fashionMNIST.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "fashionMNIST.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "F82gCB2ol0w6",
        "colab_type": "code",
        "outputId": "c9de5bcc-e470-4c26-eba6-17f14f94df50",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        }
      },
      "source": [
        "from google.colab import drive\n",
        "\n",
        "# This will prompt for authorization.\n",
        "drive.mount('/content/drive')\n",
        "print('Drive Mounted')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n",
            "Drive Mounted\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "ec9bb5c8-c7e3-400e-b493-6697608b0d4e",
        "id": "w41oEqRfnETI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        }
      },
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Dropout\n",
        "from keras.layers import Flatten\n",
        "from sklearn.preprocessing import LabelEncoder,OneHotEncoder\n",
        "from keras import backend as K\n",
        "from keras.models import Sequential\n",
        "from keras.layers.core import Dense, Dropout, Activation, Flatten\n",
        "from keras.layers.convolutional import Convolution2D, MaxPooling2D\n",
        "\n",
        "from keras.optimizers import adam\n",
        "from keras.models import Model\n",
        "from keras.layers import merge,core\n",
        "from keras.engine.topology import Input\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "\n",
        "from subprocess import check_output\n",
        "print(check_output([\"ls\", \"drive/My Drive/FashionMnist\"]).decode(\"utf8\"))\n",
        "K.set_image_dim_ordering('th')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fashion-mnist_test.csv\n",
            "fashion-mnist_train.csv\n",
            "t10k-images-idx3-ubyte\n",
            "t10k-labels-idx1-ubyte\n",
            "train-images-idx3-ubyte\n",
            "train-labels-idx1-ubyte\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K9ou1d5FohdC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = pd.read_csv(\"drive/My Drive/FashionMnist/fashion-mnist_train.csv\").values\n",
        "test  = pd.read_csv(\"drive/My Drive/FashionMnist/fashion-mnist_test.csv\").values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fgp2EGOQo6u3",
        "colab_type": "code",
        "outputId": "4c8719c5-dd05-4259-e2e3-fc64b45ea0a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "# Reshape and normalize training data\n",
        "trainX = train[:, 1:].reshape(train.shape[0],1,28, 28).astype( 'float32' )\n",
        "\n",
        "X_train1=trainX/255.0\n",
        "X_train=X_train1[:,:,:,:]\n",
        "y_train=train[:,0]\n",
        "\n",
        "\n",
        "print(\"Xtrain.shape\",X_train.shape)\n",
        "print(\"ytrain.shape\",y_train.shape)\n",
        "\n",
        "\n",
        "# Reshape and normalize test data\n",
        "testX = test[:,1:].reshape(test.shape[0],1, 28, 28).astype( 'float32' )\n",
        "X_test = testX / 255.0\n",
        "\n",
        "y_test = test[:,0]\n",
        "print(\"Xtest.shape\",X_test.shape)\n",
        "print(\"ytest.shape\",y_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Xtrain.shape (60000, 1, 28, 28)\n",
            "ytrain.shape (60000,)\n",
            "Xtest.shape (10000, 1, 28, 28)\n",
            "ytest.shape (10000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tJ9xLa9mphab",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#One-Hot Encoding the Label to Output\n",
        "from sklearn import preprocessing\n",
        "lb = preprocessing.LabelBinarizer()\n",
        "y_train = lb.fit_transform(y_train)\n",
        "y_test = lb.fit_transform(y_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YUs2Mlzjp05K",
        "colab_type": "code",
        "outputId": "8d0454e4-ce61-4855-df87-6488cd59b368",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1086
        }
      },
      "source": [
        "model = Sequential()\n",
        "\n",
        "#model.add(Convolution2D(32, (3, 3), activation='relu', input_shape=(1,28,28), data_format='channels_first'))\n",
        "\n",
        "model.add(Convolution2D(16, (3,3), border_mode='valid',input_shape=(1,28,28))) #1st\n",
