CONVOLUTION_DECONVOLUTION_LAYERS.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CONVOLUTION_DECONVOLUTION_LAYERS.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "fg7xw7GeyoZt",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "4fe2a2b7-a966-4700-a7b8-00478d043d6a"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-9c0ee244-8c41-4724-b80a-30d88013c2a9\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-9c0ee244-8c41-4724-b80a-30d88013c2a9\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving A.png to A.png\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "nWPS4wTHBvMG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "outputId": "b1277869-660e-44af-96ed-fbfbd0b9fc16"
      },
      "cell_type": "code",
      "source": [
        "from IPython.display import Image\n",
        "Image('CONVOLUTION_DECONVOLUTION_PROCESS.png')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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FG7kDIEpkDgA7kGaA3eJYvJE7AKJE5gCwA2kGWC92xRu5AyBKZA4AO5BmQDWI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\nnmH0+uTwJOPXq/PKsobpr++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pDw50f82KQ+aYLp6Z48+VSJZnjr+IEykI55sz9ciGqYfL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\nrkJggg==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "_mpxf2kRBfOI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.layers import Input\n",
        "from keras.layers import Conv2DTranspose\n",
        "from keras.layers import Conv2D\n",
        "from keras.models import Model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "B4LLJeN8i7D6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "619891d3-4287-4476-ac27-1e977a37b399"
      },
      "cell_type": "code",
      "source": [
        "img_shape = (256,256,3)\n",
        "input_img = Input(shape=(img_shape))\n",
        "C1 = Conv2D(6,(2,2),strides=(2,2),padding='SAME',name='CONV1')(input_img)\n",
        "D1 = Conv2DTranspose(32, (3, 3), strides=(2,2), padding='same',name='TRANS_CONV1')(C1)\n",
        "C2 = Conv2D(6,(2,2),strides=(2,2),padding='SAME',name='CONV2')(D1)\n",
        "D2 = Conv2DTranspose(32, (3, 3), strides=(2,2), padding='same',name='TRANS_CONV2')(C2)\n",
        "C3 = Conv2D(6,(2,2),strides=(2,2),padding='SAME',name='CONV3')(D2)\n",
        "D3 = Conv2DTranspose(32, (3, 3), strides=(2,2), padding='same',name='TRANS_CONV3')(C3)\n",
        "C4 = Conv2D(6,(2,2),strides=(2,2),padding='SAME',name='CONV4')(D3)\n",
        "D4 = Conv2DTranspose(32, (3, 3), strides=(2,2), padding='same',name='TRANS_CONV4')(C4)\n",
        "model = Model(input_img, D4)\n",
        "model.summary()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_2 (InputLayer)         (None, 256, 256, 3)       0         \n",
            "_________________________________________________________________\n",
            "CONV1 (Conv2D)               (None, 128, 128, 6)       78        \n",
            "_________________________________________________________________\n",
            "TRANS_CONV1 (Conv2DTranspose (None, 256, 256, 32)      1760      \n",
            "_________________________________________________________________\n",
            "CONV2 (Conv2D)               (None, 128, 128, 6)       774       \n",
            "_________________________________________________________________\n",
            "TRANS_CONV2 (Conv2DTranspose (None, 256, 256, 32)      1760      \n",
            "_________________________________________________________________\n",
            "CONV3 (Conv2D)               (None, 128, 128, 6)       774       \n",
            "_________________________________________________________________\n",
            "TRANS_CONV3 (Conv2DTranspose (None, 256, 256, 32)      1760      \n",
            "_________________________________________________________________\n",
            "CONV4 (Conv2D)               (None, 128, 128, 6)       774       \n",
            "_________________________________________________________________\n",
            "TRANS_CONV4 (Conv2DTranspose (None, 256, 256, 32)      1760      \n",
            "=================================================================\n",
            "Total params: 9,440\n",
            "Trainable params: 9,440\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "axVwR41DCvD9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "05c5d4a5-c7fa-482c-c29b-0763584b0413"
      },
      "cell_type": "code",
      "source": [
        "from IPython.display import Image\n",
        "Image('A.png')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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nzHX/iUfO+Hu5bJQiWw0hlrPYXXbb+smilNrmhLs1XfQKmJ18UpsKrWTkTsNw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\n93z1dAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "id": "BK63LYF3zoLb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 980
        },
        "outputId": "af6dc34b-1ae3-4773-d259-07217d80c75b"
      },
      "cell_type": "code",
      "source": [
        "from IPython.display import SVG\n",
        "from keras.utils.vis_utils import model_to_dot\n",
        "SVG(model_to_dot(model, show_shapes=True).create(prog='dot', format='svg'))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
            "image/svg+xml": "<svg height=\"719pt\" viewBox=\"0.00 0.00 442.00 719.00\" width=\"442pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 715)\">\n<title>G</title>\n<polygon fill=\"#ffffff\" points=\"-4,4 -4,-715 438,-715 438,4 -4,4\" stroke=\"transparent\"/>\n<!-- 139889577396880 -->\n<g class=\"node\" id=\"node1\">\n<title>139889577396880</title>\n<polygon fill=\"none\" points=\"55.5,-664.5 55.5,-710.5 378.5,-710.5 378.5,-664.5 55.5,-664.5\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"122\" y=\"-683.8\">input_2: InputLayer</text>\n<polyline fill=\"none\" points=\"188.5,-664.5 188.5,-710.5 \" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"217.5\" y=\"-695.3\">input:</text>\n<polyline fill=\"none\" points=\"188.5,-687.5 246.5,-687.5 \" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"217.5\" y=\"-672.3\">output:</text>\n<polyline fill=\"none\" points=\"246.5,-664.5 246.5,-710.5 \" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"312.5\" y=\"-695.3\">(None, 256, 256, 3)</text>\n<polyline fill=\"none\" points=\"246.5,-687.5 378.5,-687.5 \" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"312.5\" y=\"-672.3\">(None, 256, 256, 3)</text>\n</g>\n<!-- 139889577396936 -->\n<g class=\"node\" id=\"node2\">\n<title>139889577396936</title>\n<polygon fill=\"none\" points=\"60.5,-581.5 60.5,-627.5 373.5,-627.5 373.5,-581.5 60.5,-581.5\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"122\" y=\"-600.8\">CONV1: 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          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "17NediAo675t",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}