Lab2_CT221.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import mean\n",
    "from numpy import std\n",
    "from matplotlib import pyplot as plt\n",
    "import mplcyberpunk\n",
    "from sklearn.model_selection import KFold\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.datasets import mnist\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D\n",
    "from tensorflow.keras.layers import MaxPooling2D\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.layers import Flatten\n",
    "from tensorflow.keras.optimizers import SGD\n",
    "from keras.optimizers import RMSprop\n",
    "from keras.preprocessing.image import load_img\n",
    "from keras.preprocessing.image import img_to_array\n",
    "from keras.models import load_model\n",
    "from keras.utils import plot_model\n",
    " \n",
    "from tensorflow.keras.datasets import mnist\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: X=(60000, 28, 28), y=(60000,)\n",
      "Test: X=(10000, 28, 28), y=(10000,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load dataset\n",
    "plt.style.use('cyberpunk')\n",
    "(trainX, trainy), (testX, testy) = mnist.load_data()\n",
    "# summarize loaded dataset\n",
    "print('Train: X=%s, y=%s' % (trainX.shape, trainy.shape))\n",
    "print('Test: X=%s, y=%s' % (testX.shape, testy.shape))\n",
    "# plot first few images\n",
    "for i in range(9):\n",
    "\t# define subplot\n",
    "\tplt.subplot(330 + 1 + i)\n",
    "\t# plot raw pixel data\n",
    "\tplt.imshow(trainX[i], cmap=plt.get_cmap('gray'))\n",
    "\tplt.grid(False)\n",
    "# show the figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load train and test dataset\n",
    "def load_dataset():\n",
    " # load dataset\n",
    " (trainX, trainY), (testX, testY) = mnist.load_data()\n",
    " # reshape dataset to have a single channel\n",
    " trainX = trainX.reshape((trainX.shape[0], 28, 28, 1))\n",
    " testX = testX.reshape((testX.shape[0], 28, 28, 1))\n",
    " # one hot encode target values\n",
    " trainY = to_categorical(trainY)\n",
    " testY = to_categorical(testY)\n",
    " return trainX, trainY, testX, testY\n",
    "\n",
    "# scale pixels\n",
    "def prep_pixels(train, test):\n",
    " # convert from integers to floats\n",
    " train_norm = train.astype('float32')\n",
    " test_norm = test.astype('float32')\n",
    " # normalize to range 0-1\n",
    " train_norm = train_norm / 255.0\n",
    " test_norm = test_norm / 255.0\n",
    " # return normalized images\n",
    " return train_norm, test_norm\n",
    "\n",
    "# define cnn model\n",
    "def define_model():\n",
    "\tmodel = Sequential()\n",
    "\tmodel.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', input_shape=(28, 28, 1)))\n",
    "\tmodel.add(MaxPooling2D((2, 2)))\n",
    "\tmodel.add(Flatten())\n",
    "\tmodel.add(Dense(100, activation='relu', kernel_initializer='he_uniform'))\n",
    "\tmodel.add(Dense(10, activation='softmax'))\n",
    "\t# compile model\n",
    "\topt = SGD(learning_rate=0.01, momentum=0.9)\n",
    "\tmodel.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\treturn model\n",
    "\n",
    "# evaluate a model using k-fold cross-validation\n",
    "def evaluate_model(dataX, dataY, n_folds=5):\n",
    "\tscores, histories = list(), list()\n",
    "\t# prepare cross validation\n",
    "\tkfold = KFold(n_folds, shuffle=True, random_state=1)\n",
    "\t# enumerate splits\n",
    "\tfor train_ix, test_ix in kfold.split(dataX):\n",
    "\t\t# define model\n",
    "\t\tmodel = define_model()\n",
    "\t\t# select rows for train and test\n",
    "\t\ttrainX, trainY, testX, testY = dataX[train_ix], dataY[train_ix], dataX[test_ix], dataY[test_ix]\n",
    "\t\t# fit model\n",
    "\t\thistory = model.fit(trainX, trainY, epochs=10, batch_size=32, validation_data=(testX, testY), verbose=0)\n",
    "\t\t# evaluate model\n",
    "\t\t_, acc = model.evaluate(testX, testY, verbose=0)\n",
    "\t\tprint('> %.3f' % (acc * 100.0))\n",