        "model.add(Activation('relu'))\n",
        "model.add(BatchNormalization(axis=2))\n",
        "model.add(Convolution2D(16, (3, 3))) # 2nd\n",
        "model.add(Activation('relu'))\n",
        "model.add(BatchNormalization(axis=2))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Convolution2D(32, (3,3),border_mode='valid')) #1st\n",
        "model.add(Activation('relu'))\n",
        "model.add(BatchNormalization(axis=2))\n",
        "model.add(Convolution2D(32, (3, 3))) # 2nd\n",
        "model.add(Activation('relu'))\n",
        "model.add(BatchNormalization(axis=2))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "\"\"\"model.add(Convolution2D(64, (3,3), data_format='channels_first')) #3rd\n",
        "model.add(Activation('relu'))\n",
        "model.add(BatchNormalization(axis=2))\n",
        "model.add(Convolution2D(64, (3,3), data_format='channels_first')) #4th\n",
        "model.add(Activation('relu'))\n",
        "model.add(BatchNormalization(axis=2))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2))) \n",
        "model.add(Dropout(0.2))\n",
        "          \n",
        "model.add(Convolution2D(128, (3,3),data_format='channels_first')) #5th\n",
        "model.add(Activation('relu'))\n",
        "model.add(BatchNormalization(axis=2))\n",
        "model.add(Convolution2D(128, (3, 3),data_format='channels_first')) #6th\n",
        "model.add(Activation('relu'))\n",
        "model.add(BatchNormalization(axis=2))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.2))\"\"\"\n",
        "\n",
        "\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dense(64))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(10))\n",
        "model.add(Activation('softmax'))\n",
        "\n",
        "\n",
        "\n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=[\"accuracy\"])\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(16, (3, 3), input_shape=(1, 28, 28..., padding=\"valid\")`\n",
            "  \"\"\"\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), padding=\"valid\")`\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_5 (Conv2D)            (None, 16, 26, 26)        160       \n",
            "_________________________________________________________________\n",
            "activation_8 (Activation)    (None, 16, 26, 26)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 16, 26, 26)        104       \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 16, 24, 24)        2320      \n",
            "_________________________________________________________________\n",
            "activation_9 (Activation)    (None, 16, 24, 24)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 16, 24, 24)        96        \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 16, 12, 12)        0         \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 16, 12, 12)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 32, 10, 10)        4640      \n",
            "_________________________________________________________________\n",
            "activation_10 (Activation)   (None, 32, 10, 10)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_7 (Batch (None, 32, 10, 10)        40        \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 32, 8, 8)          9248      \n",
            "_________________________________________________________________\n",
            "activation_11 (Activation)   (None, 32, 8, 8)          0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_8 (Batch (None, 32, 8, 8)          32        \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 32, 4, 4)          0         \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 32, 4, 4)          0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 128)               65664     \n",
            "_________________________________________________________________\n",
            "activation_12 (Activation)   (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 64)                8256      \n",
            "_________________________________________________________________\n",
            "activation_13 (Activation)   (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dropout_6 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 10)                650       \n",
