resNet_denseNet_cifar10.ipynb

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        "# ResNet and DenseNet\n",
        "## Imports\n",
        "This week we work with the cifar10 dataset. We already split it in training and test set. It is convenient to use since it already is a keras.dataset."
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        "outputId": "24e594a0-8b56-4ef3-a671-c06877911763"
      },
      "source": [
        "import tensorflow_datasets as tfds\n",
        "import tensorflow as tf\n",
        "import tensorflow.keras.layers as layers\n",
        "from tensorflow.keras.layers import Layer\n",
        "from tensorflow.keras import Model\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "train_ds, test_ds = tfds.load('cifar10', split=['train', 'test'], as_supervised=True)"
      ],
      "execution_count": null,
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          "text": [
            "\u001b[1mDownloading and preparing dataset cifar10/3.0.2 (download: 162.17 MiB, generated: 132.40 MiB, total: 294.58 MiB) to /root/tensorflow_datasets/cifar10/3.0.2...\u001b[0m\n"
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              "Dl Completed...: 0 url [00:00, ? url/s]"
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          "text": [
            "Shuffling and writing examples to /root/tensorflow_datasets/cifar10/3.0.2.incompleteRNPFZ7/cifar10-train.tfrecord\n"
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shuffling and writing examples to /root/tensorflow_datasets/cifar10/3.0.2.incompleteRNPFZ7/cifar10-test.tfrecord\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a0d0a9cf3dca4c73b8da2f6cc25833ef",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/10000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mDataset cifar10 downloaded and prepared to /root/tensorflow_datasets/cifar10/3.0.2. Subsequent calls will reuse this data.\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iC0TQdlCIjfM"
      },
      "source": [
        "## Preprocessing\n",
        "The preprocessing is basically the same as last week. We cast the data to float32, normalize it, one-hot encode it, cache, shuffle, batch and prefatch."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1DR4kvoFOtJL"
      },
      "source": [
        "def prepare_data(cifar):\n",
        "  \"\"\"\n",
        "  Preprocessing pipeline, prepares data for later use in neuronal network\n",
        "  ## Params\n",
        "    - cifar (keras.dataset) = cifar dataset\n",
        "  \"\"\"\n",
        "  #convert data from uint8 to float32\n",
        "  cifar = cifar.map(lambda img, target: (tf.cast(img, tf.float32), target))\n",
        "  #sloppy input normalization, just bringing image values from range [0, 255] to [-1, 1]\n",
        "  cifar = cifar.map(lambda img, target: (img/256, target))\n",
        "  #create one-hot targets\n",
        "  cifar = cifar.map(lambda img, target: (img, tf.one_hot(target, depth=10)))\n",
        "  #cache this progress in memory, as there is no need to redo it; it is deterministic after all\n",
        "  cifar = cifar.cache()\n",
        "  #shuffle, batch, prefetch\n",
        "  cifar = cifar.shuffle(1000)\n",
        "  cifar = cifar.batch(32)\n",
        "  cifar = cifar.prefetch(20)\n",
        "  #return preprocessed dataset\n",
        "  return cifar\n",
        "\n",
        "train_dataset = train_ds.apply(prepare_data)\n",
        "test_dataset = test_ds.apply(prepare_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in train_dataset.take(1):\n",
        "  print(i.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        },
        "id": "Ks1osxziYYFj",
        "outputId": "e33b9770-7862-40fd-8a8d-c9164ff09b7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-a31506de5edc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtrain_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\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[0;32m----> 2\u001b[0;31m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'shape'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UUvqSY9yI1ta"
      },
      "source": [
        "## ResNet\n",
        "Here are our Residual Block and our Residual Network. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t0YIcl9xQ-Fq"
      },
      "source": [
        "class Residual_Block(Layer):\n",
        "    \"\"\"Residual_Block represents a Layer in our Network, inherits from keras.layer.Layer.\"\"\"\n",
        "    \n",
        "    \n",
