HW4/Practical/DL_HW4_AmirPourmand.ipynb

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        "<div style=\"direction:rtl;line-height:300%;\">\n",
        "\t<font face=\"XB Zar\" size=5>\n",
        "\t\t<div align=center>\n",
        "\t\t\t<font face=\"IranNastaliq\" size=30>\n",
        "\t\t\t\t<p></p>\n",
        "\t\t\t\t<p></p>\n",
        "به نام خدا\n",
        "\t\t\t\t<p></p>\n",
        "\t\t\t</font>\n",
        "\t\t\t<font color=#FF7500>\n",
        "دانشگاه صنعتی شریف - دانشکده مهندسی کامپیوتر\n",
        "            </font>\n",
        "\t\t\t<p></p>\n",
        "\t\t\t<font color=blue>\n",
        "یادگیری ژرف\n",
        "            </font>\n",
        "\t\t\t<br />\n",
        "\t\t\t<br />\n",
        "پاییز ۱۴۰۰\t\t</div>\n",
        "\t\t<hr/>\n",
        "\t\t<div align=center>\n",
        "\t\t    <font size=6>\n",
        "\t\t\t    <br />\n",
        "تمرین چهارم - Autoencoders - Attention Models\n",
        "            \t<br/>\n",
        "\t\t\t</font>\n",
        "طراح: افشین کریمی\n",
        "                <br/><br/>\n",
        "                <br/>\n",
        "        </style>\n",
        "\t</font>\n",
        "</div>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fedupasRLBQa"
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      "source": [
        "<div style=\"direction:rtl;line-height:300%;\">\n",
        "\t<font face=\"XB Zar\" size=5>\n",
        "\t\t<div align=center>\n",
        "\t\t\t\t<p></p>\n",
        "\t\t\t\t<p></p>\n",
        "                نام: امیر پورمند\n",
        "\t\t\t\t<br/>\n",
        "                شماره دانشجویی: ۹۹۲۱۰۲۵۹\n",
        "        </div>\n",
        "\t\t<br />\n",
        "\t\t<hr />\n",
        "\t\t<style type=\"text/css\" scoped>\n",
        "        p{\n",
        "        border: 1px solid #a2a9b1;background-color: #f8f9fa;display: inline-block;\n",
        "        };\n",
        "        </style>\n",
        "\t</font>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XklhkNc0REPo"
      },
      "source": [
        "# Autoencoders\n",
        "\n",
        "Just Complete the ToDo Parts "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "07jYTmTtVqMn"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "from torchvision import datasets\n",
        "from torchvision import transforms\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
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        },
        "outputId": "d1a4211f-d03b-420f-a9e8-59c18521fc16"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "  0%|          | 0/9912422 [00:00<?, ?it/s]"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
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        {
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              "  0%|          | 0/1648877 [00:00<?, ?it/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
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        {
          "output_type": "display_data",
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              "version_major": 2
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Transforms images to a PyTorch Tensor\n",
        "tensor_transform = transforms.ToTensor()\n",
        "  \n",
        "# Download the MNIST Dataset\n",
        "dataset = datasets.MNIST(root = \"./data\",\n",
        "                         train = True,\n",
        "                         download = True,\n",
        "                         transform = tensor_transform)\n",
        "  \n",
        "# DataLoader is used to load the dataset \n",
        "# for training\n",
        "loader = torch.utils.data.DataLoader(dataset = dataset,\n",
        "                                     batch_size = 32,\n",
        "                                     shuffle = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HYXxhKMkVqHH"
      },
      "outputs": [],
      "source": [
        "# Creating a PyTorch class\n",
        "# 28*28 ==> 9 ==> 28*28\n",
        "class AE(torch.nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "          \n",
        "        ''' Todo: Build a linear encoder with Linear\n",
        "         layer followed by Relu activation function\n",
        "         784 ==> 9 '''\n",
        "        self.encoder = nn.Sequential(\n",
        "                nn.Linear(28*28, 14*14),\n",
        "                nn.ReLU(),\n",
        "                nn.Linear(14*14,7*7),\n",
        "                nn.ReLU(),\n",
        "                nn.Linear(7*7, 9),\n",
        "                nn.ReLU(),\n",
        "        )\n",
        "          \n",
        "        ''' Todo: Build a linear decoder with Linear\n",
        "         layer followed by Relu activation function\n",
        "         The Sigmoid activation function\n",
        "         outputs the value between 0 and 1\n",
        "         9 ==> 784 '''\n",
        "        self.decoder = nn.Sequential(\n",
        "                nn.Linear(9,7*7),\n",
        "                nn.ReLU(),\n",
