Lab7_Solution.ipynb

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      "name": "Lab7_Solution.ipynb",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9_efjnXBr7kN"
      },
      "source": [
        "# ConvNet Architectures\n",
        "The purpose of this lab is to teach to design your own ConvNets from scratch (and implement other people's architectures from research papers).\n",
        "\n",
        "If you instead feel like catching up on transfer learning, you can revisit one of the following:\n",
        "- [Lab2](https://github.com/aivclab/dlcourse/blob/master/Lab2_Solution.ipynb), Task 11 (Keras)\n",
        "- [Lab 4 - part 3](https://github.com/aivclab/dlcourse/blob/master/Lab_4_(part3)_PyTorch_Transfer_Learning.ipynb) (PyTorch).\n",
        "\n",
        "**Remember to enable GPU**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jWsWu8S_zCpw"
      },
      "source": [
        "**NOTE:** In case you have trouble running Keras/TensorFlow in Colab, try one of the following:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pm72GSu1yb6Q"
      },
      "source": [
        "# Try this\n",
        "#!pip install --upgrade tensorflow==1.8.0\n",
        "\n",
        "# ... or this\n",
        "#%tensorflow_version 1.x\n",
        "\n",
        "# Check TensorFlow version\n",
        "#import tensorflow as tf\n",
        "#print(tf.__version__)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FPS1O-4hsuPO"
      },
      "source": [
        "## 1. Download data and train simple ConvNet with Keras\n",
        "First download the CIFAR10 dataset:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nDPpCY5nr6vs",
        "outputId": "d43d9cb4-772f-41e3-901b-1c8b49a3d7ed",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from __future__ import print_function\n",
        "import keras\n",
        "from keras.layers import Input, Conv2D, MaxPooling2D, Dropout, Dense, Flatten\n",
        "from keras.models import Model\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.datasets import cifar10\n",
        "from keras.optimizers import Adam\n",
        "import numpy as np\n",
        "\n",
        "num_classes = 10\n",
        "\n",
        "# Load the CIFAR10 data.\n",
        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
        "\n",
        "# Input image dimensions.\n",
        "input_shape = x_train.shape[1:]\n",
        "\n",
        "# Normalize data.\n",
        "x_train = x_train.astype('float32') / 255\n",
        "x_test = x_test.astype('float32') / 255\n",
        "\n",
        "# Subtract pixel mean (zero-center)\n",
        "x_train_mean = np.mean(x_train, axis=0)\n",
        "x_train -= x_train_mean\n",
        "x_test -= x_train_mean\n",
        "\n",
        "print('x_train shape:', x_train.shape)\n",
        "print(x_train.shape[0], 'train samples')\n",
        "print(x_test.shape[0], 'test samples')\n",
        "print('y_train shape:', y_train.shape)\n",
        "\n",
        "# Convert class vectors to binary class matrices.\n",
        "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
        "y_test = keras.utils.to_categorical(y_test, num_classes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170500096/170498071 [==============================] - 3s 0us/step\n",
            "x_train shape: (50000, 32, 32, 3)\n",
            "50000 train samples\n",
            "10000 test samples\n",
            "y_train shape: (50000, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t0n0Ihg9xOBu"
      },
      "source": [
        "Set up simple ConvNet:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q43HWFfavN3J",
        "outputId": "0a524d6e-94ca-44d8-c8ad-651cc26abad0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "inputs = Input(shape=input_shape)\n",
        "\n",
        "# Encoder (convolutional base)\n",
        "x = Conv2D(8, kernel_size=(3, 3), activation='relu')(inputs)\n",
        "x = MaxPooling2D((2, 2))(x)\n",
        "x = Conv2D(16, kernel_size=(3, 3), activation='relu')(x)\n",
        "x = MaxPooling2D((2, 2))(x)\n",
        "x = Conv2D(32, kernel_size=(3, 3), activation='relu')(x)\n",
        "encoded = Flatten()(x)\n",
        "\n",
        "# Decoder (2 fully connected layers)\n",
        "x = Dense(64, activation='relu')(encoded)\n",
        "x = Dropout(0.5)(x)\n",
        "predictions = Dense(num_classes,activation='softmax')(x)\n",
        "\n",
        "# This creates a callable model that includes the Input layer and the prediction layer\n",
        "model = Model(inputs=inputs, outputs=predictions)\n",
        "\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer=Adam(),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 32, 32, 3)]       0         \n",
            "_________________________________________________________________\n",
            "conv2d (Conv2D)              (None, 30, 30, 8)         224       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 15, 15, 8)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 13, 13, 16)        1168      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 6, 6, 16)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 4, 4, 32)          4640      \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 64)                32832     \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 39,514\n",
            "Trainable params: 39,514\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cHovxkaWxVeJ"
      },
      "source": [
        "... and train (while training you may want to read ahead on Task 1):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PVc45dFixT-b",
        "outputId": "4bdc7353-f520-46f9-f965-1f5714643600",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "batch_size = 32\n",
        "epochs = 4\n",
        "\n",
        "# This will do preprocessing and realtime data augmentation:\n",
        "datagen = ImageDataGenerator(\n",
        "    # set input mean to 0 over the dataset\n",
        "    featurewise_center=False,\n",
        "    # set each sample mean to 0\n",
        "    samplewise_center=False,\n",
        "    # divide inputs by std of dataset\n",
        "    featurewise_std_normalization=False,\n",
        "    # divide each input by its std\n",
        "    samplewise_std_normalization=False,\n",
        "    # apply ZCA whitening\n",
        "    zca_whitening=False,\n",
        "    # epsilon for ZCA whitening\n",
        "    zca_epsilon=1e-06,\n",
        "    # randomly rotate images in the range (deg 0 to 180)\n",
        "    rotation_range=0,\n",
        "    # randomly shift images horizontally\n",
        "    width_shift_range=0.1,\n",
        "    # randomly shift images vertically\n",
        "    height_shift_range=0.1,\n",
        "    # set range for random shear\n",
        "    shear_range=0.,\n",
        "    # set range for random zoom\n",
        "    zoom_range=0.,\n",
        "    # set range for random channel shifts\n",
        "    channel_shift_range=0.,\n",
        "    # set mode for filling points outside the input boundaries\n",
        "    fill_mode='nearest',\n",
        "    # value used for fill_mode = \"constant\"\n",
        "    cval=0.,\n",
        "    # randomly flip images\n",
        "    horizontal_flip=True,\n",
        "    # randomly flip images\n",
        "    vertical_flip=False,\n",
        "    # set rescaling factor (applied before any other transformation)\n",
        "    rescale=None,\n",
        "    # set function that will be applied on each input\n",
        "    preprocessing_function=None,\n",
        "    # image data format, either \"channels_first\" or \"channels_last\"\n",
        "    data_format=None,\n",
        "    # fraction of images reserved for validation (strictly between 0 and 1)\n",
        "    validation_split=0.0)\n",
        "\n",
        "# Compute quantities required for featurewise normalization\n",
        "# (std, mean, and principal components if ZCA whitening is applied).\n",
        "datagen.fit(x_train)\n",
        "\n",
        "# Fit the model on the batches generated by datagen.flow().\n",
        "history = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),\n",
        "                    validation_data=(x_test, y_test),\n",