            "_________________________________________________________________\n",
            "activation_14 (Activation)   (None, 10)                0         \n",
            "=================================================================\n",
            "Total params: 91,210\n",
            "Trainable params: 91,074\n",
            "Non-trainable params: 136\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dg0mdzz2sMwE",
        "colab_type": "code",
        "outputId": "429aa52b-e077-48d9-86a3-6ae530107776",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1475
        }
      },
      "source": [
        "from keras import callbacks\n",
        "checkpointer = callbacks.ModelCheckpoint(filepath='drive/My Drive/COLAB Programs/fashionMNIST/_best_weights.h5', verbose=1, monitor='loss', mode='auto', save_best_only=True)\n",
        "model.fit(X_train, y_train,\n",
        "          epochs=20,\n",
        "          verbose=1, callbacks=[checkpointer])\n",
        "score = model.evaluate(X_test, y_test, batch_size=128)\n",
        "print(\"Score\",score)\n",
        "model.save(\"drive/My Drive/COLAB Programs/fashionMNIST/model.hdf5\")\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "60000/60000 [==============================] - 38s 635us/step - loss: 0.5335 - acc: 0.8066\n",
            "\n",
            "Epoch 00001: loss improved from inf to 0.53351, saving model to _best_weights.h5\n",
            "Epoch 2/20\n",
            "60000/60000 [==============================] - 36s 604us/step - loss: 0.3501 - acc: 0.8726\n",
            "\n",
            "Epoch 00002: loss improved from 0.53351 to 0.35009, saving model to _best_weights.h5\n",
            "Epoch 3/20\n",
            "60000/60000 [==============================] - 36s 602us/step - loss: 0.3013 - acc: 0.8910\n",
            "\n",
            "Epoch 00003: loss improved from 0.35009 to 0.30126, saving model to _best_weights.h5\n",
            "Epoch 4/20\n",
            "60000/60000 [==============================] - 36s 604us/step - loss: 0.2742 - acc: 0.9014\n",
            "\n",
            "Epoch 00004: loss improved from 0.30126 to 0.27420, saving model to _best_weights.h5\n",
            "Epoch 5/20\n",
            "60000/60000 [==============================] - 36s 606us/step - loss: 0.2509 - acc: 0.9098\n",
            "\n",
            "Epoch 00005: loss improved from 0.27420 to 0.25087, saving model to _best_weights.h5\n",
            "Epoch 6/20\n",
            "60000/60000 [==============================] - 37s 608us/step - loss: 0.2372 - acc: 0.9137\n",
            "\n",
            "Epoch 00006: loss improved from 0.25087 to 0.23722, saving model to _best_weights.h5\n",
            "Epoch 7/20\n",
            "60000/60000 [==============================] - 37s 609us/step - loss: 0.2257 - acc: 0.9174\n",
            "\n",
            "Epoch 00007: loss improved from 0.23722 to 0.22574, saving model to _best_weights.h5\n",
            "Epoch 8/20\n",
            "60000/60000 [==============================] - 37s 613us/step - loss: 0.2137 - acc: 0.9224\n",
            "\n",
            "Epoch 00008: loss improved from 0.22574 to 0.21372, saving model to _best_weights.h5\n",
            "Epoch 9/20\n",
            "60000/60000 [==============================] - 37s 615us/step - loss: 0.2067 - acc: 0.9241\n",
            "\n",
            "Epoch 00009: loss improved from 0.21372 to 0.20672, saving model to _best_weights.h5\n",
            "Epoch 10/20\n",
            "60000/60000 [==============================] - 37s 614us/step - loss: 0.1972 - acc: 0.9273\n",
            "\n",
            "Epoch 00010: loss improved from 0.20672 to 0.19724, saving model to _best_weights.h5\n",
            "Epoch 11/20\n",
            "60000/60000 [==============================] - 37s 610us/step - loss: 0.1935 - acc: 0.9288\n",
            "\n",
            "Epoch 00011: loss improved from 0.19724 to 0.19355, saving model to _best_weights.h5\n",
            "Epoch 12/20\n",
            "60000/60000 [==============================] - 36s 607us/step - loss: 0.1843 - acc: 0.9309\n",