        "    def __init__(self, input_dim):\n",
        "        \"\"\"\n",
        "        Constructor, calls super Constructor.\n",
        "        One residual block consists of 3 pairs of convulutional layer + batch normalization. Trainable is set to True. \n",
        "        ## Params\n",
        "            - input_dim (int) = input dimension (to save the original input dimension)\n",
        "        \"\"\"\n",
        "        super(Residual_Block, self).__init__()\n",
        "        self.conv1 = layers.Conv2D(filters=24, kernel_size=1, strides=(1, 1), padding=\"same\", activation=\"relu\", kernel_regularizer=tf.keras.regularizers.l1_l2)\n",
        "        self.norm1 = layers.BatchNormalization()\n",
        "        self.conv2 = layers.Conv2D(filters=24, kernel_size=3, strides=(1, 1), padding=\"same\", activation=\"relu\", kernel_regularizer=tf.keras.regularizers.l1_l2)\n",
        "        self.norm2 = layers.BatchNormalization()\n",
        "        self.conv3 = layers.Conv2D(filters=input_dim, kernel_size=1, strides=(1, 1), padding=\"same\", activation=\"relu\", kernel_regularizer=tf.keras.regularizers.l1_l2)\n",
        "        self.norm3 = layers.BatchNormalization()\n",
        "        self.norm1.trainable = True\n",
        "        self.norm2.trainable = True\n",
        "        self.norm3.trainable = True\n",
        "\n",
        "    def call(self, inputs):\n",
        "        \"\"\"\n",
        "        Update weights/biases of each neuron\n",
        "        ## Params\n",
        "            - inputs = inputs data\n",
        "        \"\"\"\n",
        "        x = self.conv1(inputs)\n",
        "        x = self.norm1(x)\n",
        "        x = self.conv2(x)\n",
        "        x = self.norm2(x)\n",
        "        x = self.conv3(x)\n",
        "        x = self.norm3(x)\n",
        "        return x\n",
        "\n",
        "class ResNet(Model):\n",
        "    \"\"\"ResNet represents Residual Network/ model.\"\"\"\n",
        "\n",
        "\n",
        "    def __init__(self, n_blocks=2):\n",
        "        \"\"\"\n",
        "        Constructor, calls super Constructor. \n",
        "        Is initialized with 1 convolutional layer followed by specified number of residual blocks followed by global average pooling layer and Dense layer as output layer.\n",
        "        ## Params\n",
        "            - n_blocks (int) = number of Blocks in ResNet\n",
        "        \"\"\"\n",
        "        super(ResNet, self).__init__()\n",
        "\n",
        "        self.conv1 = layers.Conv2D(filters=24, kernel_size=1, strides=(1, 1), padding=\"same\", activation=\"relu\", kernel_regularizer=tf.keras.regularizers.l1_l2)\n",
        "        self.blocks = [Residual_Block(24) for block in range(n_blocks)]\n",
        "        self.global_pool = layers.GlobalAvgPool2D()\n",
        "        self.out = layers.Dense(10, activation=\"softmax\")\n",
        "\n",
        "    @tf.function\n",
        "    def call(self, inputs):\n",
        "        \"\"\"\n",
        "        Update weights/biases of each neuron\n",
        "        ## Params\n",
        "            - inputs = inputs data\n",
        "        \"\"\"\n",
        "        x = self.conv1(inputs)\n",
        "        for block in self.blocks:\n",
        "            x = block(x)\n",
        "        x = self.global_pool(x)\n",
        "        return self.out(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mfumg4QMI8jj"
      },
      "source": [
        "## DenseNet\n",
        "Here are our Transition Layer, our Dense Block and our Dense Network."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TK_blO-5bc0l"
      },
      "source": [
        "class Transition_Layers(Layer):\n",
        "    \"\"\"Transition Layer for our DenseNet.\"\"\"\n",
        "\n",
        "\n",
        "    def __init__(self,reduce_to):\n",
        "        \"\"\"Constructor calls super constructor.\n",
        "        Initialized with Batch normalization (trainable=True), a convolutional layer and a average pooling layer.\n",
        "        ## Params\n",
        "            - reduce_to (int) = number of filters in Convolutional layer\n",
        "        \"\"\"\n",
        "        super(Transition_Layers, self).__init__()\n",
        "        self.batch1 = layers.BatchNormalization()\n",
        "        self.batch1.trainable = True\n",
        "        self.conv1 = layers.Conv2D(filters = reduce_to,kernel_size=(1,1),padding=\"valid\",use_bias=False)\n",