        "                    epochs=epochs, verbose=1, workers=4,shuffle=True)\n",
        "\n",
        "# Score trained model.\n",
        "scores = model.evaluate(x_test, y_test, verbose=1)\n",
        "print('Test loss:', scores[0])\n",
        "print('Test accuracy:', scores[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-3-7651725cf296>:54: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.fit, which supports generators.\n",
            "Epoch 1/4\n",
            "1563/1563 [==============================] - 34s 22ms/step - loss: 1.7765 - accuracy: 0.3433 - val_loss: 1.4545 - val_accuracy: 0.4698\n",
            "Epoch 2/4\n",
            "1563/1563 [==============================] - 32s 21ms/step - loss: 1.5620 - accuracy: 0.4346 - val_loss: 1.3436 - val_accuracy: 0.5110\n",
            "Epoch 3/4\n",
            "1563/1563 [==============================] - 31s 20ms/step - loss: 1.4695 - accuracy: 0.4702 - val_loss: 1.2549 - val_accuracy: 0.5508\n",
            "Epoch 4/4\n",
            "1563/1563 [==============================] - 31s 20ms/step - loss: 1.4154 - accuracy: 0.4930 - val_loss: 1.2571 - val_accuracy: 0.5487\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 1.2571 - accuracy: 0.5487\n",
            "Test loss: 1.2571250200271606\n",
            "Test accuracy: 0.5486999750137329\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ou1ry0kg3B8-"
      },
      "source": [
        "Plot loss and accuracy curves"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gHvYQsFjyRP3",
        "outputId": "32f9803d-e47d-427c-8a63-57f3132cad36",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def show_history(history):\n",
        "  plt.figure(figsize=(20,6))\n",
        "\n",
        "  # summarize history for accuracy\n",
        "  plt.subplot(121)\n",
        "  plt.plot(history.history['accuracy'])\n",
        "  plt.plot(history.history['val_accuracy'])\n",
        "  plt.title('model accuracy')\n",
        "  plt.ylabel('accuracy')\n",
        "  plt.xlabel('epoch')\n",
        "  plt.legend(['train', 'test'], loc='upper left')\n",
        "\n",
        "  # summarize history for loss\n",
        "  plt.subplot(122)\n",
        "  plt.plot(history.history['loss'])\n",
        "  plt.plot(history.history['val_loss'])\n",
        "  plt.title('model loss')\n",
        "  plt.ylabel('loss')\n",
        "  plt.xlabel('epoch')\n",
        "  plt.legend(['train', 'test'], loc='upper left')\n",
        "  plt.show()\n",
        "\n",
        "show_history(history)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0svLoKzu47CK"
      },
      "source": [
        "###Bonus question (not related to today's topic):\n",
        "- Has this network finished training? Why? Why not?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SmWCy2h8A1H4"
      },
      "source": [
        "###Answer:\n",
        "- Look at the loss curves. The training loss is still going down, which indicates that the model is not done training. On the other the validation loss seems to have stabilized, which could indicate that further training would result in overfitting (validation loss starts going up). In practise, I would run a few more iterations to see if I could get the validation loss a bit further down."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FfZDz-Gy3n-b"
      },
      "source": [
        "##2. Task 1: Write down the network architecture\n",
        "The architecture of AlexNet (including output shapes) can be written in this format (see lecture 7 slides):\n",
        "\n",
        "```\n",
        "[227x227x3] INPUT\n",
        "[55x55x96] CONV1: 96 11x11 filters at stride 4, pad 0\n",
        "[27x27x96] MAX POOL1: 3x3 filters at stride 2\n",
        "[27x27x96] NORM1: Normalization layer\n",
        "[27x27x256] CONV2: 256 5x5 filters at stride 1, pad 2\n",
        "[13x13x256] MAX POOL2: 3x3 filters at stride 2\n",
        "[13x13x256] NORM2: Normalization layer\n",
        "[13x13x384] CONV3: 384 3x3 filters at stride 1, pad 1\n",
        "[13x13x384] CONV4: 384 3x3 filters at stride 1, pad 1\n",
        "[13x13x256] CONV5: 256 3x3 filters at stride 1, pad 1\n",
        "[6x6x256] MAX POOL3: 3x3 filters at stride 2\n",
        "[4096] FC6: 4096 neurons\n",
        "[4096] FC7: 4096 neurons\n",
        "[1000] FC8: 1000 neurons (class scores)\n",
        "```\n",
        "where `[...]` denotets the output shape of the layer."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jUOx8lIK4SL5"
      },
      "source": [
        "Using the same format, write down the architecture of the network you just trained (you also need to specify stride and pad):"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CAcSObtv4jNW"
      },
      "source": [
        "\n",
        "```\n",
        "[???] INPUT\n",
        "[???]\n",
        "...\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xr-VVTTEIkRx",
        "outputId": "b37d4531-871d-4284-c4ee-dfe86a0e847c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 32, 32, 3)]       0         \n",
            "_________________________________________________________________\n",
            "conv2d (Conv2D)              (None, 30, 30, 8)         224       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 15, 15, 8)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 13, 13, 16)        1168      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 6, 6, 16)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 4, 4, 32)          4640      \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 64)                32832     \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 39,514\n",
            "Trainable params: 39,514\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HgeoTQnAJgty"
      },
      "source": [
        "###Solution:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "urBsmOOk5opw"
      },
      "source": [
        "```\n",
        "[32x32x3] INPUT\n",
        "[30x30x8] CONV1: 8 3x3 filters at stride 1, pad 0\n",
        "[15x15x8] MAX POOL1: 2x2 filters at stride 2\n",
        "[13x13x8] CONV2: 16 3x3 filters at stride 1, pad 0\n",
        "[6x6x16] MAX POOL2: 2x2 filters at stride 2\n",
        "[4x4x32] CONV3: 32 3x3 filters at stride 1, pad 0\n",
        "[64] FC1: 64 neurons\n",
        "[10] FC2: 10 neurons (class scores)\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2DK7wIOp7mzP"
      },
      "source": [
        "##3. Task 2: Implement simple network architectures\n",
        "###Task 2.1 Padding for preserving shape\n",
        "Padding (or zero-padding) is mostly used when we want the output of a convolution layer to have the same shape is the input.\n",
        "\n",
        "Keras' Conv2D layer does not allow you to specify the padding directly (as an integer). Rather you can chose between `'valid'` and `'same'`. Check the [documentation](https://keras.io/api/layers/convolution_layers/convolution2d/) to see what the difference is. By changing the padding only, modify our model to have the following architecture. \n",
        "\n",
        "Also fill in the `???` (i.e., what is the pad size?):\n",
        "```\n",
        "[32x32x3] INPUT\n",
        "[32x32x8] CONV1: 8 3x3 filters at stride 1, pad 1\n",
        "[16x16x8] MAX POOL1: 2x2 filters at stride 2\n",
        "[16x16x16] CONV2: 16 3x3 filters at stride 1, pad 1\n",
        "[8x8x16] MAX POOL2: 2x2 filters at stride 2\n",
        "[8x8x32] CONV3: 32 3x3 filters at stride 1, pad 1\n",
        "[64] FC1: 64 neurons\n",
        "[10] FC2: 10 neurons (class scores)\n",
        "```\n",
        "Note that the shape is the same before and after each convolution layer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qI6kUuV_8Mhw",
        "outputId": "d68de829-5d21-4348-c109-10bb3261416b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Encoder (convolutional base)\n",
        "x = Conv2D(8, kernel_size=(3, 3), padding='same', activation='relu')(inputs)\n",
        "x = MaxPooling2D((2, 2))(x)\n",
        "x = Conv2D(16, kernel_size=(3, 3), padding='same', activation='relu')(x)\n",
        "x = MaxPooling2D((2, 2))(x)\n",
        "x = Conv2D(32, kernel_size=(3, 3), padding='same', activation='relu')(x)\n",
