transfer-learning-fine-tuning-feature-extract (CAT DOG CLASSIFIER).ipynb

{"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"accelerator":"GPU","kaggle":{"accelerator":"gpu","dataSources":[],"dockerImageVersionId":30747,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!mkdir -p ~/.kaggle\n!cp kaggle.json ~/.kaggle/","metadata":{"id":"itSCyw3ipYfv","execution":{"iopub.status.busy":"2024-08-11T11:26:40.706944Z","iopub.execute_input":"2024-08-11T11:26:40.707532Z","iopub.status.idle":"2024-08-11T11:26:42.693510Z","shell.execute_reply.started":"2024-08-11T11:26:40.707498Z","shell.execute_reply":"2024-08-11T11:26:42.692568Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"cp: cannot stat 'kaggle.json': No such file or directory\n","output_type":"stream"}]},{"cell_type":"code","source":"!kaggle datasets download -d salader/dogs-vs-cats","metadata":{"id":"KW42KSn9pfyA","colab":{"base_uri":"https://localhost:8080/"},"outputId":"65abb533-1cdd-440e-aa3c-eb3c16021b89","execution":{"iopub.status.busy":"2024-08-11T11:26:42.695468Z","iopub.execute_input":"2024-08-11T11:26:42.695796Z","iopub.status.idle":"2024-08-11T11:26:49.979765Z","shell.execute_reply.started":"2024-08-11T11:26:42.695771Z","shell.execute_reply":"2024-08-11T11:26:49.978758Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Warning: Looks like you're using an outdated API Version, please consider updating (server 1.6.17 / client 1.6.14)\nDataset URL: https://www.kaggle.com/datasets/salader/dogs-vs-cats\nLicense(s): unknown\nDownloading dogs-vs-cats.zip to /kaggle/working\n 99%|██████████████████████████████████████▋| 1.06G/1.06G [00:05<00:00, 241MB/s]\n100%|███████████████████████████████████████| 1.06G/1.06G [00:05<00:00, 214MB/s]\n","output_type":"stream"}]},{"cell_type":"code","source":"import zipfile\nzip_ref = zipfile.ZipFile('/kaggle/working/dogs-vs-cats.zip', 'r')\nzip_ref.extractall('/kaggle/working')\nzip_ref.close()\n\n# or use this:\n# !unzip -q /kaggle/working/dogs-vs-cats.zip","metadata":{"id":"CXz412Aip5B4","execution":{"iopub.status.busy":"2024-08-11T11:26:49.980937Z","iopub.execute_input":"2024-08-11T11:26:49.981219Z","iopub.status.idle":"2024-08-11T11:27:02.074134Z","shell.execute_reply.started":"2024-08-11T11:26:49.981195Z","shell.execute_reply":"2024-08-11T11:27:02.073142Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow import keras\nfrom keras import Sequential\nfrom keras.layers import Dense, Flatten, BatchNormalization, Dropout\nfrom keras.applications.vgg16 import VGG16\nfrom keras.callbacks import EarlyStopping","metadata":{"id":"8dAgedWkp8Pi","execution":{"iopub.status.busy":"2024-08-11T13:38:08.415858Z","iopub.execute_input":"2024-08-11T13:38:08.416857Z","iopub.status.idle":"2024-08-11T13:38:08.421582Z","shell.execute_reply.started":"2024-08-11T13:38:08.416819Z","shell.execute_reply":"2024-08-11T13:38:08.420668Z"},"trusted":true},"execution_count":63,"outputs":[]},{"cell_type":"code","source":"conv_base = VGG16(\n    weights='imagenet',\n    include_top = False,\n    input_shape=(224, 224,3) # could be any shape\n)","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xGeyBIoxp_ji","outputId":"53603394-54df-4183-8972-60dead8a38e3","execution":{"iopub.status.busy":"2024-08-11T14:10:05.222920Z","iopub.execute_input":"2024-08-11T14:10:05.223703Z","iopub.status.idle":"2024-08-11T14:10:05.481026Z","shell.execute_reply.started":"2024-08-11T14:10:05.223668Z","shell.execute_reply":"2024-08-11T14:10:05.480244Z"},"trusted":true},"execution_count":94,"outputs":[]},{"cell_type":"code","source":"set_trainable = False\n\nfor layer in conv_base.layers:\n  if layer.name == 'block5_conv1':\n    set_trainable = True\n  if set_trainable:\n    layer.trainable = True\n  else:\n    layer.trainable = False\n\nfor layer in conv_base.layers:\n  print(layer.name, layer.trainable)","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rsRlwtCUqB81","outputId":"dfea1783-8823-4e98-859c-5a525fa8ae51","execution":{"iopub.status.busy":"2024-08-11T14:10:06.728976Z","iopub.execute_input":"2024-08-11T14:10:06.729760Z","iopub.status.idle":"2024-08-11T14:10:06.736463Z","shell.execute_reply.started":"2024-08-11T14:10:06.729720Z","shell.execute_reply":"2024-08-11T14:10:06.735448Z"},"trusted":true},"execution_count":95,"outputs":[{"name":"stdout","text":"input_layer_11 False\nblock1_conv1 False\nblock1_conv2 False\nblock1_pool False\nblock2_conv1 False\nblock2_conv2 False\nblock2_pool False\nblock3_conv1 False\nblock3_conv2 False\nblock3_conv3 False\nblock3_pool False\nblock4_conv1 False\nblock4_conv2 False\nblock4_conv3 False\nblock4_pool False\nblock5_conv1 True\nblock5_conv2 True\nblock5_conv3 True\nblock5_pool