    "\t\t# stores scores\n",
    "\t\tscores.append(acc)\n",
    "\t\thistories.append(history)\n",
    "\treturn scores, histories\n",
    "\n",
    "# plot diagnostic learning curves\n",
    "def summarize_diagnostics(histories):\n",
    "\tfor i in range(len(histories)):\n",
    "\t\t# plot loss\n",
    "\t\tplt.subplot(2, 1, 1)\n",
    "\t\tplt.title('Cross Entropy Loss')\n",
    "\t\tplt.plot(histories[i].history['loss'], color='blue', label='train')\n",
    "\t\tplt.plot(histories[i].history['val_loss'], color='orange', label='test')\n",
    "\t\t# plot accuracy\n",
    "\t\tplt.subplot(2, 1, 2)\n",
    "\t\tplt.title('Classification Accuracy')\n",
    "\t\tplt.plot(histories[i].history['accuracy'], color='blue', label='train')\n",
    "\t\tplt.plot(histories[i].history['val_accuracy'], color='orange', label='test')\n",
    "\t\tplt.show()\n",
    "\n",
    "\n",
    "# summarize model performance\n",
    "def summarize_performance(scores):\n",
    " # print summary\n",
    " print('Accuracy: mean=%.3f std=%.3f, n=%d' % (mean(scores)*100, std(scores)*100, len(scores)))\n",
    " # box and whisker plots of results\n",
    " plt.boxplot(scores)\n",
    " plt.show()\n",
    "\n",
    "def run_test_harness():\n",
    " # load dataset\n",
    " trainX, trainY, testX, testY = load_dataset()\n",
    " # prepare pixel data\n",
    " trainX, testX = prep_pixels(trainX, testX)\n",
    " # evaluate model\n",
    " scores, histories = evaluate_model(trainX, trainY)\n",
    " # learning curves\n",
    " summarize_diagnostics(histories)\n",
    " # summarize estimated performance\n",
    " summarize_performance(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "(trainX, trainY), (testX, testY) = mnist.load_data()\n",
    "# reshape dataset to have a single channel\n",
    "trainX = trainX.reshape((trainX.shape[0], 28, 28, 1))\n",
    "testX = testX.reshape((testX.shape[0], 28, 28, 1))\n",
    "# one hot encode target values\n",
    "trainY = to_categorical(trainY)\n",
    "testY = to_categorical(testY)\n",
    "\n",
    "trainX, testX = prep_pixels(trainX, testX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = plt.imshow(trainX[0][:,:,0], cmap=plt.get_cmap('gray'))\n",
    "plt.grid(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', \n",
    "                 activation ='relu', input_shape = (28,28,1)))\n",
    "model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', \n",
    "                 activation ='relu'))\n",
    "model.add(MaxPool2D(pool_size=(2,2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "\n",
    "model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', \n",
    "                 activation ='relu'))\n",
    "model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', \n",
    "                 activation ='relu'))\n",
    "model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(256, activation = \"relu\"))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(10, activation = \"softmax\"))\n",
    "\n",
    "optimizer = tf.keras.optimizers.legacy.RMSprop(learning_rate=0.001, rho=0.9, epsilon=1e-08, decay=0.0)\n",
    "\n",
    "model.compile(optimizer = optimizer , loss = \"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
    "\n",
    "learning_rate_reduction = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_accuracy', \n",
    "                                            patience=3, \n",
    "                                            verbose=1, \n",
    "                                            factor=0.5, \n",
    "                                            min_lr=0.00001)\n",
    "epochs = 30 # Turn epochs to 30 to get 0.9967 accuracy\n",
    "batch_size = 86\n",
    "\n",
    "datagen = tf.keras.preprocessing.image.ImageDataGenerator(\n",
    "        featurewise_center=False,  # set input mean to 0 over the dataset\n",
    "        samplewise_center=False,  # set each sample mean to 0\n",