        "encoded = Flatten()(x)\n",
        "\n",
        "# Decoder (2 fully connected layers)\n",
        "x = Dense(64, activation='relu')(encoded)\n",
        "x = Dropout(0.5)(x)\n",
        "predictions = Dense(num_classes,activation='softmax')(x)\n",
        "\n",
        "# This creates a callable model that includes the Input layer and the prediction layer\n",
        "model = Model(inputs=inputs, outputs=predictions)\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 32, 32, 3)]       0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 32, 32, 8)         224       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 16, 16, 8)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 16, 16, 16)        1168      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 8, 8, 16)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 8, 8, 32)          4640      \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 64)                131136    \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 137,818\n",
            "Trainable params: 137,818\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tRe-4VZBffMt"
      },
      "source": [
        "**Note:** It is possible to exactly control zero-padding Keras. See [ZeroPadding2D](https://keras.io/api/layers/reshaping_layers/zero_padding2d/)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JGmBgcqcBmxE"
      },
      "source": [
        "###Task 2.2 Stride for downsampling\n",
        "As mentioned in Lecture 6, *max pool* for downsampling is more or less deprecated. Instead, modern network architectures use `stride > 1` in the convolutiton layers for downsampling.\n",
        "\n",
        "Change the stride of the convolution layers to implement the following architecture.\n",
        "\n",
        "Also fill in the `???` (i.e., what is the stride and pad size?):\n",
        "```\n",
        "[32x32x3] INPUT\n",
        "[16x16x8] CONV1: 8 3x3 filters at stride 2, pad 1\n",
        "[8x8x16] CONV2: 16 3x3 filters at stride 2, pad 1\n",
        "[4x4x32] CONV3: 32 3x3 filters at stride 2, pad 1\n",
        "[64] FC1: 64 neurons\n",
        "[10] FC2: 10 neurons (class scores)\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eaeaJYrUJuzh"
      },
      "source": [
        "###Solution:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V4a6ZUSMFNc8",
        "outputId": "c4b991e0-41fc-4ed8-ef62-fab4c392fe7f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Encoder (convolutional base)\n",
        "x = Conv2D(8, kernel_size=(3, 3), strides=2, padding='same', activation='relu')(inputs)\n",
        "x = Conv2D(16, kernel_size=(3, 3), strides=2, padding='same', activation='relu')(x)\n",
        "x = Conv2D(32, kernel_size=(3, 3), strides=2, padding='same', activation='relu')(x)\n",
        "encoded = Flatten()(x)\n",
        "\n",
        "# Decoder (2 fully connected layers)\n",
        "x = Dense(64, activation='relu')(encoded)\n",
        "x = Dropout(0.5)(x)\n",
        "predictions = Dense(num_classes,activation='softmax')(x)\n",
        "\n",
        "# This creates a callable model that includes the Input layer and the prediction layer\n",
        "model = Model(inputs=inputs, outputs=predictions)\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 32, 32, 3)]       0         \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 16, 16, 8)         224       \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 8, 8, 16)          1168      \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 4, 4, 32)          4640      \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 64)                32832     \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 39,514\n",
            "Trainable params: 39,514\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VMyfNVqiI8RQ"
      },
      "source": [
        "###Task 2.3: Batch normalization\n",
        "Always use batch normalization! It reduces the risk of vanishing gradients (i.e., it ensures that we have gradient flow through the whole network) and makes training faster.\n",
        "\n",
        "Batch normalization is performed after convolution, but before activation.\n",
        "\n",
        "To implement the architecture below you will need to separate the ReLU from the convolution layer. That is, instead of\n",
        "\n",
        "```\n",
        "x = Conv2D(16, kernel_size=(3, 3), activation='relu')(x)\n",
        "```\n",
        "\n",
        "write\n",
        "\n",
        "```\n",
        "x = Conv2D(16, kernel_size=(3, 3))(x)\n",
        "x = Activation('relu')(x)\n",
        "```\n",
        "\n",
        "Required documentation:\n",
        "- https://keras.io/activations/\n",
        "- https://keras.io/layers/normalization/\n",
        "\n",
        "Your task is to implement this architecture, where I have written out batch normalization (BN) and ReLU activation explicitly:\n",
        "\n",
        "```\n",
        "[32x32x3] INPUT\n",
        "[16x16x8] CONV1: 8 3x3 filters at stride 2, pad 1\n",
        "[16x16x8] BN1: Batch normalization\n",
        "[16x16x8] ReLU\n",
        "[8x8x16] CONV2: 16 3x3 filters at stride 2, pad 1\n",
        "[8x8x16] BN2: Batch normalization\n",
        "[8x8x16] ReLU\n",
        "[4x4x32] CONV3: 32 3x3 filters at stride 2, pad 1\n",
        "[4x4x32] BN3: Batch normalization\n",
        "[4x4x32] ReLU\n",
        "[64] FC1: 64 neurons\n",
        "[10] FC2: 10 neurons (class scores)\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pEut1pW_Jrl6"
      },
      "source": [
        "###Solution:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1zxxMAC074cA",
        "outputId": "e8a71636-c459-4efa-f0d6-d5102c2d0662",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from keras.layers import BatchNormalization, Activation\n",
        "\n",
        "# Encoder (convolutional base)\n",
        "x = Conv2D(8, kernel_size=(3, 3), strides=2, padding='same')(inputs)\n",
        "x = BatchNormalization()(x)\n",
        "x = Activation('relu')(x)\n",
        "x = Conv2D(16, kernel_size=(3, 3), strides=2, padding='same')(x)\n",
        "x = BatchNormalization()(x)\n",
        "x = Activation('relu')(x)\n",
        "x = Conv2D(32, kernel_size=(3, 3), strides=2, padding='same')(x)\n",
        "x = BatchNormalization()(x)\n",
        "x = Activation('relu')(x)\n",
        "encoded = Flatten()(x)\n",
        "\n",
        "# Decoder (2 fully connected layers)\n",
        "x = Dense(64, activation='relu')(encoded)\n",
        "x = Dropout(0.5)(x)\n",
        "predictions = Dense(num_classes,activation='softmax')(x)\n",
        "\n",
        "# This creates a callable model that includes the Input layer and the prediction layer\n",
        "model = Model(inputs=inputs, outputs=predictions)\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_7\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 32, 32, 3)]       0         \n",
            "_________________________________________________________________\n",
            "conv2d_9 (Conv2D)            (None, 16, 16, 8)         224       \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 16, 16, 8)         32        \n",
            "_________________________________________________________________\n",
            "activation (Activation)      (None, 16, 16, 8)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_10 (Conv2D)           (None, 8, 8, 16)          1168      \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 8, 8, 16)          64        \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 8, 8, 16)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_11 (Conv2D)           (None, 4, 4, 32)          4640      \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 4, 4, 32)          128       \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 4, 4, 32)          0         \n",
            "_________________________________________________________________\n",
            "flatten_3 (Flatten)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 64)                32832     \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 39,738\n",
            "Trainable params: 39,626\n",
            "Non-trainable params: 112\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p5pvJ3vjNTtF"
