decoder_com_keras.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#Adaptado por Bruno Menezes (https://github.com/brunoleomenezes) e será utilizado apenas para fins acadêmicos."
      ],
      "metadata": {
        "id": "gbylM34L1_Jl"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hu3aedKp4q5R"
      },
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from tensorflow.keras.datasets import mnist"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2L-k511I4_Lp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "32be6d60-a0d1-4d89-8b16-c8522cbe5529"
      },
      "source": [
        "(X_train, y_train),(X_test, y_test) = mnist.load_data()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 1s 0us/step\n",
            "11501568/11490434 [==============================] - 1s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KRh9UqFT5QHJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "e34a756e-741d-4fd9-901c-f3d2e3881bfa"
      },
      "source": [
        "plt.imshow(X_train[1])"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fe2bd36e4d0>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        },
        {
          "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",
      "metadata": {
        "id": "78nPr2nYGECR"
      },
      "source": [
        "X_train = X_train / 255.0\n",
        "X_test = X_test / 255.0"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9UjXbGZOnyw_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a346d3c0-ddd5-4557-bffc-31d0ba41ab1b"
      },
      "source": [
        "X_input = tf.keras.layers.Input(shape=(28,28,1))\n",
        "# encoder\n",
        "X = tf.keras.layers.Conv2D(filters=16, kernel_size=3, strides=2, padding='same')(X_input)\n",
        "encoded = tf.keras.layers.Conv2D(filters=8, kernel_size=3, strides=2, padding='same')(X)\n",
        "# espaço latente\n",
        "# decoder\n",
        "X = tf.keras.layers.Conv2DTranspose(filters=16, kernel_size=3, strides=2, padding='same')(encoded)\n",
        "X = tf.keras.layers.Conv2DTranspose(filters=1, kernel_size=3, strides=2, activation='sigmoid', padding='same')(X)\n",
        "\n",
        "model = tf.keras.models.Model(inputs=X_input, outputs=X)\n",
        "\n",
        "encoded_input = tf.keras.layers.Input(shape=(7,7,8))\n",
        "X = model.layers[-2](encoded_input)\n",
        "X = model.layers[-1](X)\n",
        "\n",
        "decoder = tf.keras.models.Model(inputs=encoded_input, outputs=X)\n",
        "\n",
        "model.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.Adam(lr=0.001))\n",
        "\n",
        "decoder.summary()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None, 7, 7, 8)]         0         \n",
            "                                                                 \n",
            " conv2d_transpose (Conv2DTra  (None, 14, 14, 16)       1168      \n",
            " nspose)                                                         \n",
            "                                                                 \n",
            " conv2d_transpose_1 (Conv2DT  (None, 28, 28, 1)        145       \n",
            " ranspose)                                                       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,313\n",
            "Trainable params: 1,313\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "78tfWT7Gd_Pv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4daf1940-d9ba-42d1-be95-fcdfa0d5bd02"
      },
      "source": [
        "history = model.fit(X_train, X_train, batch_size=100, validation_split=0.1, epochs=30)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "540/540 [==============================] - 15s 5ms/step - loss: 0.0622 - val_loss: 0.0074\n",
            "Epoch 2/30\n",
            "540/540 [==============================] - 2s 4ms/step - loss: 0.0072 - val_loss: 0.0070\n",
            "Epoch 3/30\n",
            "540/540 [==============================] - 2s 4ms/step - loss: 0.0067 - val_loss: 0.0063\n",
            "Epoch 4/30\n",
            "540/540 [==============================] - 2s 5ms/step - loss: 0.0058 - val_loss: 0.0055\n",
            "Epoch 5/30\n",
            "540/540 [==============================] - 2s 5ms/step - loss: 0.0049 - val_loss: 0.0044\n",
            "Epoch 6/30\n",
            "540/540 [==============================] - 2s 5ms/step - loss: 0.0042 - val_loss: 0.0041\n",
            "Epoch 7/30\n",
            "540/540 [==============================] - 2s 4ms/step - loss: 0.0040 - val_loss: 0.0039\n",
            "Epoch 8/30\n",
            "540/540 [==============================] - 3s 5ms/step - loss: 0.0039 - val_loss: 0.0039\n",
            "Epoch 9/30\n",
            "540/540 [==============================] - 4s 7ms/step - loss: 0.0039 - val_loss: 0.0038\n",
            "Epoch 10/30\n",
