/200 [==============================] - ETA: 0s - loss: 0.3735 - accuracy: 0.8401\n","Epoch 00014: val_loss did not improve from 0.41552\n","200/200 [==============================] - 24s 118ms/step - loss: 0.3735 - accuracy: 0.8401 - val_loss: 0.5545 - val_accuracy: 0.7645\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 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PF154gZ49e3LBBRdwxx13cOWVVwLQpUsXli9fzu23386sWbO46667Kn19qRx2VmdiikidK9uNUrb75M9//jMDBw5kwIABrFq1ap/ujvJeeuklzjrrLNLT02nfvj2jR48u/dnKlSs5/vjj6du3L/n5+axatarKet5++2169OhBz549ARg/fjyLFy8u/fnYsWMBGDRoUOkAWJVZsmQJ559/PlDxsLNz5sxh8+bNtGjRgsGDBzN//nxmzJjBm2++Sbt27apcd3Ua/HjgIpI8VbWU69KYMWOYMmUKy5cvZ8eOHQwaNIj333+fWbNm8eqrr3LggQeSl5dX6TCy1cnLy+ORRx6hf//+3HPPPSxatCihekuGpE1kONqpU6dyxhln8OSTTzJs2DCeeeaZ0mFnn3jiCfLy8rjqqqu44IILal2nWuAiUufatm3LiSeeyIQJE0pb31u3bqVNmzZ06NCBTZs28dRTlZ5OAsDw4cN55JFH+OKLL9i2bRuPPfZY6c+2bdvGoYceyq5du0qHgAVo164d27Zt229dvXr1Yu3atbz77rsA/OlPf+I73/lOrV5bKoedVQtcROrFuHHjOOuss0q7UkqGX+3duzeHH344w4YNq3L5gQMH8v3vf5/+/ftz0EEHMXjw4NKf/eIXv2DIkCFkZGQwZMiQ0tA+77zzuPjii5kzZ07pzkuA1q1bM3/+fM4991x2797N4MGDmTRpUq1eV8m1Ovv160d6evo+w86++OKLNGvWjKOPPpqRI0eyYMECbrnlFtLS0mjbtm3CF37QcLIijZyGk40WDScrItIEKMBFRCJKAS7SBGi0i2io6e9JAS7SyLVu3Zri4mKFeAPn7hQXF9O6deu4l9FRKCKNXGZmJoWFhWg454avdevWZGbGf3kFBbhII5eWlkaPHj1SXYbUAXWhiIhElAJcRCSiFOAiIhGlABcRiSgFuIhIRCnARUQiSgEuIhJRCnARkYhSgIuIRJQCXEQkohTgIiIRpQAXEYmoagPczO42s4/NbGWZaZ3M7Dkzeyd2f2DdlikiIuXF0wK/BxhRbtpU4AV3PxJ4IfZcRETqUbUB7u6LgU/LTR4D3Bt7fC9wZpLrEhGRatS2D/xgd98Qe7wROLiyGc1sopkVmFmBBpQXEUmehHdierhOU6XXanL3ue6e4+45GRkZiW5ORERiahvgm8zsUIDY/cfJK0lEROJR2wB/FBgfezwe+FtyyhERkXjFcxjhA8A/gF5mVmhmPwJ+BZxqZu8Ap8Sei4hIPar2osbuPq6SH52c5FpERKQGdCamiEhEKcBFRCJKAS4iElEKcBGRiFKAi4hElAJcRCSiFOAiIhGlABcRiSgFuIhIRCnARUQiSgEuIhJRCnARkYhSgIuIRJQCXEQkohTgIiIRpQAXEYkoBbiISEQpwEVEIkoBLiISUQpwEZGIUoCLiESUAlxEJKISCnAzm2Jmq8xspZk9YGatk1WYiIhUrdYBbmZdgcuBHHfvAzQHzktWYSIiUrVEu1BaAAeYWQsgHfgo8ZJERCQetQ5wd/8QmAWsBzYAW9z92fLzmdlEMysws4KioqLaVyoiIvtIpAvlQGAM0AM4DGhjZj8sP5+7z3X3HHfPycjIqH2lIiKyj0S6UE4B3nf3InffBfwVGJqcskREpDqJBPh64FgzSzczA04GVienLBERqU4ifeCvAA8By4E3Y+uam6S6RESkGi0SWdjdpwPTk1SLiIjUgM7EFBGJKAW4iEhEKcBFRCJKAS4iElEKcBGRiFKAi4hElAJcRCSiFOAiIhGlABcRiSgFuIhIRCnARUQiSgEuIhJRCnARkYhSgIuIRJQCXEQkohTgIiIRpQAXEYkoBbiISEQpwEVEIkoBLiISUU0+wPPzISsLmjUL9/n5qa5IRCQ+CV2VPury82HiRNixIzxfty48B8jNTV1dIiLxaNIt8GnT9oZ3iR07wnQRkYYuoQA3s45m9pCZrTGz1WZ2XLIKqw/r19dsuohIQ5JoC/w3wNPu3hvoD6xOvKT6061bzaaLiDQktQ5wM+sADAfmAbj7TnffnKzC6sPMmZCevu+09PQwXUSkoUukBd4DKALmm9lrZnaXmbUpP5OZTTSzAjMrKCoqSmBzyZebC3PnQvfuYBbu587VDkwRiQZz99otaJYD/BMY5u6vmNlvgK3ufkNly+Tk5HhBQUHtKhURaaLMbJm755SfnkgLvBAodPdXYs8fAgYmsD4REamBWge4u28EPjCzXrFJJwNvJaUqERGpVqIn8lwG5JtZS+A/wIWJlyQiIvFIKMDdfQWwX79Msm3fDm++CcdF6ihzEZG6FYkzMS+5BEaOhA8+SHUlIiINRyQC/KabYPdu+NGPoJYHzYiINDqRCPBvfANmzYLnnoM77kh1NSIiDUMkAhxCN8ppp8E118C776a6GhGR1ItMgJvBvHnQsiWMHw979qS6IhGR1IpMgAN07Qq33QZLl4YuFRGRpixSAQ7wgx/A2WfDjTeGQwtFRJqqyAW4WdiR2bEjnH8+7NyZ6opERFIjcgEOkJEBd94Jr78eDjEUEWmKIhngAKNHQ14e/Pd/wyuvVDu7iEijE9kAB5g9GzIz4YIL9r+2pYhIYxfpAO/QAebPh3//G667LtXViIjUr0gHOMBJJ8Fll8GcObBwYaqrERGpP5EPcIBf/Qp69oQLL4QtW1JdjYhI/WgUAZ6eDvfeC4WFMGVKqqsREakfjSLAAY49FqZODX3ijz6a6mpEROpeowlwgOnToX9/uPhiKCpKdTUiInWrUQV4y5bwxz/CZ5/B5MkaO1xEGrdGFeAA/frBL34BDz8M99+f6mpEROpOowtwgJ/+FIYOhZ/8BD78MNXViIjUjUYZ4M2bh6NSdu7UZdhEpPFqlAEO8M1vwi23wDPPwB/+kOpqRESSr9EGOIQdmaeeGrpU3nsv1dWIiCRXwgFuZs3N7DUzezwZBSWTGdx9N7RoocuwiUjjk4wW+BXA6iSsp05kZsJvfwsvvwy33prqakREkiehADezTOAM4K7klFM3fvhDOOss+PnPYeXKVFcjIpIcibbAZwPXAl9XNoOZTTSzAjMrKErR6ZFmYUdmhw5h7HBdhk1EGoNaB7iZjQI+dvdlVc3n7nPdPcfdczIyMmq7uYRlZMDcufDaa3DzzSkrQ0QkaRJpgQ8DRpvZWmABcJKZ3ZeUqurImWeGFvgvfwmvvprqakREElPrAHf369w9092zgPOAhe7+w6RVVkd+8xs49NAQ5F98kepqRERqr1EfB16Rjh3DkLNr1sD116e6GhGR2ktKgLv7IncflYx11YdTToFLLw0XRV60KNXViIjUTpNrgZf49a/D6fZ5ebB1a6qrERGpuSYb4G3ahLHDP/gArroq1dWIiNRckw1wgOOOg2uvhXnzNHa4iERPkw5wgBkzYPjwMFbKM8+kuhoRkfg1+QBv1SpcBPnoo+Hss+GVV1JdkYhIfJp8gEM4xf7pp+Hgg+H002F1gx2aS0RkLwV4zCGHwLPPQloafPe7YedmovLzISsLmjUL9/n5ia9TRKSEAryMb3wjtMS3bg0h/skntV9Xfj5MnAjr1oVLuq1bF54rxEUkWRTg5WRnw2OPwfvvwxlnwPbttVvPtGmwY8e+03bsCNNFRJJBAV6B4cPhwQehoCDs2KzN8LPr19dsuohITSnAKzFmDNx5Z+gXHz8evq50xPOKdetWs+kiIjWlAK/ChAnhlPsFC+CKK0JfdrxmzoT09H2npaeH6SIiydAi1QU0dNdcAx9/DP/7v3DQQXDDDfEtl5sb7qdNC90m3bqF8C6ZLiKSKAV4Nczgf/4nHJFy443QpQtMnhzfsrm5CmwRqTsK8Dg0axb6w4uLwzC0XbrAueemuioRaerUBx6ntLRwZMqwYaFV/fzzqa5IRJo6BXgNpKeHcVN694azzgqHGYqIpIoCvIYOPDCcrdmlC4wcCW+/neqKRKSpUoDXwmGHhePDmzULp9wXFqa6IhFpihTgtXTkkfDUU/DZZ3DaafDpp6muSESaGgV4AgYOhL/9Dd59F0aNgs8/T3VFItKUKMATdOKJ8MAD4UIQ554Lu3aluiIRaSoU4Ekwdiz8/vehS+XCC2s+boqISG3U+kQeMzsc+CNwMODAXHf/TbIKi5qLL4aionDqfJcu8H//F87iFBGpK4mcibkbuNrdl5tZO2CZmT3n7m8lqbbIue66EOKzZ4dxU66/PtUViUhjVusAd/cNwIbY421mthroCjTZADcLg16VtMQzMkLLXESkLiRlLBQzywIGAPtd093MJgITAbo1gcGwmzWD+fPDYYWTJkHnzqGPXEQk2RLeiWlmbYGHgSvdfWv5n7v7XHfPcfecjIyMRDcXCWlp8Je/wJAhMG4cPPdcqisSkcYooQA3szRCeOe7+1+TU1Lj0KYNPP54OOFnxIgwrvgXX6S6KhFpTGod4GZmwDxgtbvfmrySGo9OnWDpUrjoIpg1K1wweenSVFclIo1FIi3wYcD5wElmtiJ2Oz1JdTUa7dvDH/4QulG++gq+/W24+ur9r1gvIlJTtQ5wd1/i7ubu/dw9O3Z7MpnFNSannAJvvhl2bN56a2iNL1mS6qpEJMp0JmY9atcObr8dXnghnHI/fDhMmVK71nh+PmRlhaNesrLCcxFpWhTgKXDSSaE1/uMfh5N++vWDxYvjXz4/HyZOhHXrwD3cT5yoEBdpahTgKdK2Ldx2G7z4Yhg75Tvfgcsvj29Ew2nT9m+179gRpotI06EAT7ETTgit8csug9/+NrTGFy2qepn162s2XUQaJwV4A9CmDcyZA3//ezgd/8QT4Sc/ge3bK56/shNam8CJriJShgK8ARk+HF5/Ha64Iuzs7NsXFi7cf76ZM8MFlstKTw/TRaTpUIA3MG3ahB2bixeHU/JPPjns7Ny2be88ubkwdy507x5a7N27h+e5uamrW0Tqn7l7vW0sJyfHCwoK6m17UbdjB9xwQxhbvFs3mDcvBLqINC1mtszdc8pPVwu8AUtPD8PTLlkCrVqFk4EmTYKt+w0ZJiJNkQI8AoYOhRUr4Kc/hTvvhD594NlnU12ViKSaAjwiDjgAbrkFXn459JOfdlq4WMSWLamuTERSRQEeMcceC6+9Bj/7Gdx9N/TqFcYcnzUrHD+erEDXqfoiDZ92YkbYv/4Fv/41vPoqfPDB3ulHHgmDBkFOTrgfODCMihivklP1y57tmZ6uI11EUqWynZgK8EaiqAiWLQu3goJwXz7USwK9ulDPygrjq5TXvTusXVsX1YtIVRTgTVB1od6z595ALxvqzZqFQbLKMwvjtohI/aoswJNyUWNpmDIywuXcRozYO618qC9ZAg88sPfnPXuGHaYVDXGbrFP18/PDwFvr14d1zpyprhkJjYatW+Gjjyq+bdwILVqERkZVt3bt9p92wAGhAVJfvv4adu4MF3Epue/cOdSRTArwJiaeUC8urjjA9+yBU0+FQw6p/NapU9X/KOX710uGwoXkhbg+IBqezz+vPJjL3ir6u2vfHg47LPx97dkT/ma2bg23LVtg9+7qt9+8efXB37LlvoGbyH1FNT39dDh6LJnUhSIVuuMOmDEDPv4YOnQI/eft24dW0MaNsGEDfPnl/sulpcHBB1ce8JdeCps27b/c4YfDG2+EC13s2hX+Ccrexztt6VK4//7wuESLFuEDq1ev8I+1a9e+9zWZVlwc3pPdu8Nr7dEj7Bto0ybs6K3Jfflpf/kL/Pznez94br4Zxo4Ng5p9/vne+7KPazKtqAg2bw6tw2bNoGPHcGvZcu8tLW3f55Xdys+3ciU89RR89ln4O+nbN5x8VhLMFZ18dsAB0LVrCOfKboceGoZeruxD2T2EZkmgb90ahp0o+7yyW8l8mzaFDwL30Pho0yZss1Wr8Npatdr3cU3vX38dHnsMPv00/K3UpkGhPnBJKvfwD1AS6FXdNm1qGH3nbdqEMG/RIgRQ2ft4pm3YEA7h3LNn7zqbNYMjjtjb7fT553vvy85XH1q2DMFT8uFQ8rhtW/jkk/DtqmxNzZuHw1K7dg0fgGU/DKu7lcxXWevXDL75Tejfv/Jwbt8+vm6Nujwqqq6PuErW+hXgkjJ79oQA2bgRvvvd0IItr2NHuPHGvS27svfVTSv7ODOz4hqSsQO2JkfnuIeQKxvoVd3PmBFaxxW9LzffXHkwl0xr0ya8/mTUXhNffx2+hVQ0Fu+Ws/oAAATlSURBVH2yjlqqy6Oi6vqIq2StXwEuDUJdt3jq8h+yLo/Oqesjf1R7/a87mevXYFbSINT1ULh1OVZ6XV5Io64v0qHa63/d9bF+3L3WN2AE8DbwLjC1uvkHDRrkInXtvvvcu3d3Nwv3992XvPWmp7uHNlW4pacnZ/11ue66Xr9qr/v1AwVeUQZXNDGeG9AceA84AmgJvA4cVdUyCnCJurr6cKjrddf1+lV73a6/sgCvdR+4mR0HzHD302LPr4u16P+7smXUBy4iUnN10QfeFShzYjaFsWnlNzzRzArMrKCoqCiBzYmISFl1vhPT3ee6e46752RkZNT15kREmoxEAvxD4PAyzzNj00REpB4kEuCvAkeaWQ8zawmcBzyanLJERKQ6tR7Myt13m9lPgGcIR6Tc7e6rklaZiIhUqV7PxDSzIqCC8+Ti0gX4JInl1CfVnhpRrT2qdYNqryvd3X2/nYj1GuCJMLOCig6jiQLVnhpRrT2qdYNqr286lV5EJKIU4CIiERWlAJ+b6gISoNpTI6q1R7VuUO31KjJ94CIisq8otcBFRKQMBbiISERFIsDNbISZvW1m75rZ1FTXEy8zO9zMXjSzt8xslZldkeqaasLMmpvZa2b2eKprqQkz62hmD5nZGjNbHRs5MxLMbErsb2WlmT1gZq1TXVNlzOxuM/vYzFaWmdbJzJ4zs3di9wemssbKVFL7LbG/mTfM7P+ZWcdU1hiPBh/gZtYc+B0wEjgKGGdmR6W2qrjtBq5296OAY4FLI1Q7wBXA6lQXUQu/AZ52995AfyLyGsysK3A5kOPufQhnOJ+X2qqqdA/hoi5lTQVecPcjgRdizxuie9i/9ueAPu7eD/g3cF19F1VTDT7AgWOAd939P+6+E1gAjElxTXFx9w3uvjz2eBshSPYbcrchMrNM4AzgrlTXUhNm1gEYDswDcPed7l7B5YIbrBbAAWbWAkgHPkpxPZVy98XAp+UmjwHujT2+FzizXouKU0W1u/uz7r479vSfhAH6GrQoBHhc4443dGaWBQwAXkltJXGbDVwLJOHSrvWqB1AEzI91/9xlZm1SXVQ83P1DYBawHtgAbHH3Z1NbVY0d7O4bYo83AgenspgETACeSnUR1YlCgEeembUFHgaudPetqa6nOmY2CvjY3ZelupZaaAEMBO5w9wHA5zTcr/H7iPUXjyF8CB0GtDGzH6a2qtqLXQoscscpm9k0QvdnfqprqU4UAjzS446bWRohvPPd/a+pridOw4DRZraW0GV1kpndl9qS4lYIFLp7yTedhwiBHgWnAO+7e5G77wL+CgxNcU01tcnMDgWI3X+c4npqxMzygFFArkfgJJkoBHhkxx03MyP0xa5291tTXU+83P06d8909yzC+73Q3SPREnT3jcAHZtYrNulk4K0UllQT64FjzSw99rdzMhHZAVvGo8D42OPxwN9SWEuNmNkIQrfhaHffkep64tHgAzy2U6Fk3PHVwJ8jNO74MOB8Qgt2Rex2eqqLagIuA/LN7A0gG/hliuuJS+xbw0PAcuBNwv9ngz2928weAP4B9DKzQjP7EfAr4FQze4fwjeJXqayxMpXUfhvQDngu9r/6+5QWGQedSi8iElENvgUuIiIVU4CLiESUAlxEJKIU4CIiEaUAFxGJKAW4iEhEKcBFRCLq/wOcqdR1HxgGIgAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["evaluate_model(tf.keras.models.load_model(GLOBAL_MODELS_FOLDER + '/experiment10_feature_extraction.h5'))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":730},"executionInfo":{"status":"ok","timestamp":1641803318688,"user_tz":-60,"elapsed":9572,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"f7b3a5e0-375e-410e-a802-08576f07f571","id":"ycleoKIaSJVz"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Classification report: \n","              precision    recall  f1-score   support\n","\n","           0     0.6189    0.7888    0.6936       696\n","           1     0.8820    0.7648    0.8192      1437\n","\n","    accuracy                         0.7726      2133\n","   macro avg     0.7505    0.7768    0.7564      2133\n","weighted avg     0.7962    0.7726    0.7782      2133\n","\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["### Fine tuning"],"metadata":{"id":"5hd0fFOSCFSu"}},{"cell_type":"markdown","source":["#### Experiment 11"],"metadata":{"id":"7mTw7rkFESiS"}},{"cell_type":"code","source":["fine_tuning = models.load_model(os.path.join(GLOBAL_MODELS_FOLDER, 'experiment7_feature_extraction.h5'))\n","\n","setTrainable = False\n","for layer in fine_tuning.get_layer('base').layers:\n","    if layer.name in ('block_15_depthwise_BN'):\n","        setTrainable = True\n","    if setTrainable == True:\n","        layer.trainable = True\n","    else:\n","      layer.trainable = False\n","\n","compile_model(fine_tuning, learning_rate=1e-5)\n","\n","for i, layer in enumerate(fine_tuning.get_layer('base').layers):\n","    print(i, layer.name, layer.trainable)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SGlRzWSfEUlY","executionInfo":{"status":"ok","timestamp":1641803394765,"user_tz":-60,"elapsed":2592,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"50da1e15-ac00-45a2-8aec-25293bfff1af"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model_2\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input_5 (InputLayer)        [(None, 224, 224, 3)]     0         \n","                                                                 \n"," tf.math.truediv_2 (TFOpLamb  (None, 224, 224, 3)      0         \n"," da)                                                             \n","                                                                 \n"," tf.math.subtract_2 (TFOpLam  (None, 224, 224, 3)      0         \n"," bda)                                                            \n","                                                                 \n"," base (Functional)           (None, 7, 7, 160)         1364864   \n","                                                                 \n"," global_average_pooling2d_1   (None, 160)              0         \n"," (GlobalAveragePooling2D)                                        \n","                                                                 \n"," dense_3 (Dense)             (None, 256)               41216     \n","                                                                 \n"," dropout_1 (Dropout)         (None, 256)               0         \n","                                                                 \n"," dense_4 (Dense)             (None, 1)                 257       \n","                                                                 \n","=================================================================\n","Total params: 1,406,337\n","Trainable params: 205,953\n","Non-trainable params: 1,200,384\n","_________________________________________________________________\n","0 input_2 False\n","1 Conv1 False\n","2 bn_Conv1 False\n","3 Conv1_relu False\n","4 expanded_conv_depthwise False\n","5 expanded_conv_depthwise_BN False\n","6 expanded_conv_depthwise_relu False\n","7 expanded_conv_project False\n","8 expanded_conv_project_BN False\n","9 block_1_expand False\n","10 block_1_expand_BN False\n","11 block_1_expand_relu False\n","12 block_1_pad False\n","13 block_1_depthwise False\n","14 block_1_depthwise_BN False\n","15 block_1_depthwise_relu False\n","16 block_1_project False\n","17 block_1_project_BN False\n","18 block_2_expand False\n","19 block_2_expand_BN False\n","20 block_2_expand_relu False\n","21 block_2_depthwise False\n","22 block_2_depthwise_BN False\n","23 block_2_depthwise_relu False\n","24 block_2_project False\n","25 block_2_project_BN False\n","26 block_2_add False\n","27 block_3_expand False\n","28 block_3_expand_BN False\n","29 block_3_expand_relu False\n","30 block_3_pad False\n","31 block_3_depthwise False\n","32 block_3_depthwise_BN False\n","33 block_3_depthwise_relu False\n","34 block_3_project False\n","35 block_3_project_BN False\n","36 block_4_expand False\n","37 block_4_expand_BN False\n","38 block_4_expand_relu False\n","39 block_4_depthwise False\n","40 block_4_depthwise_BN False\n","41 block_4_depthwise_relu False\n","42 block_4_project False\n","43 block_4_project_BN False\n","44 block_4_add False\n","45 block_5_expand False\n","46 block_5_expand_BN False\n","47 block_5_expand_relu False\n","48 block_5_depthwise False\n","49 block_5_depthwise_BN False\n","50 block_5_depthwise_relu False\n","51 block_5_project False\n","52 block_5_project_BN False\n","53 block_5_add False\n","54 block_6_expand False\n","55 block_6_expand_BN False\n","56 block_6_expand_relu False\n","57 block_6_pad False\n","58 block_6_depthwise False\n","59 block_6_depthwise_BN False\n","60 block_6_depthwise_relu False\n","61 block_6_project False\n","62 block_6_project_BN False\n","63 block_7_expand False\n","64 block_7_expand_BN False\n","65 block_7_expand_relu False\n","66 block_7_depthwise False\n","67 block_7_depthwise_BN False\n","68 block_7_depthwise_relu False\n","69 block_7_project False\n","70 block_7_project_BN False\n","71 block_7_add False\n","72 block_8_expand False\n","73 block_8_expand_BN False\n","74 block_8_expand_relu False\n","75 block_8_depthwise False\n","76 block_8_depthwise_BN False\n","77 block_8_depthwise_relu False\n","78 block_8_project False\n","79 block_8_project_BN False\n","80 