      },
      "source": [
        "###Task 2.4: Global average pooling\n",
        "Recall that global average pooling takes an NxNxC volume and reduces it to a C-dimensional vector by averaging the NxN valus of each channel (c).\n",
        "\n",
        "Thus, we can replace the `Flatten` layer and the first `Dense` layer (i.e., fully connected layer) with a global average pooling layer.\n",
        "\n",
        "Check the documentation here:\n",
        "https://keras.io/api/layers/pooling_layers/global_average_pooling2d/\n",
        "\n",
        "Your task is to implement the following architecture:\n",
        "\n",
        "```\n",
        "[32x32x3] INPUT\n",
        "[16x16x8] CONV1: 8 3x3 filters at stride 2, pad 1\n",
        "[8x8x16] CONV2: 16 3x3 filters at stride 2, pad 1\n",
        "[4x4x32] CONV3: 32 3x3 filters at stride 2, pad 1\n",
        "[32] GAP: Global average pooling\n",
        "[10] FC2: 10 neurons (class scores)\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i6yhfEMNJz1L"
      },
      "source": [
        "###Solution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5sSIJpdNLk4a",
        "outputId": "a8a0b9a3-a7fb-493b-d86e-8ed305f70f97",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from keras.layers import GlobalAveragePooling2D\n",
        "\n",
        "# Encoder (convolutional base)\n",
        "x = Conv2D(8, kernel_size=(3, 3), strides=2, padding='same', activation='relu')(inputs)\n",
        "x = Conv2D(16, kernel_size=(3, 3), strides=2, padding='same', activation='relu')(x)\n",
        "x = Conv2D(32, kernel_size=(3, 3), strides=2, padding='same', activation='relu')(x)\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "\n",
        "# Decoder (1 fully connected layer)\n",
        "predictions = Dense(num_classes,activation='softmax')(x)\n",
        "\n",
        "# This creates a callable model that includes the Input layer and the prediction layer\n",
        "model = Model(inputs=inputs, outputs=predictions)\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_9\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 32, 32, 3)]       0         \n",
            "_________________________________________________________________\n",
            "conv2d_12 (Conv2D)           (None, 16, 16, 8)         224       \n",
            "_________________________________________________________________\n",
            "conv2d_13 (Conv2D)           (None, 8, 8, 16)          1168      \n",
            "_________________________________________________________________\n",
            "conv2d_14 (Conv2D)           (None, 4, 4, 32)          4640      \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d (Gl (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 10)                330       \n",
            "=================================================================\n",
            "Total params: 6,362\n",
            "Trainable params: 6,362\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "seRuB8D_QrAp"
      },
      "source": [
        "**Note** that because we removed a dense layer, the above model looses some of its original capacity, so it might perform worse on the validation set. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7YcyGCORRUXB"
      },
      "source": [
        "###Task 2.6: Variable input size\n",
        "The really cool thing about global average pooling is that it allows the network to handle input images of variable size.\n",
        "\n",
        "Consider the following piece of code (it's supposed to crash...):\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NErawUzCU81q",
        "outputId": "11a1c154-eae0-45ed-8f82-41dbf71813a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        }
      },
      "source": [
        "# Tell Keras that height/width can be arbitrary\n",
        "variable_input_shape = ((None,None,3)) \n",
        "variable_inputs = Input(variable_input_shape)\n",
        "\n",
        "# Encoder (convolutional base)\n",
        "x = Conv2D(8, kernel_size=(3, 3), activation='relu')(variable_inputs)\n",
        "x = MaxPooling2D((2, 2))(x)\n",
        "x = Conv2D(16, kernel_size=(3, 3), activation='relu')(x)\n",
        "x = MaxPooling2D((2, 2))(x)\n",
        "x = Conv2D(32, kernel_size=(3, 3), activation='relu')(x)\n",
        "encoded = Flatten()(x)\n",
        "\n",
        "# Decoder (2 fully connected layers)\n",
        "x = Dense(64, activation='relu')(encoded)\n",
        "x = Dropout(0.5)(x)\n",
        "predictions = Dense(num_classes,activation='softmax')(x)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-a2afe570572a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;31m# Decoder (2 fully connected layers)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDense\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'relu'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mencoded\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDense\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_classes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mactivation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'softmax'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    924\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_in_functional_construction_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    925\u001b[0m       return self._functional_construction_call(inputs, args, kwargs,\n\u001b[0;32m--> 926\u001b[0;31m                                                 input_list)\n\u001b[0m\u001b[1;32m    927\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    928\u001b[0m     \u001b[0;31m# Maintains info about the `Layer.call` stack.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m_functional_construction_call\u001b[0;34m(self, inputs, args, kwargs, input_list)\u001b[0m\n\u001b[1;32m   1096\u001b[0m         \u001b[0;31m# Build layer if applicable (if the `build` method has been\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1097\u001b[0m         \u001b[0;31m# overridden).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1098\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_build\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1099\u001b[0m         \u001b[0mcast_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m_maybe_build\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2641\u001b[0m         \u001b[0;31m# operations.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2642\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mtf_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_init_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2643\u001b[0;31m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_shapes\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint:disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2644\u001b[0m       \u001b[0;31m# We must set also ensure that the layer is marked as built, and the build\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2645\u001b[0m       \u001b[0;31m# shape is stored since user defined build functions may not be calling\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/core.py\u001b[0m in \u001b[0;36mbuild\u001b[0;34m(self, input_shape)\u001b[0m\n\u001b[1;32m   1166\u001b[0m     \u001b[0mlast_dim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtensor_shape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdimension_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_shape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1167\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlast_dim\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1168\u001b[0;31m       raise ValueError('The last dimension of the inputs to `Dense` '\n\u001b[0m\u001b[1;32m   1169\u001b[0m                        'should be defined. Found `None`.')\n\u001b[1;32m   1170\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_spec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mInputSpec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin_ndim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlast_dim\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: The last dimension of the inputs to `Dense` should be defined. Found `None`."
