minor_project.ipynb

{
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  "metadata": {
    "colab": {
      "name": "minor project.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "okdyN_8MdBrF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.datasets import mnist\n",
        "from keras.layers import LSTM, Dense, Dropout\n",
        "from keras.models import Sequential\n",
        "from keras.utils import np_utils\n",
        "\n",
        "import cv2\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "iJ4d-pOtdUQy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "a803f9ce-48d3-453c-e5c0-108268505f86"
      },
      "cell_type": "code",
      "source": [
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
      ],
      "execution_count": 3,
      "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"
        }
      ]
    },
    {
      "metadata": {
        "id": "hAUCvrN6dZvh",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#image generaion for given number \n",
        "X = []\n",
        "for i in range(len(y_train)):\n",
        "    if y_train[i] == 3:\n",
        "        X.append(x_train[i])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "DosDf9YalL8w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3c3144ab-953a-45a0-8899-8e6106d38ec0"
      },
      "cell_type": "code",
      "source": [
        "len(X)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6131"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "id": "xIyK-4VwdiM8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X= np.array(X)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qSLMElYZdzdd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X = np.reshape(X, (1, len(X), 28, 28))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "GhxCm5_Ed-sW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_train=[]\n",
        "Y_train =[]\n",
        "\n",
        "#as row by row and pixel by pixel approach\n",
        "for it in range(X.shape[1]):\n",
        "    for i in range(1,X.shape[2] -1):\n",
        "        for j in range(1,X.shape[3] -1):\n",
        "            X_train.append([X[0][it][i-1][j-1], X[0][it][i-1][j], X[0][it][i-1][j+1]])\n",
        "            Y_train.append(X[0][it][i][j])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PL0Q9eIn67J9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "31bd7289-ee3a-4719-dea8-923cbb539fe3"
      },
      "cell_type": "code",
      "source": [
        "X.shape[1]*(X.shape[2]-2)*(X.shape[3] -2)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4144556"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "id": "AG23esykeEac",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_train = np.reshape(X_train,(((X.shape[1])*(X.shape[2]-2)*(X.shape[3]-2)),1,3))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WS_6xhtcHBSo",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "Y_train = np.array(Y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rqH9Jv5jH8kw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "Y_train = np.reshape(Y_train,(((X.shape[1])*(X.shape[2]-2)*(X.shape[3]-2)),1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NCQlKJTdIOmG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "74b0a29c-5f54-4a8a-cbb9-6ecb6605133e"
      },
      "cell_type": "code",
      "source": [
        "X_train.shape, Y_train.shape #problem look here"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((4144556, 1, 3), (4144556, 1))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "id": "1kvvZvyGJHGe",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "Y_train = np_utils.to_categorical(Y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RNAGk8d8hP0U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a0071214-2c3a-42f4-857e-d41bc546004d"
      },
      "cell_type": "code",
      "source": [
        "Y_train.shape"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4144556, 256)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "id": "JhuUu4y6hQRt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HzxI3RmseI0h",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(256, input_shape=(X_train.shape[1],X_train.shape[2])))\n",
        "#model.add(LSTM(128, activation='softmax'))\n",
        "#model.add(Dropout(0.2))\n",
        "model.add(Dense(256, activation = 'softmax'))\n",
        "model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8NDLzE4EkkQB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "8cb9e2dc-cf72-4a9c-ff37-79694e904a3e"
      },
      "cell_type": "code",
      "source": [
        "model.fit(X_train, Y_train, epochs=4, batch_size = 32768)#this should work\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/4\n",
            "2097152/4144556 [==============>...............] - ETA: 2:34 - loss: 4.8769 - acc: 0.7660"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "85cUUN2El7b1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "outputId": "8244f58a-f0f4-4991-f377-baf9fb26425a"
      },
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_1 (LSTM)                (None, 256)               266240    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 256)               65792     \n",
            "=================================================================\n",
            "Total params: 332,032\n",
            "Trainable params: 332,032\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "vXsbKmkdZh74",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.save(\"mp_3.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Dr7VGoC1HTGw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "unique, counts = np.unique(X_train, return_counts=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZaVFMsc2HrL4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "d = dict(zip(unique, counts))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "DSsoIlFzHyY6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "04dee821-81c2-493a-a7e0-dae183588a60"
      },
      "cell_type": "code",
      "source": [
        "d[0]/(sum(d[i] for i in range(0,256)))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7899274006570643"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "metadata": {
        "id": "nH676uCBIIuQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_test = []\n",
        "Y_test = []\n",
        "for i in range(1,27):\n",
        "    for j in range(1,27):\n",
        "        X_test.append([X[0][0][i-1][j-1], X[0][0][i-1][j], X[0][0][i-1][j+1]])\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "avFeGY_YKSR7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "outputId": "15dad101-b68a-4ae0-eb48-dfb6bb08f0bb"
      },
      "cell_type": "code",
      "source": [
        "from keras.models import load_model\n",
        "test_model = load_model('mp_3.h5')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "error",
          "ename": "OSError",
          "evalue": "ignored",
          "traceback": [
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            "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