block_8_add False\n","81 block_9_expand False\n","82 block_9_expand_BN False\n","83 block_9_expand_relu False\n","84 block_9_depthwise False\n","85 block_9_depthwise_BN False\n","86 block_9_depthwise_relu False\n","87 block_9_project False\n","88 block_9_project_BN False\n","89 block_9_add False\n","90 block_10_expand False\n","91 block_10_expand_BN False\n","92 block_10_expand_relu False\n","93 block_10_depthwise False\n","94 block_10_depthwise_BN False\n","95 block_10_depthwise_relu False\n","96 block_10_project False\n","97 block_10_project_BN False\n","98 block_11_expand False\n","99 block_11_expand_BN False\n","100 block_11_expand_relu False\n","101 block_11_depthwise False\n","102 block_11_depthwise_BN False\n","103 block_11_depthwise_relu False\n","104 block_11_project False\n","105 block_11_project_BN False\n","106 block_11_add False\n","107 block_12_expand False\n","108 block_12_expand_BN False\n","109 block_12_expand_relu False\n","110 block_12_depthwise False\n","111 block_12_depthwise_BN False\n","112 block_12_depthwise_relu False\n","113 block_12_project False\n","114 block_12_project_BN False\n","115 block_12_add False\n","116 block_13_expand False\n","117 block_13_expand_BN False\n","118 block_13_expand_relu False\n","119 block_13_pad False\n","120 block_13_depthwise False\n","121 block_13_depthwise_BN False\n","122 block_13_depthwise_relu False\n","123 block_13_project False\n","124 block_13_project_BN False\n","125 block_14_expand False\n","126 block_14_expand_BN False\n","127 block_14_expand_relu False\n","128 block_14_depthwise False\n","129 block_14_depthwise_BN False\n","130 block_14_depthwise_relu False\n","131 block_14_project False\n","132 block_14_project_BN False\n","133 block_14_add False\n","134 block_15_expand False\n","135 block_15_expand_BN False\n","136 block_15_expand_relu False\n","137 block_15_depthwise True\n","138 block_15_depthwise_BN True\n","139 block_15_depthwise_relu True\n","140 block_15_project True\n","141 block_15_project_BN True\n","142 block_15_add True\n"]}]},{"cell_type":"code","source":["run_model(fine_tuning, 'experiment11_fine_tuning_cut', epochs = 100, patience=5)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"os6RI-LJExDN","executionInfo":{"status":"ok","timestamp":1641804514011,"user_tz":-60,"elapsed":729557,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"3565ac94-3f77-4318-f746-444b0630be78"},"execution_count":null,"outputs":[{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["Epoch 1/100\n","200/200 [==============================] - ETA: 0s - loss: 0.5147 - accuracy: 0.7652\n","Epoch 00001: val_loss improved from inf to 0.42879, saving model to /content/models/experiment9_fine_tuning_cut.h5\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n","  layer_config = serialize_layer_fn(layer)\n"]},{"output_type":"stream","name":"stdout","text":["200/200 [==============================] - 29s 125ms/step - loss: 0.5147 - accuracy: 0.7652 - val_loss: 0.4288 - val_accuracy: 0.8110\n","Epoch 2/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4850 - accuracy: 0.7706\n","Epoch 00002: val_loss improved from 0.42879 to 0.42622, saving model to /content/models/experiment9_fine_tuning_cut.h5\n","200/200 [==============================] - 24s 121ms/step - loss: 0.4850 - accuracy: 0.7706 - val_loss: 0.4262 - val_accuracy: 0.8129\n","Epoch 3/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4748 - accuracy: 0.7753\n","Epoch 00003: val_loss improved from 0.42622 to 0.42404, saving model to /content/models/experiment9_fine_tuning_cut.h5\n","200/200 [==============================] - 24s 119ms/step - loss: 0.4748 - accuracy: 0.7753 - val_loss: 0.4240 - val_accuracy: 0.8208\n","Epoch 4/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4728 - accuracy: 0.7728\n","Epoch 00004: val_loss improved from 0.42404 to 0.42252, saving model to /content/models/experiment9_fine_tuning_cut.h5\n","200/200 [==============================] - 25s 121ms/step - loss: 0.4728 - accuracy: 0.7728 - val_loss: 0.4225 - val_accuracy: 0.8204\n","Epoch 5/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4709 - accuracy: 0.7791\n","Epoch 00005: val_loss improved from 0.42252 to 0.42066, saving model to /content/models/experiment9_fine_tuning_cut.h5\n","200/200 [==============================] - 24s 120ms/step - loss: 0.4709 - accuracy: 0.7791 - val_loss: 0.4207 - val_accuracy: 0.8194\n","Epoch 6/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4646 - accuracy: 0.7797\n","Epoch 00006: val_loss improved from 0.42066 to 0.41894, saving model to /content/models/experiment9_fine_tuning_cut.h5\n","200/200 [==============================] - 24s 121ms/step - loss: 0.4646 - accuracy: 0.7797 - val_loss: 0.4189 - val_accuracy: 0.8161\n","Epoch 7/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4623 - accuracy: 0.7803\n","Epoch 00007: val_loss improved from 0.41894 to 0.41872, saving model to /content/models/experiment9_fine_tuning_cut.h5\n","200/200 [==============================] - 24s 119ms/step - loss: 0.4623 - accuracy: 0.7803 - val_loss: 0.4187 - val_accuracy: 0.8255\n","Epoch 8/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4594 - accuracy: 0.7831\n","Epoch 00008: val_loss improved from 0.41872 to 0.41763, saving model to /content/models/experiment9_fine_tuning_cut.h5\n","200/200 [==============================] - 25s 122ms/step - loss: 0.4594 - accuracy: 0.7831 - val_loss: 0.4176 - val_accuracy: 0.8269\n","Epoch 9/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4588 - accuracy: 0.7830\n","Epoch 00009: val_loss improved from 0.41763 to 0.41572, saving model to /content/models/experiment9_fine_tuning_cut.h5\n","200/200 [==============================] - 25s 122ms/step - loss: 0.4588 - accuracy: 0.7830 - val_loss: 0.4157 - val_accuracy: 0.8246\n","Epoch 10/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4565 - accuracy: 0.7860\n","Epoch 00010: val_loss improved from 0.41572 to 0.41412, saving model to /content/models/experiment9_fine_tuning_cut.h5\n","200/200 [==============================] - 25s 122ms/step - loss: 0.4565 - accuracy: 0.7860 - val_loss: 0.4141 - val_accuracy: 0.8255\n","Epoch 11/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4553 - accuracy: 0.7881\n","Epoch 00011: val_loss did not improve from 0.41412\n","200/200 [==============================] - 24s 120ms/step - loss: 0.4553 - accuracy: 0.7881 - val_loss: 0.4170 - val_accuracy: 0.8283\n","Epoch 12/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4540 - accuracy: 0.7827\n","Epoch 00012: val_loss improved from 0.41412 to 0.41341, saving model to /content/models/experiment9_fine_tuning_cut.h5\n","200/200 [==============================] - 25s 122ms/step - loss: 0.4540 - accuracy: 0.7827 - val_loss: 0.4134 - val_accuracy: 0.8297\n","Epoch 13/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4497 - accuracy: 0.7839\n","Epoch 00013: val_loss did not improve from 0.41341\n","200/200 [==============================] - 24s 120ms/step - loss: 0.4497 - accuracy: 0.7839 - val_loss: 0.4138 - val_accuracy: 0.8265\n","Epoch 14/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4487 - accuracy: 0.7894\n","Epoch 