TF2C1W1L2_Siamese_network.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNrn1/GOcEho16YVXlzUTet",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Deepak-Mewada/TensorFlowAdvancedTechniquesSpecialization_DeeplearningAI/blob/main/TF2C1W1L2_Siamese_network.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Ungraded Lab: Implement a Siamese network¶\n",
        "\n",
        "This lab will go through creating and training a multi-input model. You will build a basic Siamese Network to find the similarity or dissimilarity between items of clothing."
      ],
      "metadata": {
        "id": "jxNkIvUEb4Wj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Imports"
      ],
      "metadata": {
        "id": "0F2tZuJ4b_Hp"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lP5-X9OSLPU2"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Input, Flatten, Dense, Dropout, Lambda\n",
        "from tensorflow.keras.optimizers import RMSprop\n",
        "from tensorflow.keras.datasets import fashion_mnist\n",
        "from tensorflow.keras.utils import plot_model\n",
        "from tensorflow.keras import backend as K\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from PIL import Image, ImageFont , ImageDraw\n",
        "import random"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Prepare the Dataset\n",
        "\n",
        "First define a few utilities for preparing and visualizing your dataset."
      ],
      "metadata": {
        "id": "Yg0kUNvhcBOx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def create_pairs(x, digit_indices):\n",
        "    '''Positive and negative pair creation.\n",
        "    Alternates between positive and negative pairs.\n",
        "    '''\n",
        "\n",
        "    pairs  = []\n",
        "    labels = []\n",
        "    n = min([len(digit_indices[d]) for d in range(10)]) - 1\n",
        "\n",
        "    for d in range(10):\n",
        "      for i in range(n):\n",
        "        z1 , z2 = digit_indices[d][i], digit_indices[d][i+1]\n",
        "        pairs += [[x[z1],x[z2]]]\n",
        "        inc = random.randrange(1,10)\n",
        "        dn = (d+inc) %10\n",
        "        z1, z2 = digit_indices[d][i], digit_indices[dn][i]\n",
        "        pairs += [[x[z1],x[z2]]]\n",
        "        labels+= [1,0]\n",
        "\n",
        "    return np.array(pairs), np.array(labels)\n",
        "  \n",
        "\n",
        "def create_pairs_on_set(image, labels):\n",
        "\n",
        "    digit_indices = [np.where(labels == i)[0] for i in range(10)]\n",
        "    pairs , y = create_pairs(image, digit_indices)\n",
        "    y = y.astype('float32')\n",
        "\n",
        "    return pairs, y\n",
        "\n",
        "\n",
        "def show_image(image):  \n",
        "  plt.figure()\n",
        "  plt.imshow(image)\n",
        "  plt.colorbar()\n",
        "  plt.grid(False)\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "sLaCp4bOcU8p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now download and prepare our train and test sets. You will also create pairs of images that will go into the multi-input model."
      ],
      "metadata": {
        "id": "HE7ELkJKcvJR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# load the dataset fashion mnist\n",
        "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()\n",
        "\n",
        "#prepare train and test dataset\n",
        "train_images = train_images.astype('float32')\n",
        "test_images = test_images.astype('float32')\n",
        "\n",
        "#normalize the values\n",
        "train_images = train_images/255.0\n",
        "test_images = test_images/255.0\n",
        "\n",
        "#create pairs on train and test sets\n",
        "tr_pairs, tr_y = create_pairs_on_set(train_images, train_labels)\n",
        "ts_pairs, ts_y = create_pairs_on_set(test_images, test_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CKUgCQufc0nB",
        "outputId": "a4aa4b26-5ce8-43c1-c84a-fded49202106"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "29515/29515 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26421880/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "5148/5148 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4422102/4422102 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "can see a sample pair of images below."