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            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/models.py\u001b[0m in \u001b[0;36mload_model\u001b[0;34m(filepath, custom_objects, compile)\u001b[0m\n\u001b[1;32m    258\u001b[0m     \u001b[0mopened_new_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh5py\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mopened_new_file\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 260\u001b[0;31m         \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5py\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    261\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    262\u001b[0m         \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfilepath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/h5py/_hl/files.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, mode, driver, libver, userblock_size, swmr, **kwds)\u001b[0m\n\u001b[1;32m    310\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mphil\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    311\u001b[0m                 \u001b[0mfapl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_fapl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdriver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlibver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 312\u001b[0;31m                 \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_fid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muserblock_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mswmr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mswmr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    313\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    314\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mswmr_support\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/h5py/_hl/files.py\u001b[0m in \u001b[0;36mmake_fid\u001b[0;34m(name, mode, userblock_size, fapl, fcpl, swmr)\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mswmr\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mswmr_support\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m             \u001b[0mflags\u001b[0m \u001b[0;34m|=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mACC_SWMR_READ\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 142\u001b[0;31m         \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfapl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'r+'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m         \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mACC_RDWR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfapl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mh5py/h5f.pyx\u001b[0m in \u001b[0;36mh5py.h5f.open\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mOSError\u001b[0m: Unable to open file (unable to open file: name = 'mp_3.h5', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "SxSciNufKn-n",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_test = np.reshape(X_test,(len(X_test), 1, 3))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-O5Ha8C1KgPa",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "prediction = test_model.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ABYsF0snPhlu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "ce5c4ce0-67bd-40e8-d36b-7c49e6e422c4"
      },
      "cell_type": "code",
      "source": [
        "prediction"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[9.7776049e-01, 2.1709326e-04, 2.9674260e-04, ..., 6.2979979e-04,\n",
              "        1.3829813e-03, 1.2588369e-03],\n",
              "       [9.7776049e-01, 2.1709326e-04, 2.9674260e-04, ..., 6.2979979e-04,\n",
              "        1.3829813e-03, 1.2588369e-03],\n",
              "       [9.7776049e-01, 2.1709326e-04, 2.9674260e-04, ..., 6.2979979e-04,\n",
              "        1.3829813e-03, 1.2588369e-03],\n",
              "       ...,\n",
              "       [9.7776049e-01, 2.1709326e-04, 2.9674260e-04, ..., 6.2979979e-04,\n",
              "        1.3829813e-03, 1.2588369e-03],\n",
              "       [9.7776049e-01, 2.1709326e-04, 2.9674260e-04, ..., 6.2979979e-04,\n",
              "        1.3829813e-03, 1.2588369e-03],\n",
              "       [9.7776049e-01, 2.1709326e-04, 2.9674260e-04, ..., 6.2979979e-04,\n",
              "        1.3829813e-03, 1.2588369e-03]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "metadata": {
        "id": "chK_SU7tK7U2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "86541742-a9b2-4114-a483-35a0d4e2f152"
      },
      "cell_type": "code",
      "source": [
        "list(prediction[0]).index(max(prediction[0]))"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "metadata": {
        "id": "cA0TxsXBOWvJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "Y_test = []\n",
        "for i in range(prediction.shape[0]):\n",
        "  v = list(prediction[i]).index(max(prediction[i]))\n",
        "  Y_test.append(v)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "AFb9edN6OgRy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "unique, count = np.unique(np.array(Y_test),return_counts = True )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3SBpegWYcmFL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9fc48851-63bb-4322-9483-2d1936cd9ecc"
      },
      "cell_type": "code",
      "source": [
        "dict(zip(unique, count))"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 598, 253: 74, 254: 1, 255: 3}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "metadata": {
        "id": "cpxHq_H6gCBH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "Y_test = np.reshape(Y_test,(26, 26))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MwVnADjwgM6A",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "np.save(\"mp.npy\",Y_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "fvLNKZKpg4xS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3fb0d7fc-78f0-4a04-9902-708427a0e0a1"
      },
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mp.h5  mp.npy  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "POzYvArEjDDr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2de9bd33-2b0d-4516-fdca-10044439dc25"
      },
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mp.h5  mp.npy  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "e6BJ7Or7jE2B",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6kS1CcWDjcXj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "mp = np.load(\"mp.npy\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "z5Stj5Hejsbw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "c094187a-1085-4109-917f-9ed8af42408e"
      },
      "cell_type": "code",
      "source": [
        "plt.imshow(mp)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f7915616828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Llqwsu7u7c/78+dGPv/zyyyxZsqQVozRNT09PHnnkkTQajdx555254447Mjg42Oqxmqaz\nszNXrlxJkgwODs7aS9fe3t4sX748yf+78fWZM2daPNHE/efm3gcPHszChQtn7bn88XHe7LlsSSwf\neOCBHD16NEly+vTpdHd359Zbb23FKE3z3nvv5dChQ0mSoaGhfPXVV+np6WnxVM2zcuXK0XN67Nix\nrFq1qsUTNcdsu/H1jW7uPRvP5WTcxLxldx3au3dvPv744zQajbz88sv59a9/3YoxmubixYt5/vnn\n8+233+batWvZsmVLHnzwwVaPNSlOnTqV3bt359y5c2lvb09PT0/27t2bbdu25erVq1m6dGl27dqV\n+fPnt3rUCbnRcW7cuDEHDhy47sbXXV1drR513Pr6+vLWW2/lrrvuGn3stddey4svvjirzuWNjvPx\nxx/PkSNHyufSLdoACuzgASgQS4ACsQQoEEuAArEEKBBLgAKxBCgQS4CC/wMpb2efwbXDKAAAAABJ\nRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f791573f2b0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "vn-D95sqjvpw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}