00014: val_loss improved from 0.41341 to 0.40900, saving model to /content/models/experiment9_fine_tuning_cut.h5\n","200/200 [==============================] - 25s 123ms/step - loss: 0.4487 - accuracy: 0.7894 - val_loss: 0.4090 - val_accuracy: 0.8232\n","Epoch 15/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4472 - accuracy: 0.7917\n","Epoch 00015: val_loss improved from 0.40900 to 0.40803, saving model to /content/models/experiment9_fine_tuning_cut.h5\n","200/200 [==============================] - 24s 121ms/step - loss: 0.4472 - accuracy: 0.7917 - val_loss: 0.4080 - val_accuracy: 0.8222\n","Epoch 16/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4429 - accuracy: 0.7919\n","Epoch 00016: val_loss did not improve from 0.40803\n","200/200 [==============================] - 24s 121ms/step - loss: 0.4429 - accuracy: 0.7919 - val_loss: 0.4084 - val_accuracy: 0.8283\n","Epoch 17/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4400 - accuracy: 0.7947\n","Epoch 00017: val_loss improved from 0.40803 to 0.40589, saving model to /content/models/experiment9_fine_tuning_cut.h5\n","200/200 [==============================] - 24s 121ms/step - loss: 0.4400 - accuracy: 0.7947 - val_loss: 0.4059 - val_accuracy: 0.8218\n","Epoch 18/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4426 - accuracy: 0.7958\n","Epoch 00018: val_loss did not improve from 0.40589\n","200/200 [==============================] - 24s 121ms/step - loss: 0.4426 - accuracy: 0.7958 - val_loss: 0.4061 - val_accuracy: 0.8260\n","Epoch 19/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4409 - accuracy: 0.7902\n","Epoch 00019: val_loss improved from 0.40589 to 0.40436, saving model to /content/models/experiment9_fine_tuning_cut.h5\n","200/200 [==============================] - 25s 122ms/step - loss: 0.4409 - accuracy: 0.7902 - val_loss: 0.4044 - val_accuracy: 0.8265\n","Epoch 20/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4362 - accuracy: 0.7982\n","Epoch 00020: val_loss improved from 0.40436 to 0.40371, saving model to /content/models/experiment9_fine_tuning_cut.h5\n","200/200 [==============================] - 25s 122ms/step - loss: 0.4362 - accuracy: 0.7982 - val_loss: 0.4037 - val_accuracy: 0.8255\n","Epoch 21/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4333 - accuracy: 0.7991\n","Epoch 00021: val_loss improved from 0.40371 to 0.40345, saving model to /content/models/experiment9_fine_tuning_cut.h5\n","200/200 [==============================] - 24s 121ms/step - loss: 0.4333 - accuracy: 0.7991 - val_loss: 0.4034 - val_accuracy: 0.8260\n","Epoch 22/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4307 - accuracy: 0.7983\n","Epoch 00022: val_loss did not improve from 0.40345\n","200/200 [==============================] - 24s 119ms/step - loss: 0.4307 - accuracy: 0.7983 - val_loss: 0.4035 - val_accuracy: 0.8265\n","Epoch 23/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4296 - accuracy: 0.8010\n","Epoch 00023: val_loss did not improve from 0.40345\n","200/200 [==============================] - 24s 121ms/step - loss: 0.4296 - accuracy: 0.8010 - val_loss: 0.4047 - val_accuracy: 0.8283\n","Epoch 24/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4316 - accuracy: 0.7982\n","Epoch 00024: val_loss improved from 0.40345 to 0.40029, saving model to /content/models/experiment9_fine_tuning_cut.h5\n","200/200 [==============================] - 24s 121ms/step - loss: 0.4316 - accuracy: 0.7982 - val_loss: 0.4003 - val_accuracy: 0.8279\n","Epoch 25/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4277 - accuracy: 