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UyKohUFBbTb4"
      },
      "source": [
        "What goes wrong is that the Flatten layer must know the shape of the input `x` in advance. Now, lets replace it with a global average pooling layer and see what happens:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UAsH3NA1bo3l",
        "outputId": "3c6ec756-2f4b-4803-8dab-2b70eb3028ec",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Tell Keras that height/width can be arbitrary\n",
        "variable_input_shape = ((None,None,3)) \n",
        "variable_inputs = Input(variable_input_shape)\n",
        "\n",
        "# Encoder (convolutional base)\n",
        "x = Conv2D(8, kernel_size=(3, 3), activation='relu')(variable_inputs)\n",
        "x = MaxPooling2D((2, 2))(x)\n",
        "x = Conv2D(16, kernel_size=(3, 3), activation='relu')(x)\n",
        "x = MaxPooling2D((2, 2))(x)\n",
        "x = Conv2D(32, kernel_size=(3, 3), activation='relu')(x)\n",
        "encoded = GlobalAveragePooling2D()(x)\n",
        "\n",
        "# Decoder (2 fully connected layers)\n",
        "x = Dense(64, activation='relu')(encoded)\n",
        "x = Dropout(0.5)(x)\n",
        "predictions = Dense(num_classes,activation='softmax')(x)\n",
        "# This creates a callable model that includes the Input layer and the prediction layer\n",
        "\n",
        "model = Model(inputs=variable_inputs, outputs=predictions)\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_11\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_3 (InputLayer)         [(None, None, None, 3)]   0         \n",
            "_________________________________________________________________\n",
            "conv2d_18 (Conv2D)           (None, None, None, 8)     224       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2 (None, None, None, 8)     0         \n",
            "_________________________________________________________________\n",
            "conv2d_19 (Conv2D)           (None, None, None, 16)    1168      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2 (None, None, None, 16)    0         \n",
            "_________________________________________________________________\n",
            "conv2d_20 (Conv2D)           (None, None, None, 32)    4640      \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d_1 ( (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 64)                2112      \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 8,794\n",
            "Trainable params: 8,794\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "De6l35Dib-lB"
      },
      "source": [
        "Now, we can run the network on images of arbitrary shape:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NTs3Q-OGRVww",
        "outputId": "0e82708f-7522-4ae2-f834-ba12fd39425f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Make a batch of images that have size 36x36 instead of 32x32\n",
        "import cv2\n",
        "batch = x_train[0:batch_size,:]\n",
        "batch_resized = np.zeros((batch_size,36,36,3))\n",
        "for i in range(batch_size):\n",
        "  batch_resized[i,:,:,:] = cv2.resize(batch[i,:,:,:],(36,36))\n",
        "\n",
        "# Shape 32x32\n",
        "predictions = model.predict(batch)\n",
        "print(predictions.shape)\n",
        "\n",
        "# Shape 36x36\n",
        "predictions = model.predict(batch_resized)\n",
        "print(predictions.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(32, 10)\n",
            "(32, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HNcuPRhFdRJO"
      },
      "source": [
        "####Question\n",
        "There is a small caveat, however. The network can only handle input images down to a certain shape (height/width).\n",
        "- Why?\n",
        "- What is the smallest input width/height this model can handle? "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CYXekx1mk4tN"
      },
      "source": [
        "###Answer\n",
        "If you set the height/width to 18, it still works. The output shape of the last convolution layer becomes 1x1x32. If you set the height/width lower than 18, the calculated output dimensions of that layer become zero or negative. So 18 is the minimum height/width according to my experiments :-)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GQjMrrEKjQjo",
        "outputId": "a59bed74-5f76-4063-dc9d-d37ca4ec0952",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Tell Keras that height/width can be arbitrary\n",
        "variable_input_shape = ((18,18,3)) \n",
        "variable_inputs = Input(variable_input_shape)\n",
        "\n",
        "# Encoder (convolutional base)\n",
        "x = Conv2D(8, kernel_size=(3, 3), activation='relu')(variable_inputs)\n",
        "x = MaxPooling2D((2, 2))(x)\n",
        "x = Conv2D(16, kernel_size=(3, 3), activation='relu')(x)\n",
        "x = MaxPooling2D((2, 2))(x)\n",
        "x = Conv2D(32, kernel_size=(3, 3), activation='relu')(x)\n",
        "encoded = GlobalAveragePooling2D()(x)\n",
        "\n",
        "# Decoder (2 fully connected layers)\n",
        "x = Dense(64, activation='relu')(encoded)\n",
        "x = Dropout(0.5)(x)\n",
        "predictions = Dense(num_classes,activation='softmax')(x)\n",
        "# This creates a callable model that includes the Input layer and the prediction layer\n",
        "\n",
        "model = Model(inputs=variable_inputs, outputs=predictions)\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_13\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_4 (InputLayer)         [(None, 18, 18, 3)]       0         \n",
            "_________________________________________________________________\n",
            "conv2d_21 (Conv2D)           (None, 16, 16, 8)         224       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_8 (MaxPooling2 (None, 8, 8, 8)           0         \n",
            "_________________________________________________________________\n",
            "conv2d_22 (Conv2D)           (None, 6, 6, 16)          1168      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_9 (MaxPooling2 (None, 3, 3, 16)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_23 (Conv2D)           (None, 1, 1, 32)          4640      \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d_2 ( (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 64)                2112      \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 8,794\n",
            "Trainable params: 8,794\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n3h5lMG4O3Aw"
      },
      "source": [
        "##Task 3: Implement building blocks of advanced network architectures\n",
        "###Task 3.1 Naive Inception module\n",
        "The simple, naive inception module combines 1x1, 3x3, and 5x5 convolution, as well as max pooling, into a single *layer* or *module*. It does so by concatenating along the channel dimension.\n",
        "\n",
        "Suppose the input to the module (called `x`) has shape `NxNxC`. Then the outputs of the convolutions should be:\n",
        "\n",
        "- `NxNxf1` for `conv1` (1x1 convolution), where `f1` is the number of 1x1 filters.\n",
        "- `NxNxf3` for `conv3` (3x3 convolution), where `f3` is the number of 3x3 filters.\n",
        "- `NxNxf5` for `conv5` (5x5 convolution), where `f5` is the number of 5x5 filters.\n",
        "\n",
        "and the output of the max pooling layer should be\n",
        "\n",
        "- `NxNxC` for `pool` (3x3 max pooling)\n",
        "\n",
        "You can then use Keras' `Concatenate`layer to combine the outputs. The shape of the output should be `NxNx(f1+f3+f5+C)`.\n",
        "\n",
        "Below is a template for implementing and testing a naive inception module. Your task is to fill in the empty gaps. The output should look like this:\n",
        "\n",
        "![alt text](https://github.com/aivclab/dlcourse/blob/master/data/inception_naive.png?raw=true)\n",
        "\n",
        "**NOTE 1** that the `InputLayer` is *not* part of the inception module, and neither is the first `Conv2D` layer. The first `Conv2D` layer just serves to generate an input feature map with a \"nice\" shape, meaning that the dimensions are powers of 2 (in this case `16x16x64`).\n",