        "        self.pooling = layers.AvgPool2D(pool_size=(2,2), strides = (2,2), padding = 'valid')\n",
        "\n",
        "\n",
        "    def call(self, inputs):\n",
        "        \"\"\"\n",
        "        Update weights/biases of each neuron\n",
        "        ## Params\n",
        "            - inputs = inputs data\n",
        "        \"\"\"\n",
        "        x = self.batch1(inputs)\n",
        "        x = tf.nn.relu(x)\n",
        "        x = self.conv1(x)\n",
        "        x = self.pooling(x)\n",
        "        return x\n",
        "        \n",
        "\n",
        "class Dense_Block(Layer):\n",
        "    \"\"\"Dense block for our DenseNet\"\"\"\n",
        "\n",
        "\n",
        "    def __init__(self,  n_filters, new_channels):\n",
        "        \"\"\"Constructor calls super Constructor\n",
        "        Initializes 1 Block for our DenseNet, consist 2 pairs of Batch normalization (trainable=True) + convolutional layer.\n",
        "        ## Params\n",
        "            - n_filters (int) = number of filters for first conv layer\n",
        "            - new_channels (int) = number of filters for second conv layer\n",
        "        \"\"\"\n",
        "        super(Dense_Block, self).__init__()\n",
        "        self.batch1 = layers.BatchNormalization()\n",
        "        self.batch1.trainable = True\n",
        "        self.conv1 = layers.Conv2D(n_filters, kernel_size=(1,1),padding=\"valid\",use_bias=False)\n",
        "        self.batch2 = layers.BatchNormalization()\n",
        "        self.batch2.trainable = True\n",
        "        self.conv2 = layers.Conv2D(new_channels, kernel_size=(3,3), padding=\"same\", use_bias=False)\n",
        "        \n",
        "    def call(self, inputs):\n",
        "        \"\"\"\n",
        "        Update weights/biases of each neuron\n",
        "        ## Params\n",
        "            - inputs = input data\n",
        "        \"\"\"\n",
        "        x = self.batch1(inputs)\n",
        "        x = tf.nn.relu(x)\n",
        "        x = self.conv1(x)\n",
        "        x = self.batch2(x)\n",
        "        x = tf.nn.relu(x)\n",
        "        x = self.conv2(x)\n",
        "        return tf.concat([x,inputs],axis=-1)\n",
        "\n",
        "\n",
        "class DenseNet(Model):\n",
        "    \"\"\"DenseNet represents DenseNet/model\"\"\"\n",
        "\n",
        "\n",
        "    def __init__(self, n_filters=128, new_channels=64, n_transitions=64):\n",
        "        \"\"\"Constructor calls super Constructor\n",
        "        Initialized with starting layers, a Dense block, a Transition layer, again a Dense block and an output layer.\n",
        "        ## Params\n",
        "            - n_filters (int) = number of filters for first conv layer\n",
        "            - new_channels (int) = number of filters for second conv layer\n",
        "            - n_transitions (int) = number of transition layers\n",
        "        \"\"\"\n",
        "        super(DenseNet, self).__init__()\n",
        "\n",
        "        #Starting layers\n",
        "        self.starting_layers = []\n",
        "        self.starting_layers.append(layers.ZeroPadding2D(padding=(3, 3), data_format=None))\n",
        "        self.starting_layers.append(layers.Conv2D(filters = 64, kernel_size=(7,7),padding=\"valid\",use_bias=False))\n",
        "        self.starting_layers.append(layers.BatchNormalization())\n",
        "        self.starting_layers[-1].trainable = True\n",
        "        self.starting_layers.append(tf.nn.relu)\n",
        "        self.starting_layers.append(layers.ZeroPadding2D(padding=(1, 1), data_format=None))\n",
        "\n",
        "        # First block\n",
        "        self.block1 = [Dense_Block(n_filters, new_channels) for _ in range(3)]\n",
        "        self.block1.append(Transition_Layers(n_transitions))\n",
        "\n",
        "        # Second block\n",
        "        self.block2 = [Dense_Block(n_filters, new_channels) for _ in range(3)]\n",
        "        self.block2.append(Transition_Layers(n_transitions))\n",
        "\n",
        "        #Third\n",
        "        self.block3 = [Dense_Block(n_filters, new_channels) for _ in range(3)]\n",
        "\n",
        "        # out Neurons\n",
        "        self.outgoing_layers = []\n",
        "        self.outgoing_layers.append(layers.BatchNormalization())\n",
        "        self.outgoing_layers[-1].trainable = True\n",