      ],
      "metadata": {
        "id": "yiLEB1kMcyGB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#array index\n",
        "this_pair =7\n",
        "\n",
        "#show images at this index\n",
        "show_image(ts_pairs[this_pair][0])\n",
        "show_image(ts_pairs[this_pair][1])\n",
        "\n",
        "#print the label for this pair\n",
        "print(ts_y[this_pair])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "taiFSoG-c1EZ",
        "outputId": "56b3385f-8545-41f8-d17b-c9f4748c9a9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print other pairs \n",
        "\n",
        "show_image(tr_pairs[:,0][0])\n",
        "show_image(tr_pairs[:,0][1])\n",
        "\n",
        "show_image(tr_pairs[:,1][0])\n",
        "show_image(tr_pairs[:,1][1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "N7AiKu6JkYp8",
        "outputId": "6d6c730f-9f9d-41dd-c547-430ffe95a738"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Build the Model"
      ],
      "metadata": {
        "id": "Qih6SN8ccE1g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, you'll define some utilities for building our model."
      ],
      "metadata": {
        "id": "tCtZTzuqc4dW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def initialize_base_network():\n",
        "\n",
        "  input = Input(shape = (28,28) , name = \"base_input\")\n",
        "  x = Flatten(name = 'flatten_input')(input)\n",
        "  x = Dense(units = 128, activation = 'relu', name = 'first_base_dense')(x)\n",
        "  x = Dropout(0.1, name = 'first_dropout')(x)\n",
        "  x = Dense(units = 128, activation ='relu', name = 'second_base_dense')(x)\n",
        "  x = Dropout(0.1, name= \"second_dropout\")(x)\n",
        "  x = Dense(units = 128, name = \"third_base_dense\")(x)\n",
        "\n",
        "  return Model(inputs= input, outputs=x)\n",
        "\n",
        "\n",
        "def euclidean_distance(vects):\n",
        "  x , y = vects\n",
        "  sum_square = K.sum(K.square(x-y), axis=1, keepdims= True)\n",
        "  return K.sqrt(K.maximum(sum_square, K.epsilon()))\n",
        "\n",
        "def eucl_dist_output_shape(shapes):\n",
        "  shape1, shape2 = shapes\n",
        "  return (shape1[0],1)"
      ],
      "metadata": {
        "id": "OJXCTO9fcV3Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's see how our base network looks. This is where the two inputs will pass through to generate an output vector."
      ],
      "metadata": {
        "id": "SYXk9oFbc9ip"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_network = initialize_base_network()\n",
        "plot_model(base_network, show_shapes= True, show_layer_names = True)"
      ],
      "metadata": {
        "id": "xY5ZzMplc_3x",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        },
        "outputId": "d4bf8106-7bc7-48c7-876f-9a98f8c16904"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's now build the Siamese network. The plot will show two inputs going to the base network."
      ],
      "metadata": {
        "id": "v4xCJf7adC2y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Siamese-Network\n",
        "\n",
        "#create the left input and point to the base network\n",
        "input_a = Input(shape= (28,28,), name= \"left_input\")\n",
        "vect_output_a = base_network(input_a)\n",
        "\n",
        "#create the right input and point to the base network\n",
        "input_b = Input(shape = (28,28,), name = \"right_output\")\n",
        "vect_output_b = base_network(input_b)\\\n",
        "\n",
        "\n",
        "#measure the similarity of the two vector outputs\n",
        "output = Lambda(euclidean_distance, name = 'output_layer', output_shape = eucl_dist_output_shape)([vect_output_a, vect_output_b])\n",
        "\n",
        "#specify the inputs and outputs of the model\n",
        "model = Model([input_a, input_b], output)\n",
        "\n",
        "#plot the model graph\n",
        "plot_model(model, show_shapes= True, show_layer_names = True , to_file= 'outer-model.png')\n"
      ],
      "metadata": {
        "id": "5iQfuMUwdDd5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "904a7c1c-77e4-40ea-9ea8-ae48ee383423"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train rhe Model"
      ],
      "metadata": {
        "id": "vGaY9IxscJi5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " now define the custom loss for our network and start training."