            "\n",
            "Epoch 00012: loss improved from 0.19355 to 0.18428, saving model to _best_weights.h5\n",
            "Epoch 13/20\n",
            "60000/60000 [==============================] - 36s 605us/step - loss: 0.1804 - acc: 0.9326\n",
            "\n",
            "Epoch 00013: loss improved from 0.18428 to 0.18043, saving model to _best_weights.h5\n",
            "Epoch 14/20\n",
            "60000/60000 [==============================] - 37s 609us/step - loss: 0.1744 - acc: 0.9361\n",
            "\n",
            "Epoch 00014: loss improved from 0.18043 to 0.17436, saving model to _best_weights.h5\n",
            "Epoch 15/20\n",
            "60000/60000 [==============================] - 36s 607us/step - loss: 0.1691 - acc: 0.9373\n",
            "\n",
            "Epoch 00015: loss improved from 0.17436 to 0.16911, saving model to _best_weights.h5\n",
            "Epoch 16/20\n",
            "60000/60000 [==============================] - 36s 606us/step - loss: 0.1650 - acc: 0.9381\n",
            "\n",
            "Epoch 00016: loss improved from 0.16911 to 0.16505, saving model to _best_weights.h5\n",
            "Epoch 17/20\n",
            "60000/60000 [==============================] - 37s 612us/step - loss: 0.1615 - acc: 0.9402\n",
            "\n",
            "Epoch 00017: loss improved from 0.16505 to 0.16153, saving model to _best_weights.h5\n",
            "Epoch 18/20\n",
            "60000/60000 [==============================] - 36s 605us/step - loss: 0.1542 - acc: 0.9422\n",
            "\n",
            "Epoch 00018: loss improved from 0.16153 to 0.15418, saving model to _best_weights.h5\n",
            "Epoch 19/20\n",
            "60000/60000 [==============================] - 36s 604us/step - loss: 0.1516 - acc: 0.9437\n",
            "\n",
            "Epoch 00019: loss improved from 0.15418 to 0.15155, saving model to _best_weights.h5\n",
            "Epoch 20/20\n",
            "60000/60000 [==============================] - 37s 609us/step - loss: 0.1472 - acc: 0.9449\n",
            "\n",
            "Epoch 00020: loss improved from 0.15155 to 0.14721, saving model to _best_weights.h5\n",
            "10000/10000 [==============================] - 1s 90us/step\n",
            "Score [0.18638661918640137, 0.9355]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1GgdHDXW0xAo",
        "colab_type": "code",
        "outputId": "ad776acd-232b-4651-e93d-c591185c7f75",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "from PIL import Image\n",
        "import cv2\n",
        "from numpy import genfromtxt\n",
        "\n",
        "temp=X_test[53][0]\n",
        "\n",
        "im=Image.fromarray(temp)\n",
        "im= np.expand_dims(im, axis=0)\n",
        "im= np.expand_dims(im, axis=0)\n",
        "\n",
        "s=model.predict_classes(im)\n",
        "print(\"Predict_Classes\",model.predict_classes(im))\n",
        "print(\"Predict: \",model.predict(im))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predict_Classes [3]\n",
            "Predict:  [[1.0531291e-02 2.4761271e-06 2.3721055e-05 9.8785257e-01 5.2752435e-05\n",
            "  4.5150207e-07 1.3730986e-03 3.4634806e-07 1.6323850e-04 1.2874695e-07]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-hiKy3DzzWhc",
        "colab_type": "code",
        "outputId": "bb308790-4dec-4a2f-f87d-451271ef5d62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1233
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import classification_report,confusion_matrix\n",
        "import itertools\n",
        "\n",
        "Y_pred = model.predict(X_test)\n",
        "#print(\"#################################\")\n",
        "#print(\"Y_pred\",Y_pred)\n",
        "y_pred = np.argmax(Y_pred, axis=1)\n",
        "#print(\"y_pred\",y_pred)\n",
        "#print(\"#################################\")\n",
        "y_pred = model.predict_classes(X_test)\n",
        "#print(\"y_pred classes: \",y_pred)\n",
        "target_names = ['0','1','2','3','4','5','6','7','8','9']\n",
        "\t\t\t\t\t\n",
        "print(classification_report(np.argmax(y_test,axis=1), y_pred,target_names=target_names))\n",
        "\n",