        "        self.outgoing_layers.append(tf.nn.relu)\n",
        "        self.outgoing_layers.append(layers.GlobalAveragePooling2D())\n",
        "        self.outgoing_layers.append(layers.Dense(10,activation=\"softmax\"))\n",
        "    \n",
        "\n",
        "    @tf.function\n",
        "    def call(self, inputs):\n",
        "        \"\"\"\n",
        "        Update weights/biases of each neuron\n",
        "        ## Params\n",
        "            - inputs = inputs data\n",
        "        \"\"\"\n",
        "        x = inputs\n",
        "        for layer in self.starting_layers:\n",
        "            x = layer(x)\n",
        "\n",
        "        for layer in self.block1:\n",
        "            x = layer(x)\n",
        "\n",
        "        for layer in self.block2:\n",
        "            x = layer(x)\n",
        "\n",
        "        for layer in self.block3:\n",
        "            x = layer(x)\n",
        "\n",
        "        for layer in self.outgoing_layers:\n",
        "            x = layer(x)\n",
        "    \n",
        "        return x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cnQZsiAVJFJm"
      },
      "source": [
        "## Train and Test function\n",
        "The training and test function are the same as last week. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bXSGQRyCeACM"
      },
      "source": [
        "def train_step(model, input, target, loss_function, optimizer):\n",
        "  \"\"\"\n",
        "  Function for model training\n",
        "    ## Params\n",
        "        - model (Model )= instance of Model class \n",
        "        - input (keras.dataset) = input for model\n",
        "        - target (keras.dataset) = targets \n",
        "        - loss_function (keras.losses.LossFunction) = loss function \n",
        "        - optimizer (keras.optimizer.Optimizer) = tensorflow optimizer \n",
        "  \"\"\"\n",
        "  # loss_object and optimizer_object are instances of respective tensorflow classes\n",
        "  with tf.GradientTape() as tape:\n",
        "    prediction = model(input)\n",
        "    loss = loss_function(target, prediction)\n",
        "    gradients = tape.gradient(loss, model.trainable_variables)\n",
        "  optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
        "  return loss\n",
        "\n",
        "\n",
        "def test(model, test_data, loss_function):\n",
        "  \"\"\"\n",
        "    Function for model testing \n",
        "    ## Params\n",
        "        - model (keras.Model) = instance of Model class \n",
        "        - test_data (keras.dataset) = data for testing \n",
        "        - loss_function (keras.losses.LossFunction) = loss function\n",
        "  \"\"\"\n",
        "  # test over complete test data\n",
        "  test_accuracy_aggregator = []\n",
        "  test_loss_aggregator = []\n",
        "\n",
        "  for (input, target) in test_data:\n",
        "    prediction = model(input)\n",
        "    sample_test_loss = loss_function(target, prediction)\n",
        "    sample_test_accuracy =  np.argmax(target, axis=1) == np.argmax(prediction, axis=1)\n",
        "    sample_test_accuracy = np.mean(sample_test_accuracy)\n",
        "    test_loss_aggregator.append(sample_test_loss.numpy())\n",
        "    test_accuracy_aggregator.append(np.mean(sample_test_accuracy))\n",
        "\n",
        "  test_loss = tf.reduce_mean(test_loss_aggregator)\n",
        "  test_accuracy = tf.reduce_mean(test_accuracy_aggregator)\n",
        "\n",
        "  return test_loss, test_accuracy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-4RX_QqsJMlq"
      },
      "source": [
        "## Running our Code\n",
        "We first run our DenseNet and than our ResNet. The accuracies are printed out. \n",
        "- Training samples: 1000 \n",
        "- Testing samples: 100\n",
        "- Epochs: 10 epochs\n",
        "- learning rate: 0.001\n",
        "- Loss: Crossentropy loss\n",
        "- Optimizer: Adam"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UNh9inTxeGwM",
        "outputId": "4793d978-21ce-45e8-bf96-366591ab1e43"
      },
      "source": [
        "tf.keras.backend.clear_session()\n",
        "\n",
        "#For showcasing we only use a subset of the training and test data (generally use all of the available data!)\n",
        "train_dataset = train_dataset.take(10000)\n",
        "test_dataset = test_dataset.take(1000)\n",
        "\n",
        "### Hyperparameters\n",
        "num_epochs = 10\n",