            "540/540 [==============================] - 3s 5ms/step - loss: 0.0038 - val_loss: 0.0038\n",
            "Epoch 11/30\n",
            "540/540 [==============================] - 3s 6ms/step - loss: 0.0038 - val_loss: 0.0038\n",
            "Epoch 12/30\n",
            "540/540 [==============================] - 3s 5ms/step - loss: 0.0038 - val_loss: 0.0038\n",
            "Epoch 13/30\n",
            "540/540 [==============================] - 3s 5ms/step - loss: 0.0038 - val_loss: 0.0038\n",
            "Epoch 14/30\n",
            "540/540 [==============================] - 2s 4ms/step - loss: 0.0038 - val_loss: 0.0038\n",
            "Epoch 15/30\n",
            "540/540 [==============================] - 2s 5ms/step - loss: 0.0038 - val_loss: 0.0038\n",
            "Epoch 16/30\n",
            "540/540 [==============================] - 3s 5ms/step - loss: 0.0038 - val_loss: 0.0037\n",
            "Epoch 17/30\n",
            "540/540 [==============================] - 2s 4ms/step - loss: 0.0037 - val_loss: 0.0037\n",
            "Epoch 18/30\n",
            "540/540 [==============================] - 3s 5ms/step - loss: 0.0037 - val_loss: 0.0037\n",
            "Epoch 19/30\n",
            "540/540 [==============================] - 2s 5ms/step - loss: 0.0037 - val_loss: 0.0036\n",
            "Epoch 20/30\n",
            "540/540 [==============================] - 2s 5ms/step - loss: 0.0037 - val_loss: 0.0036\n",
            "Epoch 21/30\n",
            "540/540 [==============================] - 2s 4ms/step - loss: 0.0036 - val_loss: 0.0036\n",
            "Epoch 22/30\n",
            "540/540 [==============================] - 2s 5ms/step - loss: 0.0036 - val_loss: 0.0036\n",
            "Epoch 23/30\n",
            "540/540 [==============================] - 2s 4ms/step - loss: 0.0036 - val_loss: 0.0036\n",
            "Epoch 24/30\n",
            "540/540 [==============================] - 2s 4ms/step - loss: 0.0036 - val_loss: 0.0036\n",
            "Epoch 25/30\n",
            "540/540 [==============================] - 2s 4ms/step - loss: 0.0036 - val_loss: 0.0036\n",
            "Epoch 26/30\n",
            "540/540 [==============================] - 2s 4ms/step - loss: 0.0036 - val_loss: 0.0036\n",
            "Epoch 27/30\n",
            "540/540 [==============================] - 3s 5ms/step - loss: 0.0036 - val_loss: 0.0036\n",
            "Epoch 28/30\n",
            "540/540 [==============================] - 2s 5ms/step - loss: 0.0036 - val_loss: 0.0036\n",
            "Epoch 29/30\n",
            "540/540 [==============================] - 2s 5ms/step - loss: 0.0036 - val_loss: 0.0036\n",
            "Epoch 30/30\n",
            "540/540 [==============================] - 2s 5ms/step - loss: 0.0036 - val_loss: 0.0036\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A4olGGhSns9Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f3c87460-e949-4ae5-970c-37a5b07e298e"
      },
      "source": [
        "print(model.layers[3].get_weights()[1])\n",
        "print(decoder.layers[1].get_weights()[1])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 0.03502098 -0.08308267 -0.03485658  0.03824246 -0.1770572   0.07786264\n",
            " -0.09999742 -0.04380798  0.04617803 -0.10581758  0.07106038 -0.07099945\n",
            "  0.06594235 -0.14589463  0.07437038  0.10506365]\n",
            "[ 0.03502098 -0.08308267 -0.03485658  0.03824246 -0.1770572   0.07786264\n",
            " -0.09999742 -0.04380798  0.04617803 -0.10581758  0.07106038 -0.07099945\n",
            "  0.06594235 -0.14589463  0.07437038  0.10506365]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qzjzO1et-sHP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "056f2c28-43d0-40cf-80fb-a4edfcac3f6a"
      },
      "source": [
        "encoded_imgs = np.random.rand(1,7,7,8)\n",
        "decoded_imgs = decoder.predict(encoded_imgs)\n",
        "\n",
        "plt.imshow(decoded_imgs.reshape(28, 28))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fe23e274710>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "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",
      "metadata": {
        "id": "zGWb9bF30SDw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "outputId": "857a60c6-5cdb-4d2a-adad-6f1724c5cea1"
      },
      "source": [
        "encoded_imgs = np.random.rand(10,7,7,8)\n",
        "decoded_imgs = decoder.predict(encoded_imgs)\n",
        "\n",
        "n = 10 \n",
        "plt.figure(figsize=(20, 4))\n",
        "for i in range(n):\n",
        "    \n",
        "    ax = plt.subplot(2, n, i + 1 + n)\n",
        "    plt.imshow(decoded_imgs[i].reshape(28, 28))\n",
        "    plt.gray()\n",
        "    ax.get_xaxis().set_visible(False)\n",
        "    ax.get_yaxis().set_visible(False)\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}