True\n","output_type":"stream"}]},{"cell_type":"code","source":"conv_base.summary()","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qMNnxAe8qxaj","outputId":"9629c8b0-30dd-49fe-b2a0-bad513983e2d","execution":{"iopub.status.busy":"2024-08-11T14:10:07.328258Z","iopub.execute_input":"2024-08-11T14:10:07.328624Z","iopub.status.idle":"2024-08-11T14:10:07.363792Z","shell.execute_reply.started":"2024-08-11T14:10:07.328592Z","shell.execute_reply":"2024-08-11T14:10:07.362761Z"},"trusted":true},"execution_count":96,"outputs":[{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"vgg16\"\u001b[0m\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"vgg16\"</span>\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ input_layer_11 (\u001b[38;5;33mInputLayer\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m3\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │         \u001b[38;5;34m1,792\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │        \u001b[38;5;34m36,928\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m128\u001b[0m)  │        \u001b[38;5;34m73,856\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m128\u001b[0m)  │       \u001b[38;5;34m147,584\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m295,168\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m590,080\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv3 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m590,080\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m1,180,160\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv3 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv3 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ input_layer_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,792</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │        <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)  │       <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,180,160</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m14,714,688\u001b[0m (56.13 MB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">14,714,688</span> (56.13 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m7,079,424\u001b[0m (27.01 MB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,079,424</span> (27.01 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m7,635,264\u001b[0m (29.13 MB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,635,264</span> (29.13 MB)\n</pre>\n"},"metadata":{}}]},{"cell_type":"code","source":"print(conv_base.output_shape)","metadata":{"execution":{"iopub.status.busy":"2024-08-11T14:13:18.540566Z","iopub.execute_input":"2024-08-11T14:13:18.540925Z","iopub.status.idle":"2024-08-11T14:13:18.545881Z","shell.execute_reply.started":"2024-08-11T14:13:18.540896Z","shell.execute_reply":"2024-08-11T14:13:18.544996Z"},"trusted":true},"execution_count":102,"outputs":[{"name":"stdout","text":"(None, 7, 7, 512)\n","output_type":"stream"}]},{"cell_type":"code","source":"model = Sequential()\n\nmodel.add(conv_base)\n\nmodel.add(Flatten())\n\nmodel.add(Dense(256,activation='relu'))\n\nmodel.add(Dense(1,activation='sigmoid'))\n\nmodel.build((None, 224, 224, 3)) ","metadata":{"id":"MuCh9EqrsMPK","execution":{"iopub.status.busy":"2024-08-11T16:15:09.640928Z","iopub.execute_input":"2024-08-11T16:15:09.641327Z","iopub.status.idle":"2024-08-11T16:15:09.670857Z","shell.execute_reply.started":"2024-08-11T16:15:09.641297Z","shell.execute_reply":"2024-08-11T16:15:09.670038Z"},"trusted":true},"execution_count":128,"outputs":[]},{"cell_type":"code","source":"model.summary()","metadata":{"execution":{"iopub.status.busy":"2024-08-11T16:15:10.760108Z","iopub.execute_input":"2024-08-11T16:15:10.760471Z","iopub.status.idle":"2024-08-11T16:15:10.780779Z","shell.execute_reply.started":"2024-08-11T16:15:10.760440Z","shell.execute_reply":"2024-08-11T16:15:10.779901Z"},"trusted":true},"execution_count":129,"outputs":[{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"sequential_20\"\u001b[0m\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_20\"</span>\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ vgg16 (\u001b[38;5;33mFunctional\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │    \u001b[38;5;34m14,714,688\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ flatten_20 (\u001b[38;5;33mFlatten\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25088\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_49 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │     \u001b[38;5;34m6,422,784\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_50 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │           \u001b[38;5;34m257\u001b[0m │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ vgg16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Functional</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │    <span style=\"color: #00af00; text-decoration-color: #00af00\">14,714,688</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ flatten_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25088</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_49 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │     <span style=\"color: #00af00; text-decoration-color: #00af00\">6,422,784</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_50 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">257</span> │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m21,137,729\u001b[0m (80.63 MB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">21,137,729</span> (80.63 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m13,502,465\u001b[0m (51.51 MB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">13,502,465</span> (51.51 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m7,635,264\u001b[0m (29.13 MB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,635,264</span> (29.13 MB)\n</pre>\n"},"metadata":{}}]},{"cell_type":"markdown","source":"# **Using Keras data loading utility**\n\n* Loading in Batches: Instead of loading the entire dataset into RAM, which might be impractical or impossible for large datasets\n\n* On-the-Fly Data Preprocessing: The data is loaded, preprocessed (e.g., resized, normalized, augmented), and fed into the model in real-time during training. \n\n* tf.data.Dataset: allows for various performance optimizations, such as parallel data loading, prefetching, and caching, which can significantly speed up the training process","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n\nbatch_size = 32\n\ntrain_datagen = ImageDataGenerator(\n        rescale=1./255, # This line normalizes the image pixels to the range [0, 1]\n        shear_range=0.2,\n        zoom_range=0.2,\n        horizontal_flip=True)\n\ntest_datagen = ImageDataGenerator(rescale=1./255)\n\n\ntrain_generator = train_datagen.flow_from_directory(\n        '/kaggle/working/train',\n        target_size=(224, 224),\n        batch_size=batch_size,\n        class_mode='binary') \n\nvalidation_generator = test_datagen.flow_from_directory(\n        '/kaggle/working/test',\n        target_size=(224, 224),\n        batch_size=batch_size,\n        class_mode='binary')","metadata":{"execution":{"iopub.status.busy":"2024-08-11T16:15:12.598067Z","iopub.execute_input":"2024-08-11T16:15:12.598450Z","iopub.status.idle":"2024-08-11T16:15:13.276655Z","shell.execute_reply.started":"2024-08-11T16:15:12.598422Z","shell.execute_reply":"2024-08-11T16:15:13.275707Z"},"trusted":true},"execution_count":130,"outputs":[{"name":"stdout","text":"Found 20000 images belonging to 2 classes.\nFound 5000 images belonging to 2 classes.\n","output_type":"stream"}]},{"cell_type":"code","source":"# Compile the model\nmodel.compile(\n    optimizer=tf.keras.optimizers.RMSprop(learning_rate=1e-5), # WHEN FINE-TUNING, THE IT'S GOOD TO KEEP THE LR SMALL\n    loss='binary_crossentropy',\n    metrics=['accuracy']\n)","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nxUjDM6Nr5Qp","outputId":"1f31ef8f-8966-40dd-ecee-397450f449c3","execution":{"iopub.status.busy":"2024-08-11T16:15:13.278286Z","iopub.execute_input":"2024-08-11T16:15:13.278575Z","iopub.status.idle":"2024-08-11T16:15:13.287574Z","shell.execute_reply.started":"2024-08-11T16:15:13.278551Z","shell.execute_reply":"2024-08-11T16:15:13.286652Z"},"trusted":true},"execution_count":131,"outputs":[]},{"cell_type":"code","source":"model.summary()","metadata":{"execution":{"iopub.status.busy":"2024-08-11T16:15:13.289166Z","iopub.execute_input":"2024-08-11T16:15:13.289730Z","iopub.status.idle":"2024-08-11T16:15:13.312016Z","shell.execute_reply.started":"2024-08-11T16:15:13.289705Z","shell.execute_reply":"2024-08-11T16:15:13.311008Z"},"trusted":true},"execution_count":132,"outputs":[{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"sequential_20\"\u001b[0m\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_20\"</span>\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ vgg16 (\u001b[38;5;33mFunctional\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │    \u001b[38;5;34m14,714,688\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ flatten_20 (\u001b[38;5;33mFlatten\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25088\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_49 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │     \u001b[38;5;34m6,422,784\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_50 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │           \u001b[38;5;34m257\u001b[0m │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ vgg16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Functional</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │    <span