        "\n",
        "If you need to consult the documentation, it is here: [Conv2d](https://keras.io/api/layers/convolution_layers/convolution2d/) [MaxPooling2D](https://keras.io/api/layers/pooling_layers/max_pooling2d/).\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-2SUTxkeMeQ6"
      },
      "source": [
        "from keras.layers import Concatenate\n",
        "from keras.utils import plot_model\n",
        "\n",
        "# Naive inception module\n",
        "def naive_inception_module(x, f1=64, f3=128, f5=32):\n",
        "  # 1x1 conv\n",
        "  conv1 = Conv2D(filters=???, kernel_size=???, padding=???, activation='relu')(x)\n",
        "  # 3x3 conv\n",
        "  conv3 = Conv2D(filters=???, kernel_size=???, padding=???, activation='relu')(x)\n",
        "  # 5x5 conv\n",
        "  conv5 = Conv2D(filters=???, kernel_size=???, padding=???, activation='relu')(x)\n",
        "  # 3x3 max pooling\n",
        "  pool = MaxPooling2D(pool_size=(3,3), strides=???, padding=???)(x)\n",
        "  # concatenate filters, assumes filters/channels last\n",
        "  y = Concatenate()([conv1, conv3, conv5, pool])\n",
        "\n",
        "  return y\n",
        "\n",
        "# Prior convolution(s) - not part of the inception module:\n",
        "# - inputs has shape 32x32x3\n",
        "# - x has shape 16x16x64 and serves as input to the inception module\n",
        "x = Conv2D(64, kernel_size=(3, 3), strides=2, padding='same',activation='relu')(inputs)\n",
        "\n",
        "# Add inception block 1\n",
        "layer = naive_inception_module(x)\n",
        "\n",
        "# create model\n",
        "model = Model(inputs=inputs, outputs=layer)\n",
        "model.summary()\n",
        "\n",
        "# plot model architecture\n",
        "plot_model(model, show_shapes=True, to_file='inception_module.png')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "epAiJhRUJ-0v"
      },
      "source": [
        "###Solution:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nPfqCJQFMK6I",
        "outputId": "61b5f4ff-09af-4e57-c210-23c2e6075d25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 721
        }
      },
      "source": [
        "from keras.layers import Concatenate\n",
        "from keras.utils import plot_model\n",
        "\n",
        "def naive_inception_module(x, f1=64, f3=128, f5=32):\n",
        "  # 1x1 conv\n",
        "  conv1 = Conv2D(filters=f1, kernel_size=(1,1), padding='same', activation='relu')(x)\n",
        "  # 3x3 conv\n",
        "  conv3 = Conv2D(filters=f3, kernel_size=(3,3), padding='same', activation='relu')(x)\n",
        "  # 5x5 conv\n",
        "  conv5 = Conv2D(filters=f5, kernel_size=(5,5), padding='same', activation='relu')(x)\n",
        "  # 3x3 max pooling\n",
        "  pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same')(x)\n",
        "  # concatenate filters, assumes filters/channels last\n",
        "  y = Concatenate()([conv1, conv3, conv5, pool])\n",
        "  return y\n",
        "\n",
        "# Prior convolution(s) - not part of the inception module:\n",
        "# - inputs has shape 32x32x3\n",
        "# - x has shape 16x16x64 and serves as input to the inception module\n",
        "x = Conv2D(64, kernel_size=(3, 3), strides=2, padding='same',activation='relu')(inputs)\n",
        "\n",
        "# Add inception block 1\n",
        "layer = naive_inception_module(x)\n",
        "\n",
        "# create model\n",
        "model = Model(inputs=inputs, outputs=layer)\n",
        "model.summary()\n",
        "\n",
        "# plot model architecture\n",
        "plot_model(model, show_shapes=True, to_file='inception_module.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_15\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 32, 32, 3)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_24 (Conv2D)              (None, 16, 16, 64)   1792        input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_25 (Conv2D)              (None, 16, 16, 64)   4160        conv2d_24[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_26 (Conv2D)              (None, 16, 16, 128)  73856       conv2d_24[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_27 (Conv2D)              (None, 16, 16, 32)   51232       conv2d_24[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_10 (MaxPooling2D) (None, 16, 16, 64)   0           conv2d_24[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate (Concatenate)       (None, 16, 16, 288)  0           conv2d_25[0][0]                  \n",
            "                                                                 conv2d_26[0][0]                  \n",
            "                                                                 conv2d_27[0][0]                  \n",
            "                                                                 max_pooling2d_10[0][0]           \n",
            "==================================================================================================\n",
            "Total params: 131,040\n",
            "Trainable params: 131,040\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HJ4U5fdFuvvK"
      },
      "source": [
        "###Question:\n",
        "- What is the number of trainable parameters in the simple model above?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tbJecuOQ5O6n"
      },
      "source": [
        "###Answer:\n",
        "- 131,040 according to the summary table"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YwLjVu5qu_VA"
      },
      "source": [
        "###Task 3.2 Inception module\n",
        "The version of the inception module that we have implemented is called the *naive inception module*.\n",
        "\n",
        "A modification to the module was made in order to reduce the amount of computation required. Specifically, 1×1 convolutional layers were added to reduce the number of filters before the 3×3 and 5×5 convolutional layers, and to decrease the number of channels after the pooling layer.\n",
        "\n",
        "1×1 convolutions are used to compute reductions **before** the expensive 3×3 and 5×5 convolutions. Besides being used as reductions, they also include the use of rectified linear activation making them dual-purpose. In addition, a 1x1 convolution is added **after** the max pooling layer to reduce dimensionality.\n",
        "\n",
        "Suppose the input to the module (called `x`) has shape `NxNxC`. Then the outputs of the convolutions should be:\n",
        "\n",
        "- `NxNxf1` for `conv1` (1x1 convolution), where `f1` is the number of 1x1 filters.\n",
        "- `NxNxf3_reduce` for `conv3_reduce` (1x1 convolution), where `f3_reduce` is the number of 1x1 filters.\n",
        "- `NxNxf3` for `conv3` (3x3 convolution), where `f3` is the number of 3x3 filters. Note that the input to `conv3` is not `x`, but `conv3_reduce`.\n",
        "- `NxNxf5_reduce` for `conv5_reduce` (1x1 convolution), where `f5_reduce` is the number of 1x1 filters.\n",
        "- `NxNxf5` for `conv5` (5x5 convolution), where `f5` is the number of 5x5 filters. Note that the input to `conv5` is not `x`, but `conv5_reduce`.\n",
        "\n",
        "and the output of the max pooling layer should be\n",
        "\n",
        "- `NxNxC` for `pool` (3x3 max pooling)\n",
        "- `NxNxfpool_reduce` for `pool_reduce` (1x1 convolution), where `fpool_reduce` is the number of 1x1 filters. Note that the input to `pool_reduce` is not `x`, but `pool`.\n",
        "\n",
        "Below is a template for implementing and testing an inception module. Your task is to fill in the empty gaps. The output should look like this:\n",
        "\n",
        "![alt text](https://github.com/aivclab/dlcourse/blob/master/data/inception.png?raw=true)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "93xKdv6SyV6B"
      },
      "source": [
        "def inception_module(x, f1=64, f3=128, f5=32, f3_reduce=32, f5_reduce=32, fpool_reduce=32):\n",
        "  # 1x1 conv\n",
        "  conv1 = Conv2D(filters=???, kernel_size=???, padding=???, activation='relu')(x)\n",
        "  # 3x3 conv\n",
        "  conv3_reduce = Conv2D(filters=???, kernel_size=???, padding=???, activation='relu')(x)\n",