        "learning_rate = 0.001\n",
        "\n",
        "# Initialize the models.\n",
        "model_dense = DenseNet()\n",
        "model_res = ResNet(5)\n",
        "\n",
        "# Initialize the loss: categorical cross entropy. Check out 'tf.keras.losses'.\n",
        "cross_entropy_loss = tf.keras.losses.CategoricalCrossentropy()\n",
        "\n",
        "# Initialize the optimizer: Adam with default parameters. Check out 'tf.keras.optimizers'\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate)\n",
        "\n",
        "def train(model):\n",
        "    \"\"\"\n",
        "    Function for testing our Models\n",
        "    ## Params\n",
        "        - model (keras.Model) = Instance of model\n",
        "    \"\"\"\n",
        "    # Initialize lists for later visualization.\n",
        "    train_losses = []\n",
        "    test_losses = []\n",
        "    test_accuracies = []\n",
        "\n",
        "    #testing once before we begin\n",
        "    test_loss, test_accuracy = test(model, test_dataset, cross_entropy_loss)\n",
        "    test_losses.append(test_loss)\n",
        "    test_accuracies.append(test_accuracy)\n",
        "\n",
        "    #check how model performs on train data once before we begin\n",
        "    train_loss, _ = test(model, train_dataset, cross_entropy_loss)\n",
        "    train_losses.append(train_loss)\n",
        "\n",
        "    # We train for num_epochs epochs.\n",
        "    for epoch in range(num_epochs):\n",
        "        print(f'Epoch: {str(epoch)} starting with accuracy {test_accuracies[-1]}')\n",
        "\n",
        "        #training (and checking in with training)\n",
        "        epoch_loss_agg = []\n",
        "        for input,target in train_dataset:\n",
        "            train_loss = train_step(model, input, target, cross_entropy_loss, optimizer)\n",
        "            epoch_loss_agg.append(train_loss)\n",
        "        \n",
        "        #track training loss\n",
        "        train_losses.append(tf.reduce_mean(epoch_loss_agg))\n",
        "\n",
        "        #testing, so we can track accuracy and test loss\n",
        "        test_loss, test_accuracy = test(model, test_dataset, cross_entropy_loss)\n",
        "        test_losses.append(test_loss)\n",
        "        test_accuracies.append(test_accuracy)\n",
        "\n",
        "    return train_losses, test_losses, test_accuracies \n",
        "\n",
        "# calling test function\n",
        "print(\"Accuracy for our DenseNet\")\n",
        "erg_dense = train(model_dense)\n",
        "print(\"\\n\")\n",
        "print(\"Accuracy for our ResNet\")\n",
        "erg_res = train(model_res)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy for our DenseNet\n",
            "Epoch: 0 starting with accuracy 0.10013977635782748\n",
            "Epoch: 1 starting with accuracy 0.41553514376996803\n",
            "Epoch: 2 starting with accuracy 0.4910143769968051\n",
            "Epoch: 3 starting with accuracy 0.536841054313099\n",
            "Epoch: 4 starting with accuracy 0.5915535143769968\n",
            "Epoch: 5 starting with accuracy 0.6291932907348243\n",
            "Epoch: 6 starting with accuracy 0.6381789137380192\n",
            "Epoch: 7 starting with accuracy 0.6795127795527156\n",
            "Epoch: 8 starting with accuracy 0.705770766773163\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DrPJbO6hJmPd"
      },
      "source": [
        "## Visualization\n",
        "### DenseNet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T7Z96P_HlPkE"
      },
      "source": [
        "# Assigning values\n",
        "train_losses_dense= erg_dense[0] \n",
        "test_losses_dense = erg_dense[1] \n",
        "test_accuracies_dense = erg_dense[2]\n",
        "\n",
        "# Visualize accuracy and loss for training and test data.\n",
        "plt.figure()\n",
        "line1, = plt.plot(train_losses_dense)\n",
        "line2, = plt.plot(test_losses_dense)\n",
        "line3, = plt.plot(test_accuracies_dense)\n",
        "plt.xlabel(\"Training steps\")\n",
        "plt.ylabel(\"Loss/Accuracy\")\n",
        "plt.xticks(np.arange(num_epochs+1))\n",
        "plt.legend((line1,line2, line3),(\"training\",\"test\", \"test accuracy\"))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SpdlDE9_UqRX"
      },
      "source": [
        "### ResNet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iutnYhtbUjVk"
      },
      "source": [
        "# Assigning values\n",