0.8007\n","Epoch 00025: val_loss did not improve from 0.40029\n","200/200 [==============================] - 24s 121ms/step - loss: 0.4277 - accuracy: 0.8007 - val_loss: 0.4073 - val_accuracy: 0.8293\n","Epoch 26/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4294 - accuracy: 0.8000\n","Epoch 00026: val_loss did not improve from 0.40029\n","200/200 [==============================] - 24s 120ms/step - loss: 0.4294 - accuracy: 0.8000 - val_loss: 0.4069 - val_accuracy: 0.8302\n","Epoch 27/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4254 - accuracy: 0.8039\n","Epoch 00027: val_loss did not improve from 0.40029\n","200/200 [==============================] - 24s 121ms/step - loss: 0.4254 - accuracy: 0.8039 - val_loss: 0.4026 - val_accuracy: 0.8288\n","Epoch 28/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4245 - accuracy: 0.8019\n","Epoch 00028: val_loss improved from 0.40029 to 0.39828, saving model to /content/models/experiment9_fine_tuning_cut.h5\n","200/200 [==============================] - 25s 122ms/step - loss: 0.4245 - accuracy: 0.8019 - val_loss: 0.3983 - val_accuracy: 0.8283\n","Epoch 29/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4218 - accuracy: 0.7991\n","Epoch 00029: val_loss did not improve from 0.39828\n","200/200 [==============================] - 24s 121ms/step - loss: 0.4218 - accuracy: 0.7991 - val_loss: 0.4038 - val_accuracy: 0.8288\n","Epoch 30/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4246 - accuracy: 0.8018\n","Epoch 00030: val_loss did not improve from 0.39828\n","200/200 [==============================] - 25s 122ms/step - loss: 0.4246 - accuracy: 0.8018 - val_loss: 0.4254 - val_accuracy: 0.8293\n","Epoch 31/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4217 - accuracy: 0.8046\n","Epoch 00031: val_loss did not improve from 0.39828\n","200/200 [==============================] - 24s 121ms/step - loss: 0.4217 - accuracy: 0.8046 - val_loss: 0.4000 - val_accuracy: 0.8293\n","Epoch 32/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4193 - accuracy: 0.8064\n","Epoch 00032: val_loss did not improve from 0.39828\n","200/200 [==============================] - 25s 121ms/step - loss: 0.4193 - accuracy: 0.8064 - val_loss: 0.4168 - val_accuracy: 0.8311\n","Epoch 33/100\n","200/200 [==============================] - ETA: 0s - loss: 0.4182 - accuracy: 0.8061\n","Epoch 00033: val_loss did not improve from 0.39828\n","200/200 [==============================] - 24s 120ms/step - loss: 0.4182 - accuracy: 0.8061 - val_loss: 0.4037 - val_accuracy: 0.8316\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["evaluate_model(tf.keras.models.load_model(GLOBAL_MODELS_FOLDER + '/experiment11_fine_tuning_cut.h5'))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":730},"id":"itENNvtuLE3S","executionInfo":{"status":"ok","timestamp":1642022624650,"user_tz":-60,"elapsed":11263,"user":{"displayName":"Federica Baldi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj0eALl9wD-GJPtSTMAFK0DvDlcGWh5ZTpdGFtwCIo=s64","userId":"15726835528723494588"}},"outputId":"b338e49c-e5ae-433d-cc2c-9622222e6fd3"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["Classification report: \n","              precision    recall  f1-score   support\n","\n","           0     0.6170    0.7615    0.6817       696\n","           1     0.8697    0.7711    0.8174      1437\n","\n","    accuracy                         0.7679      2133\n","   macro avg     0.7433    0.7663    0.7495      2133\n","weighted avg     0.7872    0.7679    0.7731      2133\n","\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]}]}