CNN.ipynb

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    "colab": {
      "name": "CNN.ipynb",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyOAMPomFoZCmq6Yj/mkPf1U",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Zualemo-xo/Convolutional-Neural-Network--Number-Recognition/blob/master/CNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZNfuv5MptPmL",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f9df6bae-3a33-4764-d668-e691db07079d"
      },
      "source": [
        "import keras"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y0yF9rl6_Lbb",
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        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Am6kNbyF_HQW",
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        "colab": {}
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      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DBDZSAymtxPd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.datasets import mnist"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L41fUAaMuKNv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "91886d1e-0a04-482b-a6a0-d0cb4c6bb733"
      },
      "source": [
        "data=mnist.load_data()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qRi0YVIxuQlQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(train_images,train_labels),(test_images,test_labels)=data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yduJ__apunbf",
        "colab_type": "code",
        "colab": {
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          "height": 34
        },
        "outputId": "4a81e793-8b0f-49ea-940a-564303c0265a"
      },
      "source": [
        "#len(train_images)\n",
        "len(train_labels)\n",
        "len(test_images)\n",
        "#train_images[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KLo6mwXAut1T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_images=train_images.reshape((60000,28,28,1))#keras wants 4d arrray for BW=1 channel"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u5btmErEwbKt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_images=test_images.reshape((10000,28,28,1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvaTmZFiw6bK",
        "colab_type": "code",
        "colab": {
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          "height": 1000
        },
        "outputId": "c63a53e6-8c27-4db4-e18c-6e9fe0c52f62"
      },
      "source": [
        "train_images[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "        [ 80],\n",
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              "\n",
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              "       [[  0],\n",
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              "\n",
              "       [[  0],\n",
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              "\n",
              "       [[  0],\n",
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              "        [  0],\n",
              "        [  0],\n",
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              "        [  0],\n",
              "        [  0],\n",
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              "        [  0],\n",
              "        [  0],\n",
              "        [  0],\n",
              "        [  0],\n",
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              "        [  0],\n",
              "        [  0],\n",
              "        [  0],\n",
              "        [  0]]], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iG8M3alzxTLR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_images=train_images.astype('float')/255#normalization"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ig0NcXKsx6xQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#train_images[0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UaxzetqFx9qN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_images=test_images.astype('float')/255"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "deVJSPw8yHBl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.preprocessing import image\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OHsiG9Emyby8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "139a5189-4035-4841-81da-3acacd777f42"
      },
      "source": [
        "plt.imshow(image.array_to_img(train_images[420]),cmap=\"gray\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f86cd90ceb8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CNXbwCLoy3oC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e6fc4031-0af1-49e5-b9c4-870997395286"
      },
      "source": [
        "train_labels[420]#to check graph 1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fahdmJXOzRLM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.utils import to_categorical"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Iwyvlb7z4zo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_labels_original=train_labels"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I4H-ef8Fz_QB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_labels=to_categorical(train_labels)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R27gpICr0Lfm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "425076d0-095f-484c-e206-b0111712cb26"
      },
      "source": [
        "train_labels[420]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., 0., 0., 1., 0., 0., 0., 0.], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gClNKJoF0Q5F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_labels_original=test_labels"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C0Q6M_FS0sot",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_labels=to_categorical(test_labels) ##########check out"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3bdkbMGj0yGH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "24203f2c-f564-43fe-d70b-00e22a702b61"
      },
      "source": [
        "test_labels[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., 0., 0., 0., 0., 1., 0., 0.], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U_O8YwMd00C1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras import layers,models"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r412FjNk1Cs9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model=models.Sequential()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X-yLdpi_1Krh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.add(layers.Conv2D(32,(3,3),activation='relu',input_shape = (28,28,1)))#1ST LAYER IS A CONVOLUTION 2D(IMAGE) LAYER,32 BEST NO OF NEURONS,KERNEL SIZE 3*3"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OfGjyVHW2mtK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        },
        "outputId": "f791a710-db7d-4f83-8a33-cea388f4f920"
      },
      "source": [
        "model.summary()#imput dimension-filter+1 so 28-3+1,32 is no of neurons"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_4 (Conv2D)            (None, 26, 26, 32)        320       \n",
            "=================================================================\n",
            "Total params: 320\n",
            "Trainable params: 320\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OhxTcRCk34IV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.add(layers.MaxPool2D(2,2))#MAX POOLING LAYER REDUCES SIZE/2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PNXNF4fI4JtD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "693871b2-79c8-4ff6-b6ff-61dfa3578149"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_4 (Conv2D)            (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         \n",
            "=================================================================\n",