        "train_losses_res = erg_res[0] \n",
        "test_losses_res = erg_res[1] \n",
        "test_accuracies_res = erg_res[2]\n",
        "\n",
        "# Visualize accuracy and loss for training and test data.\n",
        "plt.figure()\n",
        "line1, = plt.plot(train_losses_res)\n",
        "line2, = plt.plot(test_losses_res)\n",
        "line3, = plt.plot(test_accuracies_res)\n",
        "plt.xlabel(\"Training steps\")\n",
        "plt.ylabel(\"Loss/Accuracy\")\n",
        "plt.xticks(np.arange(num_epochs+1))\n",
        "plt.legend((line1,line2, line3),(\"training\",\"test\", \"test accuracy\"))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "brz4bn8woJPj"
      },
      "source": [
        "### Training losses"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z6p7uPkvRR8r",
        "outputId": "89f61e85-883e-46f6-d4c9-337ad0989a6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        }
      },
      "source": [
        "# Barplot\n",
        "fig = plt.figure()\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "epochs = np.arange(11)\n",
        "ax.bar(epochs, train_losses_res, 0.3)\n",
        "ax.bar(epochs+0.3, train_losses_dense, 0.3)\n",
        "plt.legend((line1,line2),(\"res\",\"dense\"))\n",
        "ax.set_title('Training losses')\n",
        "ax.set_ylabel('Loss')\n",
        "ax.set_xlabel('Training steps')\n",
        "ax.set_xticks(np.arange(11))\n",
        "plt.show()\n",
        "\n",
        "# Graph\n",
        "fig = plt.figure()\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "line1, = plt.plot(train_losses_res)\n",
        "line2, = plt.plot(train_losses_dense)\n",
        "plt.xlabel(\"Training steps\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.xticks(np.arange(num_epochs+1))\n",
        "plt.legend((line1,line2, line3),(\"res\",\"dense\"))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-b1a8b6eb3c88>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Barplot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\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[0m\u001b[1;32m      3\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_axes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\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      4\u001b[0m \u001b[0mepochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m11\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_losses_res\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ArE4uxkxoRGX"
      },
      "source": [
        "### Test losses"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cIQmk3ZFhI5P"
      },
      "source": [
        "# Barplot\n",
        "fig = plt.figure()\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "epochs = np.arange(11)\n",
        "ax.bar(epochs, test_losses_res, 0.3)\n",
        "ax.bar(epochs+0.3, test_losses_dense, 0.3)\n",
        "plt.legend((line1,line2),(\"res\",\"dense\"))\n",
        "ax.set_title('Test losses')\n",
        "ax.set_ylabel('Losses')\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_xticks(np.arange(11))\n",
        "plt.show()\n",
        "\n",
        "# Graph\n",
        "plt.figure()\n",
        "line1, = plt.plot(test_losses_res)\n",
        "line2, = plt.plot(test_losses_dense)\n",
        "plt.xlabel(\"Training steps\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.xticks(np.arange(num_epochs+1))\n",
        "plt.legend((line1,line2, line3),(\"res\",\"dense\"))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sMrTvAw_oUEa"
      },
      "source": [
        "### Test Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iutzFK6NhJoR"
      },
      "source": [
        "# Barplot\n",
        "fig = plt.figure()\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "epochs = np.arange(11)\n",
        "ax.bar(epochs, test_accuracies_res, 0.3)\n",
        "ax.bar(epochs+0.3, test_accuracies_dense, 0.3)\n",
        "plt.legend((line1,line2),(\"res\",\"dense\"))\n",
        "ax.set_title('Test Accuracy')\n",
        "ax.set_ylabel('Accuracy')\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_xticks(np.arange(11))\n",
        "plt.show()\n",
        "\n",
        "# Graph\n",
        "plt.figure()\n",
        "line1, = plt.plot(test_accuracies_res)\n",
        "line2, = plt.plot(test_accuracies_dense)\n",
        "plt.xlabel(\"Training steps\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.xticks(np.arange(num_epochs+1))\n",
        "plt.legend((line1,line2, line3),(\"res\",\"dense\"))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}