style=\"color: #00af00; text-decoration-color: #00af00\">14,714,688</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ flatten_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25088</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_49 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │     <span style=\"color: #00af00; text-decoration-color: #00af00\">6,422,784</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_50 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">257</span> │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m21,137,729\u001b[0m (80.63 MB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">21,137,729</span> (80.63 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m13,502,465\u001b[0m (51.51 MB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">13,502,465</span> (51.51 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m7,635,264\u001b[0m (29.13 MB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,635,264</span> (29.13 MB)\n</pre>\n"},"metadata":{}}]},{"cell_type":"code","source":"early_stopping = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)","metadata":{"execution":{"iopub.status.busy":"2024-08-11T16:15:14.824848Z","iopub.execute_input":"2024-08-11T16:15:14.825507Z","iopub.status.idle":"2024-08-11T16:15:14.829667Z","shell.execute_reply.started":"2024-08-11T16:15:14.825474Z","shell.execute_reply":"2024-08-11T16:15:14.828681Z"},"trusted":true},"execution_count":133,"outputs":[]},{"cell_type":"code","source":"history = model.fit(\n        train_generator,\n        epochs=50,\n        validation_data=validation_generator,\n        callbacks = [early_stopping])","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tiat79J_sG6W","outputId":"59a3af41-7bca-4ff5-bf14-60fd56523c35","execution":{"iopub.status.busy":"2024-08-11T16:15:15.246029Z","iopub.execute_input":"2024-08-11T16:15:15.246444Z","iopub.status.idle":"2024-08-11T16:46:37.621633Z","shell.execute_reply.started":"2024-08-11T16:15:15.246412Z","shell.execute_reply":"2024-08-11T16:46:37.620782Z"},"trusted":true},"execution_count":134,"outputs":[{"name":"stdout","text":"Epoch 1/50\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m272s\u001b[0m 426ms/step - accuracy: 0.9289 - loss: 0.1748 - val_accuracy: 0.9488 - val_loss: 0.1523\nEpoch 2/50\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m271s\u001b[0m 428ms/step - accuracy: 0.9666 - loss: 0.0929 - val_accuracy: 0.9604 - val_loss: 0.1190\nEpoch 3/50\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m268s\u001b[0m 424ms/step - accuracy: 0.9694 - loss: 0.0851 - val_accuracy: 0.9606 - val_loss: 0.1236\nEpoch 4/50\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m267s\u001b[0m 423ms/step - accuracy: 0.9736 - loss: 0.0708 - val_accuracy: 0.9624 - val_loss: 0.1182\nEpoch 5/50\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m268s\u001b[0m 424ms/step - accuracy: 0.9767 - loss: 0.0643 - val_accuracy: 0.9554 - val_loss: 0.1471\nEpoch 6/50\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m268s\u001b[0m 424ms/step - accuracy: 0.9790 - loss: 0.0577 - val_accuracy: 0.9656 - val_loss: 0.1306\nEpoch 7/50\n\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m268s\u001b[0m 424ms/step - accuracy: 0.9840 - loss: 0.0459 - val_accuracy: 0.9632 - val_loss: 0.1650\n","output_type":"stream"}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\nplt.plot(history.history['accuracy'],color='red',label='train')\nplt.plot(history.history['val_accuracy'],color='blue',label='validation')\nplt.legend()\nplt.show()","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":265},"id":"M8c6eVF4sVTT","outputId":"fc469f63-f452-47e8-846a-c8d2223e606e","execution":{"iopub.status.busy":"2024-08-11T17:04:43.872964Z","iopub.execute_input":"2024-08-11T17:04:43.873854Z","iopub.status.idle":"2024-08-11T17:04:44.076747Z","shell.execute_reply.started":"2024-08-11T17:04:43.873815Z","shell.execute_reply":"2024-08-11T17:04:44.075833Z"},"trusted":true},"execution_count":135,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"plt.plot(history.history['loss'],color='red',label='train')\nplt.plot(history.history['val_loss'],color='blue',label='validation')\nplt.legend()\nplt.show()","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":269},"id":"Ap3-7R0swWTo","outputId":"1fb28104-1095-489f-d5cd-8cee052cef7c","execution":{"iopub.status.busy":"2024-08-11T17:04:44.250510Z","iopub.execute_input":"2024-08-11T17:04:44.250890Z","iopub.status.idle":"2024-08-11T17:04:44.434491Z","shell.execute_reply.started":"2024-08-11T17:04:44.250859Z","shell.execute_reply":"2024-08-11T17:04:44.433629Z"},"trusted":true},"execution_count":136,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{"id":"Yb8ocI6PwX-d"},"execution_count":null,"outputs":[]}]}