      ],
      "metadata": {
        "id": "BqfSCL2pdUOK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def contrastive_loss_with_margin(margin):\n",
        "  def contrastive_loss(y_true, y_pred):\n",
        "      '''Contrastive loss from Hadsell-et-al.'06\n",
        "        http:://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf\n",
        "        '''\n",
        "      square_pred = K.square(y_pred)\n",
        "      margin_square = K.square(K.maximum(margin - y_pred, 0))\n",
        "      return (y_true * square_pred + (1 - y_true)* margin_square) \n",
        "  return contrastive_loss       "
      ],
      "metadata": {
        "id": "Zg4dXHQYcVSg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tr_pairs[:,1].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MXd4yneBtyEe",
        "outputId": "464b2ac1-28c9-4907-932d-4d827cae1516"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(119980, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rms = RMSprop()\n",
        "model.compile(loss = contrastive_loss_with_margin(margin=1), optimizer = rms)\n",
        "history = model.fit([tr_pairs[:,0], tr_pairs[:,1]], tr_y, epochs=20, batch_size=128, validation_data=([ts_pairs[:,0], ts_pairs[:,1]], ts_y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cQyS5x5-tQa8",
        "outputId": "571d74b3-bf90-4eee-d991-0b8451bf6558"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "938/938 [==============================] - 22s 20ms/step - loss: 0.1302 - val_loss: 0.0905\n",
            "Epoch 2/20\n",
            "938/938 [==============================] - 14s 15ms/step - loss: 0.0942 - val_loss: 0.0871\n",
            "Epoch 3/20\n",
            "938/938 [==============================] - 13s 14ms/step - loss: 0.0874 - val_loss: 0.0789\n",
            "Epoch 4/20\n",
            "938/938 [==============================] - 14s 15ms/step - loss: 0.0831 - val_loss: 0.0784\n",
            "Epoch 5/20\n",
            "938/938 [==============================] - 14s 15ms/step - loss: 0.0802 - val_loss: 0.0819\n",
            "Epoch 6/20\n",
            "938/938 [==============================] - 13s 13ms/step - loss: 0.0781 - val_loss: 0.0768\n",
            "Epoch 7/20\n",
            "938/938 [==============================] - 13s 14ms/step - loss: 0.0768 - val_loss: 0.0713\n",
            "Epoch 8/20\n",
            "938/938 [==============================] - 12s 13ms/step - loss: 0.0752 - val_loss: 0.0786\n",
            "Epoch 9/20\n",
            "938/938 [==============================] - 9s 10ms/step - loss: 0.0737 - val_loss: 0.0751\n",
            "Epoch 10/20\n",
            "938/938 [==============================] - 14s 15ms/step - loss: 0.0729 - val_loss: 0.0715\n",
            "Epoch 11/20\n",
            "938/938 [==============================] - 14s 15ms/step - loss: 0.0726 - val_loss: 0.0712\n",
            "Epoch 12/20\n",
            "938/938 [==============================] - 14s 14ms/step - loss: 0.0713 - val_loss: 0.0701\n",
            "Epoch 13/20\n",
            "938/938 [==============================] - 17s 18ms/step - loss: 0.0707 - val_loss: 0.0700\n",
            "Epoch 14/20\n",
            "938/938 [==============================] - 12s 13ms/step - loss: 0.0697 - val_loss: 0.0708\n",
            "Epoch 15/20\n",
            "938/938 [==============================] - 12s 13ms/step - loss: 0.0689 - val_loss: 0.0706\n",
            "Epoch 16/20\n",
            "938/938 [==============================] - 14s 15ms/step - loss: 0.0684 - val_loss: 0.0715\n",
            "Epoch 17/20\n",
            "938/938 [==============================] - 13s 13ms/step - loss: 0.0676 - val_loss: 0.0691\n",
            "Epoch 18/20\n",
            "938/938 [==============================] - 9s 10ms/step - loss: 0.0669 - val_loss: 0.0702\n",
            "Epoch 19/20\n",
            "938/938 [==============================] - 9s 10ms/step - loss: 0.0664 - val_loss: 0.0702\n",
            "Epoch 20/20\n",
            "938/938 [==============================] - 11s 12ms/step - loss: 0.0664 - val_loss: 0.0677\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Evaluation\n",
        "\n",
        "As usual, you can evaluate our model by computing the accuracy and observing the metrics during training."
