Horses_vs_humans.ipynb

{
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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Horses vs humans",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nikhilmittal444/horses-or-humans/blob/master/Horses_vs_humans.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "rX8mhOLljYeM"
      },
      "source": [
        "##### Copyright 2019 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "RXZT2UsyIVe_",
        "colab": {}
      },
      "source": [
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/horse-or-human.zip \\\n",
        "    -O /tmp/horse-or-human.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "PLy3pthUS0D2",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import zipfile\n",
        "\n",
        "local_zip = '/tmp/horse-or-human.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/tmp/horse-or-human')\n",
        "zip_ref.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "NR_M9nWN-K8B",
        "colab": {}
      },
      "source": [
        "# Directory with our training horse pictures\n",
        "train_horse_dir = os.path.join('/tmp/horse-or-human/horses')\n",
        "\n",
        "# Directory with our training human pictures\n",
        "train_human_dir = os.path.join('/tmp/horse-or-human/humans')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "HlqN5KbafhLI"
      },
      "source": [
        "Let's find out the total number of horse and human images in the directories:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "H4XHh2xSfgie",
        "colab": {}
      },
      "source": [
        "print('total training horse images:', len(os.listdir(train_horse_dir)))\n",
        "print('total training human images:', len(os.listdir(train_human_dir)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "C3WZABE9eX-8"
      },
      "source": [
        "Now let's take a look at a few pictures to get a better sense of what they look like. First, configure the matplot parameters:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "b2_Q0-_5UAv-",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "\n",
        "# Parameters for our graph; we'll output images in a 4x4 configuration\n",
        "nrows = 4\n",
        "ncols = 4\n",
        "\n",
        "# Index for iterating over images\n",
        "pic_index = 0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "xTvHzGCxXkqp"
      },
      "source": [
        "Now, display a batch of 8 horse and 8 human pictures. You can rerun the cell to see a fresh batch each time:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Wpr8GxjOU8in",
        "colab": {}
      },
      "source": [
        "# Set up matplotlib fig, and size it to fit 4x4 pics\n",
        "fig = plt.gcf()\n",
        "fig.set_size_inches(ncols * 4, nrows * 4)\n",
        "\n",
        "pic_index += 8\n",
        "next_horse_pix = [os.path.join(train_horse_dir, fname) \n",
        "                for fname in train_horse_names[pic_index-8:pic_index]]\n",
        "next_human_pix = [os.path.join(train_human_dir, fname) \n",
        "                for fname in train_human_names[pic_index-8:pic_index]]\n",
        "\n",
        "for i, img_path in enumerate(next_horse_pix+next_human_pix):\n",
        "  # Set up subplot; subplot indices start at 1\n",
        "  sp = plt.subplot(nrows, ncols, i + 1)\n",
        "  sp.axis('Off') # Don't show axes (or gridlines)\n",
        "\n",
        "  img = mpimg.imread(img_path)\n",
        "  plt.imshow(img)\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "qvfZg3LQbD-5",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "PixZ2s5QbYQ3",
        "colab": {}
      },
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "    # Note the input shape is the desired size of the image 300x300 with 3 bytes color\n",
        "    # This is the first convolution\n",
        "    tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(300, 300, 3)),\n",
        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
        "    # The second convolution\n",
        "    tf.keras.layers.Conv2D(32, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    # The third convolution\n",
        "    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    # The fourth convolution\n",
        "    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    # The fifth convolution\n",
        "    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    # Flatten the results to feed into a DNN\n",
        "    tf.keras.layers.Flatten(),\n",
        "    # 512 neuron hidden layer\n",
        "    tf.keras.layers.Dense(512, activation='relu'),\n",
        "    # Only 1 output neuron. It will contain a value from 0-1 where 0 for 1 class ('horses') and 1 for the other ('humans')\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7ZKj8392nbgP",
        "colab": {}
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "8DHWhFP_uhq3",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.optimizers import RMSprop\n",
        "\n",
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer=adam(lr=0.001),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Sn9m9D3UimHM"
      },
      "source": [
        "### Data Preprocessing\n",
        "\n",
        "Let's set up data generators that will read pictures in our source folders, convert them to `float32` tensors, and feed them (with their labels) to our network. We'll have one generator for the training images and one for the validation images. Our generators will yield batches of images of size 300x300 and their labels (binary).\n",
        "\n",
        "As you may already know, data that goes into neural networks should usually be normalized in some way to make it more amenable to processing by the network. (It is uncommon to feed raw pixels into a convnet.) In our case, we will preprocess our images by normalizing the pixel values to be in the `[0, 1]` range (originally all values are in the `[0, 255]` range).\n",
        "\n",
        "In Keras this can be done via the `keras.preprocessing.image.ImageDataGenerator` class using the `rescale` parameter. This `ImageDataGenerator` class allows you to instantiate generators of augmented image batches (and their labels) via `.flow(data, labels)` or `.flow_from_directory(directory)`. These generators can then be used with the Keras model methods that accept data generators as inputs: `fit`, `evaluate_generator`, and `predict_generator`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ClebU9NJg99G",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "# All images will be rescaled by 1./255\n",
        "train_datagen = ImageDataGenerator(rescale=1/255)\n",
        "\n",
        "# Flow training images in batches of 128 using train_datagen generator\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "        '/tmp/horse-or-human/',  # This is the source directory for training images\n",
        "        target_size=(300, 300),  # All images will be resized to 150x150\n",
        "        batch_size=128,\n",
        "        # Since we use binary_crossentropy loss, we need binary labels\n",