            "Total params: 320\n",
            "Trainable params: 320\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qs3BpnAf4Soi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.add(layers.Conv2D(64,(3,3),activation='relu'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9al7SJ5r5Fvg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "ceca3109-ec86-4193-e461-024f40fb1258"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_4 (Conv2D)            (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 11, 11, 64)        18496     \n",
            "=================================================================\n",
            "Total params: 18,816\n",
            "Trainable params: 18,816\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d7NMcaHJ5Hts",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.add(layers.MaxPool2D((2,2)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WeAJWHlA5W_s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "43c615cf-c1ac-47e0-cf83-ee9a5b2737f8"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_4 (Conv2D)            (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 11, 11, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64)          0         \n",
            "=================================================================\n",
            "Total params: 18,816\n",
            "Trainable params: 18,816\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "04uG0JB35bcG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.add(layers.Conv2D(64,(3,3),activation='relu'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0fX75etZ5ieJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "outputId": "8a16c77e-8420-415a-ec20-50104e793382"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_4 (Conv2D)            (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 11, 11, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 3, 3, 64)          36928     \n",
            "=================================================================\n",
            "Total params: 55,744\n",
            "Trainable params: 55,744\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xCetZP8Y5nWE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.add(layers.Flatten())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rNXBNkj55y9W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "outputId": "0cc7de59-8045-47e8-e4c8-76d9d40accce"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_4 (Conv2D)            (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 11, 11, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 3, 3, 64)          36928     \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 576)               0         \n",
            "=================================================================\n",
            "Total params: 55,744\n",
            "Trainable params: 55,744\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MSAxSo6x52kX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.add(layers.Dense(64,activation='relu'))#normal neural network after flattening 64 is the best possible by resarch\n",
        "model.add(layers.Dense(10,activation='softmax'))# 10 class problem(0-9 numbers)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VhWv1hiZ6pS4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "outputId": "a33d8bdd-364b-4aed-cd37-d45eb228414f"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_4 (Conv2D)            (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 11, 11, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 3, 3, 64)          36928     \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 576)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 64)                36928     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 93,322\n",
            "Trainable params: 93,322\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tqUk3ii-7LDp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(loss='categorical_crossentropy',optimizer = 'rmsprop', metrics=['accuracy']) #rmsprop like gradient descent"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UDc3wUI371Yw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "outputId": "d1696be5-b1cd-48fa-c291-00c92d5d48ef"
      },
      "source": [
        "model.fit(train_images,train_labels,epochs = 15,batch_size = 64)#back propagation,non deterministic g descent"
      ],
      "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-1-404aa7eef646>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_images\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtrain_labels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m#back propagation,non deterministic g descent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8xOvaDYp-N9M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e9512614-2551-45b2-bc0c-bfdba916cc64"
      },
      "source": [
        "test_loss,test_accuracy=model.evaluate(test_images,test_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 1s 111us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X0rY4Gia-aKe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f4cbb494-f5ae-4650-f84e-130756aaa3d4"
      },
      "source": [
        "test_accuracy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9897000193595886"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KxaE-xef-c2f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictions=model.predict_classes(test_images)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "49EROKhoDfvf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "63736cd6-020a-4a91-b18d-852139c59135"
      },
      "source": [
        "plt.imshow(image.array_to_img(test_images[137]),cmap=\"gray\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f86c0141390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cNRLicZ9Ds3q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6111f78d-142b-4796-84eb-68d050e1c5c0"
      },
      "source": [
        "predictions[137]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xPPwzVcIECsk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "img=image>(,grayscale=True,target_size=(28,28,1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y3Ongd6IEUMt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "img=image.img_to_array(img)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "79X4yPHREZeB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_img=img.reshape((1,28,28,1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gdgYC3YrEkh4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "img_prediction=model.predict_classes(test_img)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DiWtA9-hFaOU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "aea35905-da15-46e9-a830-290b83affd2d"
      },
      "source": [
        "img = image.load_img(path=\"/6.jpeg\",grayscale=True,target_size=(28,28,1))\n",
        "img = image.img_to_array(img)\n",
        "test_img = img.reshape((1,28,28,1))\n",
        "test_img=test_img.astype('float')/255\n",
        "img_class = model.predict_classes(test_img)\n",
        "prediction = img_class[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/utils.py:107: UserWarning: grayscale is deprecated. Please use color_mode = \"grayscale\"\n",
            "  warnings.warn('grayscale is deprecated. Please use '\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rk3k5fgEGkyD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "dbf4c348-a146-47a4-903e-981d458c45db"
      },
      "source": [
        "prediction"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mDZXEJ2nHeG6",
        "colab_type": "text"
      },
      "source": [
        "#increase epochs"
      ]
    }
  ]
}