        "print(confusion_matrix(np.argmax(y_test,axis=1), y_pred))\n",
        "\n",
        "\n",
        "# Plotting the confusion matrix\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"8--Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('8--Confusion matrix, without normalization')\n",
        "\n",
        "    print(\"9\",cm)\n",
        "\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, cm[i, j],\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "\n",
        "# Compute confusion matrix\n",
        "cnf_matrix = (confusion_matrix(np.argmax(y_test,axis=1), y_pred))\n",
        "\n",
        "np.set_printoptions(precision=2)\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "# Plot non-normalized confusion matrix\n",
        "plot_confusion_matrix(cnf_matrix, classes=target_names,\n",
        "                      title='Confusion matrix')\n",
        "#plt.figure()\n",
        "# Plot normalized confusion matrix\n",
        "#plot_confusion_matrix(cnf_matrix, classes=target_names, normalize=True,\n",
        "#                      title='Normalized confusion matrix')\n",
        "#plt.figure()\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Y_pred [[9.99e-01 1.65e-08 5.31e-04 ... 4.11e-12 7.53e-07 1.90e-13]\n",
            " [7.23e-18 1.00e+00 2.70e-18 ... 0.00e+00 1.02e-16 2.80e-34]\n",
            " [7.02e-03 2.95e-09 9.55e-01 ... 9.73e-12 8.52e-07 1.92e-11]\n",
            " ...\n",
            " [6.19e-12 7.86e-20 2.32e-15 ... 2.19e-24 1.00e+00 1.90e-25]\n",
            " [7.01e-07 2.32e-09 2.84e-09 ... 1.66e-14 1.00e+00 4.13e-15]\n",
            " [2.38e-09 1.00e+00 3.99e-09 ... 3.18e-19 2.09e-09 3.40e-18]]\n",
            "y_pred classes:  [0 1 2 ... 8 8 1]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.90      0.88      1000\n",
            "           1       0.99      1.00      0.99      1000\n",
            "           2       0.92      0.88      0.90      1000\n",
            "           3       0.94      0.94      0.94      1000\n",
            "           4       0.88      0.94      0.91      1000\n",
            "           5       0.99      0.98      0.99      1000\n",
            "           6       0.83      0.78      0.80      1000\n",
            "           7       0.97      0.97      0.97      1000\n",
            "           8       0.99      0.99      0.99      1000\n",
            "           9       0.97      0.98      0.98      1000\n",
            "\n",
            "   micro avg       0.94      0.94      0.94     10000\n",
            "   macro avg       0.94      0.94      0.94     10000\n",
            "weighted avg       0.94      0.94      0.94     10000\n",
            "\n",
            "[[896   1  17  11   0   0  71   0   4   0]\n",
            " [  0 997   0   2   0   0   1   0   0   0]\n",
            " [ 15   0 881  15  54   0  35   0   0   0]\n",
            " [ 11   4   3 944  23   0  15   0   0   0]\n",
            " [  0   1  18  12 938   0  31   0   0   0]\n",
            " [  0   0   0   0   0 985   0   8   1   6]\n",
            " [109   5  37  21  46   0 778   0   4   0]\n",
            " [  0   0   0   0   0  11   0 967   0  22]\n",
            " [  2   2   0   0   2   0   3   0 991   0]\n",
            " [  0   0   0   0   0   1   0  21   0 978]]\n",
            "8--Confusion matrix, without normalization\n",
            "9 [[896   1  17  11   0   0  71   0   4   0]\n",
            " [  0 997   0   2   0   0   1   0   0   0]\n",
            " [ 15   0 881  15  54   0  35   0   0   0]\n",
            " [ 11   4   3 944  23   0  15   0   0   0]\n",
            " [  0   1  18  12 938   0  31   0   0   0]\n",
            " [  0   0   0   0   0 985   0   8   1   6]\n",
            " [109   5  37  21  46   0 778   0   4   0]\n",
            " [  0   0   0   0   0  11   0 967   0  22]\n",
            " [  2   2   0   0   2   0   3   0 991   0]\n",
            " [  0   0   0   0   0   1   0  21   0 978]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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POHbsGAnPbZMnGRlYWvz73idKzqCkHHlh6kd8eit8tWrV0txuplChQqSmpvLk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\nDDF2ZYyBAgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}