        "        class_mode='binary')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "mu3Jdwkjwax4"
      },
      "source": [
        "### Training\n",
        "Let's train for 15 epochs -- this may take a few minutes to run.\n",
        "\n",
        "Do note the values per epoch.\n",
        "\n",
        "The Loss and Accuracy are a great indication of progress of training. It's making a guess as to the classification of the training data, and then measuring it against the known label, calculating the result. Accuracy is the portion of correct guesses. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Fb1_lgobv81m",
        "colab": {}
      },
      "source": [
        "history = model.fit(\n",
        "      train_generator,\n",
        "      steps_per_epoch=8,  \n",
        "      epochs=15,\n",
        "      verbose=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "o6vSHzPR2ghH"
      },
      "source": [
        "###Running the Model\n",
        "\n",
        "Let's now take a look at actually running a prediction using the model. This code will allow you to choose 1 or more files from your file system, it will then upload them, and run them through the model, giving an indication of whether the object is a horse or a human."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "DoWp43WxJDNT",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "from google.colab import files\n",
        "from keras.preprocessing import image\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        " \n",
        "  # predicting images\n",
        "  path = '/content/' + fn\n",
        "  img = image.load_img(path, target_size=(300, 300))\n",
        "  x = image.img_to_array(img)\n",
        "  x = np.expand_dims(x, axis=0)\n",
        "\n",
        "  images = np.vstack([x])\n",
        "  classes = model.predict(images, batch_size=10)\n",
        "  print(classes[0])\n",
        "  if classes[0]>0.5:\n",
        "    print(fn + \" is a human\")\n",
        "  else:\n",
        "    print(fn + \" is a horse\")\n",
        " "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "-8EHQyWGDvWz"
      },
      "source": [
        "### Visualizing Intermediate Representations\n",
        "\n",
        "To get a feel for what kind of features our convnet has learned, one fun thing to do is to visualize how an input gets transformed as it goes through the convnet.\n",
        "\n",
        "Let's pick a random image from the training set, and then generate a figure where each row is the output of a layer, and each image in the row is a specific filter in that output feature map. Rerun this cell to generate intermediate representations for a variety of training images."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-5tES8rXFjux",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import random\n",
        "from tensorflow.keras.preprocessing.image import img_to_array, load_img\n",
        "\n",
        "# Let's define a new Model that will take an image as input, and will output\n",
        "# intermediate representations for all layers in the previous model after\n",
        "# the first.\n",
        "successive_outputs = [layer.output for layer in model.layers[1:]]\n",
        "#visualization_model = Model(img_input, successive_outputs)\n",
        "visualization_model = tf.keras.models.Model(inputs = model.input, outputs = successive_outputs)\n",
        "# Let's prepare a random input image from the training set.\n",
        "horse_img_files = [os.path.join(train_horse_dir, f) for f in train_horse_names]\n",
        "human_img_files = [os.path.join(train_human_dir, f) for f in train_human_names]\n",
        "img_path = random.choice(horse_img_files + human_img_files)\n",
        "\n",
        "img = load_img(img_path, target_size=(300, 300))  # this is a PIL image\n",
        "x = img_to_array(img)  # Numpy array with shape (150, 150, 3)\n",
        "x = x.reshape((1,) + x.shape)  # Numpy array with shape (1, 150, 150, 3)\n",
        "\n",
        "# Rescale by 1/255\n",
        "x /= 255\n",
        "\n",
        "# Let's run our image through our network, thus obtaining all\n",
        "# intermediate representations for this image.\n",
        "successive_feature_maps = visualization_model.predict(x)\n",
        "\n",
        "# These are the names of the layers, so can have them as part of our plot\n",
        "layer_names = [layer.name for layer in model.layers[1:]]\n",
        "\n",
        "# Now let's display our representations\n",
        "for layer_name, feature_map in zip(layer_names, successive_feature_maps):\n",
        "  if len(feature_map.shape) == 4:\n",
        "    # Just do this for the conv / maxpool layers, not the fully-connected layers\n",
        "    n_features = feature_map.shape[-1]  # number of features in feature map\n",
        "    # The feature map has shape (1, size, size, n_features)\n",
        "    size = feature_map.shape[1]\n",
        "    # We will tile our images in this matrix\n",
        "    display_grid = np.zeros((size, size * n_features))\n",
        "    for i in range(n_features):\n",
        "      # Postprocess the feature to make it visually palatable\n",
        "      x = feature_map[0, :, :, i]\n",
        "      x -= x.mean()\n",
        "      x /= x.std()\n",
        "      x *= 64\n",
        "      x += 128\n",
        "      x = np.clip(x, 0, 255).astype('uint8')\n",
        "      # We'll tile each filter into this big horizontal grid\n",
        "      display_grid[:, i * size : (i + 1) * size] = x\n",
        "    # Display the grid\n",
        "    scale = 20. / n_features\n",
        "    plt.figure(figsize=(scale * n_features, scale))\n",
        "    plt.title(layer_name)\n",
        "    plt.grid(False)\n",
        "    plt.imshow(display_grid, aspect='auto', cmap='viridis')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "tuqK2arJL0wo"
      },
      "source": [
        "As you can see we go from the raw pixels of the images to increasingly abstract and compact representations. The representations downstream start highlighting what the network pays attention to, and they show fewer and fewer features being \"activated\"; most are set to zero. This is called \"sparsity.\" Representation sparsity is a key feature of deep learning.\n",
        "\n",
        "\n",
        "These representations carry increasingly less information about the original pixels of the image, but increasingly refined information about the class of the image. You can think of a convnet (or a deep network in general) as an information distillation pipeline."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "j4IBgYCYooGD"
      },
      "source": [
        "## Clean Up\n",
        "\n",
        "Before running the next exercise, run the following cell to terminate the kernel and free memory resources:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "651IgjLyo-Jx",
        "colab": {}
      },
      "source": [
        "import os, signal\n",
        "os.kill(os.getpid(), signal.SIGKILL)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}