        "                nn.Linear(7*7,14*14),\n",
        "                nn.ReLU(),\n",
        "                nn.Linear(14*14,28*28),\n",
        "                nn.Sigmoid()\n",
        "        )\n",
        "  \n",
        "    def forward(self, x):\n",
        "        encoded = self.encoder(x)\n",
        "        decoded = self.decoder(encoded)\n",
        "        return decoded"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rfals6CUVqCW"
      },
      "outputs": [],
      "source": [
        "\n",
        "''' Todo: Initialaize model '''\n",
        "model = AE()\n",
        "  \n",
        "''' Todo: Validation using MSE Loss function '''\n",
        "loss_function = nn.MSELoss()\n",
        "  \n",
        "''' Todo: Use an Adam Optimizer with lr = 0.1 '''\n",
        "optimizer = torch.optim.Adam(model.parameters(),lr=0.1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4-3OCC8tVp_n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 686
        },
        "outputId": "386d5513-57b1-4b79-8337-ba484b984087"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
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              "[<matplotlib.lines.Line2D at 0x7fc05bf67510>]"
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          "execution_count": 21
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from tqdm import tqdm\n",
        "\n",
        "epochs = 20\n",
        "outputs = []\n",
        "losses = []\n",
        "for epoch in range(epochs):\n",
        "    for (image, _) in tqdm(loader):\n",
        "        \n",
        "      ''' Todo: Reshaping the image to (-1, 784) '''\n",
        "      image = torch.reshape(image,(-1,784))\n",
        "        \n",
        "      # Output of Autoencoder\n",
        "      reconstructed = model(image)\n",
        "        \n",
        "      ''' Todo: Calculate the loss function '''\n",
        "      loss = loss_function(image,reconstructed)\n",
        "        \n",
        "      # The gradients are set to zero,\n",
        "      # the the gradient is computed and stored.\n",
        "      # .step() performs parameter update\n",
        "      optimizer.zero_grad()\n",
        "      loss.backward()\n",
        "      optimizer.step()\n",
        "        \n",
        "      # Storing the losses in a list for plotting\n",
        "      losses.append(loss)\n",
        "    outputs.append((epochs, image, reconstructed))\n",
        "  \n",
        "# Defining the Plot Style\n",
        "plt.style.use('fivethirtyeight')\n",
        "plt.xlabel('Iterations')\n",
        "plt.ylabel('Loss')\n",
        "  \n",
        "''' Todo: Plot the last 100 values '''\n",
        "plt.plot(range(100),losses[-100:])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vtwSxSiCbkoR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "d6578cee-9f17-4689-8f85-1e325c20c1ed"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot the first input image array\n",
        "for i, item in enumerate(image):\n",
        "    \n",
        "  # Reshape the array for plotting\n",
        "  item = item.reshape(-1, 28, 28)\n",
        "  plt.imshow(item[0])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sVHE0U6aegso",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "f4cd15a2-d043-448b-e5b7-77baa47ad9f8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "''' Todo: Plot the first reconstructed input image array '''  \n",
        "for i, item in enumerate(reconstructed):\n",
        "    \n",
        "  # Reshape the array for plotting\n",
        "  item = item.reshape(-1, 28, 28)\n",
        "  plt.imshow(item[0].detach().numpy())\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fbk_vMckkhCp"
      },
      "source": [
        "# Attention Models\n",
        "\n",
        "Just Complete the ToDo Parts "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SJixqDSwK4J8"
      },
      "outputs": [],
      "source": [
        "# Some imports that we require to write the network.\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch import optim\n",
        "import torch.nn.functional as F\n",
        "from torch.autograd import Variable"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SzscV_sXkuYJ"
      },
      "outputs": [],
      "source": [
        "# Encoder for the attention network that is similar to the vanilla encoders\n",
        "class Encoder(nn.Module):\n",
        "  # Store the parameters\n",
        "  def __init__(self, input_size, hidden_size, bidirectional = True):\n",
        "    super(Encoder, self).__init__()\n",
        "    self.hidden_size = hidden_size\n",
        "    self.input_size = input_size\n",
        "    self.bidirectional = bidirectional\n",
        "    \n",
        "    ''' ToDo : Create an LSTM layer '''\n",
        "    self.lstm = nn.LSTM(input_size, hidden_size, bidirectional = bidirectional)\n",
        "  \n",
        "  # The Forward function\n",
        "  def forward(self, inputs, hidden):\n",
        "    \n",