      ],
      "metadata": {
        "id": "yfYpq_RLcPNJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_accuracy(y_true, y_pred):\n",
        "  '''Compute classification accuracy with a fixed threshold on distances.\n",
        "  '''\n",
        "  pred = y_pred.ravel()<0.5\n",
        "  return np.mean(pred == y_true)"
      ],
      "metadata": {
        "id": "z4xMsbJFcWjo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loss =  model.evaluate(x= [ts_pairs[:,0], ts_pairs[:,1]], y=ts_y)\n",
        "\n",
        "y_pred_train = model.predict([tr_pairs[:,0], tr_pairs[:,1]])\n",
        "train_accuracy = compute_accuracy(tr_y, y_pred_train)\n",
        "\n",
        "y_pred_test = model.predict([ts_pairs[:,0], ts_pairs[:,1]])\n",
        "test_accuracy = compute_accuracy(ts_y , y_pred_test)\n",
        "\n",
        "print(\" Loss = {} , Train Accuracy = {} Test Accuracy = {}\".formet(loss, train_accuracy , test_accuracy))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "uBhLTo3tQ9_R",
        "outputId": "82010a91-f226-4998-c6f5-63a9b2112a8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "625/625 [==============================] - 2s 2ms/step - loss: 0.0677\n",
            "3750/3750 [==============================] - 9s 2ms/step\n",
            "625/625 [==============================] - 1s 2ms/step\n"
          ]
        },
        {
          "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-21-c7bbdc09cb58>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mtest_accuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute_accuracy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mts_y\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0my_pred_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" Loss = {} , Train Accuracy = {} Test Accuracy = {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_accuracy\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mtest_accuracy\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[0;31mAttributeError\u001b[0m: 'str' object has no attribute 'formet'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_metrics(metric_name, title, ylim=5):\n",
        "  plt.title(title)\n",
        "  plt.ylim(0, ylim)\n",
        "  plt.plot(history.history[metric_name], color = 'blue', label = metric_name)\n",
        "  plt.plot(history.history['val_'+ metric_name], color = 'green', label = 'val_'+ metric_name)\n",
        "\n",
        "plot_metrics(metric_name = 'loss', title='loss', ylim=0.2)  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "gkrjM7-uWbMI",
        "outputId": "21c930c7-5ab2-4b37-99d5-463b2f6e552a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Matplotlib config\n",
        "def visualize_images():\n",
        "    plt.rc('image', cmap='gray_r')\n",
        "    plt.rc('grid', linewidth=0)\n",
        "    plt.rc('xtick', top=False, bottom=False, labelsize='large')\n",
        "    plt.rc('ytick', left=False, right=False, labelsize='large')\n",
        "    plt.rc('axes', facecolor='F8F8F8', titlesize=\"large\", edgecolor='white')\n",
        "    plt.rc('text', color='a8151a')\n",
        "    plt.rc('figure', facecolor='F0F0F0')# Matplotlib fonts\n",
        "\n",
        "\n",
        "# utility to display a row of digits with their predictions\n",
        "def display_images(left, right, predictions, labels, title, n):\n",
        "    plt.figure(figsize=(17,3))\n",
        "    plt.title(title)\n",
        "    plt.yticks([])\n",
        "    plt.xticks([])\n",
        "    plt.grid(None)\n",
        "    left = np.reshape(left, [n, 28, 28])\n",
        "    left = np.swapaxes(left, 0, 1)\n",
        "    left = np.reshape(left, [28, 28*n])\n",
        "    plt.imshow(left)\n",
        "    plt.figure(figsize=(17,3))\n",
        "    plt.yticks([])\n",
        "    plt.xticks([28*x+14 for x in range(n)], predictions)\n",
        "    for i,t in enumerate(plt.gca().xaxis.get_ticklabels()):\n",
        "        if predictions[i] > 0.5: t.set_color('red') # bad predictions in red\n",
        "    plt.grid(None)\n",
        "    right = np.reshape(right, [n, 28, 28])\n",
        "    right = np.swapaxes(right, 0, 1)\n",
        "    right = np.reshape(right, [28, 28*n])\n",
        "    plt.imshow(right)\n"
      ],
      "metadata": {
        "id": "HZ0GIZ66XZPC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_train = np.squeeze(y_pred_train)\n",
        "indexes = np.random.choice(len(y_pred_train), size=10)\n",
        "display_images(tr_pairs[:, 0][indexes], tr_pairs[:, 1][indexes], y_pred_train[indexes], tr_y[indexes], \"clothes and their dissimilarity\", 10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 271
        },
        "id": "5hdjsdcVXbB7",
        "outputId": "e17dde9a-0452-413e-a0a6-a80af8444531"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1224x216 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1224x216 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}