        "  conv3 = Conv2D(filters=???, kernel_size=???, padding=???, activation='relu')(conv3_reduce)\n",
        "  # 5x5 conv\n",
        "  conv5_reduce = Conv2D(filters=???, kernel_size=???, padding=???, activation='relu')(x)\n",
        "  conv5 = Conv2D(filters=???, kernel_size=???, padding=???, activation='relu')(conv5_reduce)\n",
        "  # 3x3 max pooling\n",
        "  pool = MaxPooling2D(pool_size=(3,3), strides=???, padding='same')(x)\n",
        "  pool_reduce = Conv2D(filters=???, kernel_size=???, padding='same', activation='relu')(pool)\n",
        "  # concatenate filters, assumes filters/channels last\n",
        "  y = Concatenate()([conv1, conv3, conv5, pool_reduce])\n",
        "  return y\n",
        "\n",
        "# Prior convolution(s) - not part of the inception module\n",
        "x = Conv2D(64, kernel_size=(3, 3), strides=2, padding='same',activation='relu')(inputs)\n",
        "\n",
        "# Add inception block\n",
        "layer = inception_module(x)\n",
        "\n",
        "# create model\n",
        "model = Model(inputs=inputs, outputs=layer)\n",
        "model.summary()\n",
        "\n",
        "# plot model architecture\n",
        "plot_model(model, show_shapes=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ihty3Ds8yWO8"
      },
      "source": [
        "###Question:\n",
        "- What is the number of trainable parameters in the model above?\n",
        "- What is the shape of the output? Compare to *naive* inception module."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lz_AX-fV5caZ"
      },
      "source": [
        "###Answers:\n",
        "- 74,816 according to the summary table below\n",
        "- It is `16x16x256` vs `16x16x228` for the *naive* inception module. The reduction in number of channels comes from the 1x1x convolution performed after the max pooling layer."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-D5nFEpXKM5b"
      },
      "source": [
        "###Solution:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r8QhDbBKNct2",
        "outputId": "28211337-00f6-4d29-ef81-ec68fc79180b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 925
        }
      },
      "source": [
        "def inception_module(x, f1=64, f3=128, f5=32, f3_reduce=32, f5_reduce=32, fpool_reduce=32):\n",
        "  # 1x1 conv\n",
        "  conv1 = Conv2D(filters=f1, kernel_size=(1,1), padding='same', activation='relu')(x)\n",
        "  # 3x3 conv\n",
        "  conv3_reduce = Conv2D(filters=f3_reduce, kernel_size=(1,1), padding='same', activation='relu')(x)\n",
        "  conv3 = Conv2D(filters=f3, kernel_size=(3,3), padding='same', activation='relu')(conv3_reduce)\n",
        "  # 5x5 conv\n",
        "  conv5_reduce = Conv2D(filters=f5_reduce, kernel_size=(1,1), padding='same', activation='relu')(x)\n",
        "  conv5 = Conv2D(filters=f5, kernel_size=(5,5), padding='same', activation='relu')(conv5_reduce)\n",
        "  # 3x3 max pooling\n",
        "  pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same')(x)\n",
        "  pool_reduce = Conv2D(filters=fpool_reduce, kernel_size=(1,1), padding='same', activation='relu')(pool)\n",
        "  # concatenate filters, assumes filters/channels last\n",
        "  y = Concatenate()([conv1, conv3, conv5, pool_reduce])\n",
        "  return y\n",
        "\n",
        "# Prior convolution(s) - not part of the inception module\n",
        "x = Conv2D(64, kernel_size=(3, 3), strides=2, padding='same',activation='relu')(inputs)\n",
        "\n",
        "# Add inception block\n",
        "layer = inception_module(x)\n",
        "\n",
        "# create model\n",
        "model = Model(inputs=inputs, outputs=layer)\n",
        "model.summary()\n",
        "\n",
        "# plot model architecture\n",
        "plot_model(model, show_shapes=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_19\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 32, 32, 3)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_35 (Conv2D)              (None, 16, 16, 64)   1792        input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_37 (Conv2D)              (None, 16, 16, 32)   2080        conv2d_35[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_39 (Conv2D)              (None, 16, 16, 32)   2080        conv2d_35[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_12 (MaxPooling2D) (None, 16, 16, 64)   0           conv2d_35[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_36 (Conv2D)              (None, 16, 16, 64)   4160        conv2d_35[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_38 (Conv2D)              (None, 16, 16, 128)  36992       conv2d_37[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_40 (Conv2D)              (None, 16, 16, 32)   25632       conv2d_39[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_41 (Conv2D)              (None, 16, 16, 32)   2080        max_pooling2d_12[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_2 (Concatenate)     (None, 16, 16, 256)  0           conv2d_36[0][0]                  \n",
            "                                                                 conv2d_38[0][0]                  \n",
            "                                                                 conv2d_40[0][0]                  \n",
            "                                                                 conv2d_41[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 74,816\n",
            "Trainable params: 74,816\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qm9IN-zzy5EF"
      },
      "source": [
        "###Task 3.3 ResNet block\n",
        "A key innovation in the ResNet paper was the residual block. The residual block, specifically the identity residual model, is a block of two convolutional layers with the same number of filters and a small filter size, where the output of the second layer is added with the input to the first convolutional layer. Drawn as a graph, the input to the module is added to the output of the module and is called a shortcut connection:\n",
        "\n",
        "We can implement this directly in Keras using the functional API and the add() merge function:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j-C_XeytRgMw",
        "outputId": "3d44b6c6-43c0-45e7-cfca-af1c0b07ee7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        }
      },
      "source": [
        "from keras.layers import Add\n",
        "\n",
        "def residual_module_type1(x, f=64):\n",
        "\t# conv1\n",
        "\tconv1 = Conv2D(f, (3,3), padding='same', activation='relu', kernel_initializer='he_normal')(x)\n",
        "\t# conv2\n",
        "\tconv2 = Conv2D(f, (3,3), padding='same', activation='linear', kernel_initializer='he_normal')(conv1)\n",
        "\t# add filters, assumes filters/channels last\n",
        "\ty = Add()([conv2, x])\n",
        "\t# activation function\n",
        "\ty = Activation('relu')(y)\n",
        "\treturn y\n",
        "\n",
        "# Prior convolution(s) - not part of the inception module\n",
        "x = Conv2D(64, kernel_size=(3, 3), strides=2, padding='same',activation='relu')(inputs)\n",
        "\n",
        "# Add inception block\n",
        "layer = residual_module_type1(x)\n",
        "\n",
        "# create model\n",
        "model = Model(inputs=inputs, outputs=layer)\n",
        "\n",
        "# plot model architecture\n",
        "plot_model(model, show_shapes=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8lEUi1uB1xHv"
      },
      "source": [
        "The **Type 1** block above assumes that input `x` and output `y` have exactly the same shape. If this is not the case, we will get an error.\n",
        "\n",
        "The **Type 2** block serves to\n",
        "- downsample the input by a factor of two (along height and width dimensions)\n",
        "- double the number of channels\n",
        "\n",
        "Thus, if the input shape is `x.shape = N x N x C`, the output shape is `y.shape = N/2 x N/2 x C*2`.\n",
        "\n",
        "In the top figure below, the solid skip connections mark Type 1 blocks (shape preserved), and dashed skip connections mark Type 2 blocks (height/width halfed and channels doubled):\n",
        "\n",