        "    ''' Todo : Pass the input through the LSTM with the provided hidden state '''\n",
        "    output, hidden = self.lstm(inputs.view(1, 1, self.input_size), hidden)\n",
        "    return output, hidden\n",
        "    \n",
        "  # This function has to be called before passing sentence through the LSTM to initialize the hidden state.  \n",
        "  def init_hidden(self):\n",
        "    return (torch.zeros(1 + int(self.bidirectional), 1, self.hidden_size),\n",
        "      torch.zeros(1 + int(self.bidirectional), 1, self.hidden_size))\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S92J-YHJmvb3"
      },
      "outputs": [],
      "source": [
        "# This class is the attention based decoder\n",
        "class AttentionDecoder(nn.Module):\n",
        "  \n",
        "  def __init__(self, hidden_size, output_size, vocab_size):\n",
        "    super(AttentionDecoder, self).__init__()\n",
        "    self.hidden_size = hidden_size\n",
        "    self.output_size = output_size\n",
        "    \n",
        "    # This layer calculates the importance of the word, by using the previous decoder hidden state and the hidden state of the encoder at that particular time step\n",
        "    self.attn = nn.Linear(hidden_size + output_size, 1)\n",
        "    ''' Todo: The 'lstm' layer takes in concatenation of vector obtained by having a weighted sum according to attention weights and the previous word outputted '''\n",
        "    self.lstm = nn.LSTM(hidden_size + vocab_size, output_size)\n",
        "    ''' Todo: Map the output feature space into the size of vocabulary '''\n",
        "    self.final = nn.Linear(output_size, vocab_size)\n",
        "  \n",
        "  # The 'init_hidden' function is used in the same way as in the encoder.\n",
        "  def init_hidden(self):\n",
        "    return (torch.zeros(1, 1, self.output_size),\n",
        "      torch.zeros(1, 1, self.output_size))\n",
        "  \n",
        "  # The forward function of the decoder\n",
        "  def forward(self, decoder_hidden, encoder_outputs, input):\n",
        "    \n",
        "    # 'weights' list is used to store the attention weights\n",
        "    weights = []\n",
        "    for i in range(len(encoder_outputs)):\n",
        "      print(decoder_hidden[0][0].shape)\n",
        "      print(encoder_outputs[0].shape)\n",
        "      # Pass each encoder output through the 'attn' layer along with \n",
        "      # decoder's previous hidden state by concatenating them and store \n",
        "      # them in the 'weights' list \n",
        "      weights.append(self.attn(torch.cat((decoder_hidden[0][0], \n",
        "                                          encoder_outputs[i]), dim = 1)))\n",
        "      \n",
        "    ''' Todo : scale weights in range (0,1) by applying softmax activation '''\n",
        "    normalized_weights = F.softmax(torch.cat(weights, 1), 1)\n",
        "    \n",
        "    # To calculate the weighted sum, we use batch matrix multiplication\n",
        "    attn_applied = torch.bmm(normalized_weights.unsqueeze(1),\n",
        "                             encoder_outputs.view(1, -1, self.hidden_size))\n",
        "    \n",
        "    input_lstm = torch.cat((attn_applied[0], input[0]), dim = 1) #if we are using embedding, use embedding of input here instead\n",
        "    \n",
        "    output, hidden = self.lstm(input_lstm.unsqueeze(0), decoder_hidden)\n",
        "    \n",
        "    output = self.final(output[0])\n",
        "    \n",
        "    return output, hidden, normalized_weights"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YqIG8wCFvqO3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f0de9ed-8361-4280-ebae-7ed298782ffc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([1, 1, 40])\n",
            "torch.Size([2, 1, 20])\n",
            "torch.Size([2, 1, 20])\n",
            "torch.Size([1, 25])\n",
            "torch.Size([1, 40])\n",
            "torch.Size([1, 25])\n",
            "torch.Size([1, 40])\n",
            "torch.Size([1, 30])\n",
            "torch.Size([1, 1, 25])\n",
            "torch.Size([1, 1, 25])\n",
            "tensor([[0.5000, 0.5000]], grad_fn=<SoftmaxBackward0>)\n"
          ]
        }
      ],
      "source": [
        "# Testing the code\n",
        "bidirectional = True\n",
        "c = Encoder(10, 20, bidirectional)\n",
        "a, b = c.forward(torch.randn(10), c.init_hidden())\n",
        "print(a.shape)\n",
        "print(b[0].shape)\n",
        "print(b[1].shape)\n",
        "\n",
        "x = AttentionDecoder(20 * (1 + bidirectional), 25, 30)\n",
        "y, z, w = x.forward(x.init_hidden(), torch.cat((a,a)), torch.zeros(1,1, 30)) \n",
        "print(y.shape)\n",
        "print(z[0].shape)\n",
        "print(z[1].shape)\n",
        "print(w)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z_hUjaFHvv8C"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
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