        "![alt text](https://miro.medium.com/max/1372/1*6hF97Upuqg_LdsqWY6n_wg.png)\n",
        "\n",
        "One solution to implementing a Type 2 block is as follows:\n",
        "- `conv1`: Apply convolution on input `x` with a stride of 2 and `padding=same`. This has the effect of reducing the height and width by a factor of 2.\n",
        "- `conv2`: Apply convolution on output of `conv1` with a stride of 1 to preserve height/width.\n",
        "- `x_reshape`: This is the shortcut connection that must assure that `x` is reshaped to half the width/height. This can be done by applying a 1×1 convolution with a stride of 2 (before the addition).\n",
        "\n",
        "Your task is to fill in the blanks here:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mvuju9zZKXUX"
      },
      "source": [
        "###Solution:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I-DtxmucRQC1",
        "outputId": "500e100b-52dd-4210-c269-add1d7ebd6b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "def residual_module_type2(x, f=64):\n",
        "\t# conv1 (reduces height/width by a factor of two and doubles the number of filters)\n",
        "\tconv1 = Conv2D(filters=2*f, kernel_size=(3,3), strides=2, padding='same', activation='relu', kernel_initializer='he_normal')(x)\n",
        "\t# conv2\n",
        "\tconv2 = Conv2D(filters=2*f, kernel_size=(3,3), padding='same', activation='linear', kernel_initializer='he_normal')(conv1)\n",
        "  # reshape x (downsample x by factor 2 and double number of filters)\n",
        "\tx_reshape = Conv2D(filters=2*f,kernel_size=(1,1), strides=2, padding='same',activation='linear')(x)\n",
        "\t# add filters, assumes filters/channels last\n",
        "\ty = Add()([conv2, x_reshape])\n",
        "\t# activation function\n",
        "\ty = Activation('relu')(y)\n",
        "\treturn y\n",
        "\n",
        "# Prior convolution(s) - not part of the residual module\n",
        "x = Conv2D(64, kernel_size=(3, 3), strides=2, padding='same',activation='relu')(inputs)\n",
        "\n",
        "# Add residual module\n",
        "layer = residual_module_type2(x)\n",
        "\n",
        "# create model\n",
        "model = Model(inputs=inputs, outputs=layer)\n",
        "model.summary()\n",
        "\n",
        "# plot model architecture\n",
        "plot_model(model, show_shapes=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_23\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 32, 32, 3)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_45 (Conv2D)              (None, 16, 16, 64)   1792        input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_46 (Conv2D)              (None, 8, 8, 128)    73856       conv2d_45[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_47 (Conv2D)              (None, 8, 8, 128)    147584      conv2d_46[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_48 (Conv2D)              (None, 8, 8, 128)    8320        conv2d_45[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_1 (Add)                     (None, 8, 8, 128)    0           conv2d_47[0][0]                  \n",
            "                                                                 conv2d_48[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, 8, 8, 128)    0           add_1[0][0]                      \n",
            "==================================================================================================\n",
            "Total params: 231,552\n",
            "Trainable params: 231,552\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gTDnmjL4Famo"
      },
      "source": [
        "###Task 3.4 Depthwise Separable Convolution\n",
        "Depthwise separable convolution is a depthwise convolution followed by a\n",
        "pointwise convolution as follows:\n",
        "\n",
        "- **Depthwise convolution** is the channel-wise `K × K` spatial convolution. Suppose we have 64 channels, then we will have 64 `K × K` spatial convolutions. In Keras this is implemented using [DepthwiseConv2D](https://keras.io/api/layers/convolution_layers/depthwise_convolution2d/).\n",
        "- **Pointwise convolution** actually is the `1 × 1` convolution to change the dimension to, say 256.\n",
        "\n",
        "Your task is to convert the second convolution below \n",
        "\n",
        "```\n",
        "x = Conv2D(256, kernel_size=(3, 3), activation='relu')(x)\n",
        "```\n",
        "\n",
        "to a depthwis separable convolution. The filter size is `K=3`, the input shape is `30x30x64` and the output shape should be `28x28x256`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q-KHsElQGFkF",
        "outputId": "eb9f9ce6-fd79-40fc-97bc-4a036c8d36d6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "x = Conv2D(64, kernel_size=(3, 3), activation='relu')(inputs)\n",
        "x = Conv2D(256, kernel_size=(3, 3), activation='relu')(x)\n",
        "model = Model(inputs=inputs, outputs=x)\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_25\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 32, 32, 3)]       0         \n",
            "_________________________________________________________________\n",
            "conv2d_49 (Conv2D)           (None, 30, 30, 64)        1792      \n",
            "_________________________________________________________________\n",
            "conv2d_50 (Conv2D)           (None, 28, 28, 256)       147712    \n",
            "=================================================================\n",
            "Total params: 149,504\n",
            "Trainable params: 149,504\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pCo77pKvKs8L"
      },
      "source": [
        "###Solution:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kfYlCaOJRlao",
        "outputId": "5a28d9b9-c3f6-43d3-adb7-e3b90a1e75a1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from keras.layers import DepthwiseConv2D\n",
        "\n",
        "x = Conv2D(64, kernel_size=(3, 3), activation='relu')(inputs)\n",
        "x = DepthwiseConv2D(kernel_size=(3, 3), activation='relu')(x)\n",
        "x = Conv2D(256,kernel_size=(1, 1), activation='relu')(x)\n",
        "model = Model(inputs=inputs, outputs=x)\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_27\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 32, 32, 3)]       0         \n",
            "_________________________________________________________________\n",
            "conv2d_51 (Conv2D)           (None, 30, 30, 64)        1792      \n",
            "_________________________________________________________________\n",
            "depthwise_conv2d (DepthwiseC (None, 28, 28, 64)        640       \n",
            "_________________________________________________________________\n",
            "conv2d_52 (Conv2D)           (None, 28, 28, 256)       16640     \n",
            "=================================================================\n",
            "Total params: 19,072\n",
            "Trainable params: 19,072\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Flh6DmlJnUz"
      },
      "source": [
        "###Questions\n",
        "1. What is the number of parameters before and after converting to depthwise separate convolution (DSC)?\n",
        "2. What is the operation cost with standard convolution vs. DSC (see slides Lecture 7)?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k3PKJDso8bVh"
      },
      "source": [
        "###Answers:\n",
        "1. DSC has approximately 8 times fewer parameters, making it less prone to overfitting:\n",
        " - Before: 149,504\n",
        " - After: 19,072\n",
        "\n",
        "2. Answers\n",
        "- The operation cost for standard convolution is D_k x D_k x M x N x D_F x D_F, where\n",
        " - D_k = filter size = 3\n",
        " - M = number of input channels = 64\n",
        " - N = number of output channels = 256\n",
        " - D_F = output height/width = 28\n",
        " - Therefore cost = 115,605,504 operations\n",
        "\n",
        "- The operation cost for DSC is D_k x D_k x M x D_F X D_F + M x N x D_F x D_F\n",
        " - Therefore cost = 451,584 + 12,845,056 = 13,296,640 operations (so approximately 9 times faster)."
      ]
    }
  ]
}