    "        featurewise_std_normalization=False,  # divide inputs by std of the dataset\n",
    "        samplewise_std_normalization=False,  # divide each input by its std\n",
    "        zca_whitening=False,  # apply ZCA whitening\n",
    "        rotation_range=10,  # randomly rotate images in the range (degrees, 0 to 180)\n",
    "        zoom_range = 0.1, # Randomly zoom image \n",
    "        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)\n",
    "        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)\n",
    "        horizontal_flip=False,  # randomly flip images\n",
    "        vertical_flip=False # randomly flip images\n",
    ")  \n",
    "\n",
    "\n",
    "datagen.fit(trainX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(model=model, show_shapes=True, show_layer_activations=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_10\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_23 (Conv2D)          (None, 28, 28, 32)        832       \n",
      "                                                                 \n",
      " conv2d_24 (Conv2D)          (None, 28, 28, 32)        25632     \n",
      "                                                                 \n",
      " max_pooling2d_13 (MaxPooli  (None, 14, 14, 32)        0         \n",
      " ng2D)                                                           \n",
      "                                                                 \n",
      " dropout_12 (Dropout)        (None, 14, 14, 32)        0         \n",
      "                                                                 \n",
      " conv2d_25 (Conv2D)          (None, 14, 14, 64)        18496     \n",
      "                                                                 \n",
      " conv2d_26 (Conv2D)          (None, 14, 14, 64)        36928     \n",
      "                                                                 \n",
      " max_pooling2d_14 (MaxPooli  (None, 7, 7, 64)          0         \n",
      " ng2D)                                                           \n",
      "                                                                 \n",
      " dropout_13 (Dropout)        (None, 7, 7, 64)          0         \n",
      "                                                                 \n",
      " flatten_9 (Flatten)         (None, 3136)              0         \n",
      "                                                                 \n",
      " dense_18 (Dense)            (None, 256)               803072    \n",
      "                                                                 \n",
      " dropout_14 (Dropout)        (None, 256)               0         \n",
      "                                                                 \n",
      " dense_19 (Dense)            (None, 10)                2570      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 887530 (3.39 MB)\n",
      "Trainable params: 887530 (3.39 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2600f6c7e50>]"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['lr'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "697/697 - 121s - loss: 0.3141 - accuracy: 0.9014 - val_loss: 0.0345 - val_accuracy: 0.9886 - lr: 0.0010 - 121s/epoch - 173ms/step\n",
      "Epoch 2/30\n",
      "697/697 - 111s - loss: 0.0999 - accuracy: 0.9705 - val_loss: 0.0300 - val_accuracy: 0.9895 - lr: 0.0010 - 111s/epoch - 160ms/step\n",
      "Epoch 3/30\n",
      "697/697 - 104s - loss: 0.0787 - accuracy: 0.9768 - val_loss: 0.0225 - val_accuracy: 0.9926 - lr: 0.0010 - 104s/epoch - 149ms/step\n",
      "Epoch 4/30\n",
      "697/697 - 105s - loss: 0.0678 - accuracy: 0.9801 - val_loss: 0.0162 - val_accuracy: 0.9942 - lr: 0.0010 - 105s/epoch - 151ms/step\n",
      "Epoch 5/30\n",
      "697/697 - 104s - loss: 0.0641 - accuracy: 0.9820 - val_loss: 0.0159 - val_accuracy: 0.9946 - lr: 0.0010 - 104s/epoch - 149ms/step\n",
      "Epoch 6/30\n",
      "697/697 - 104s - loss: 0.0645 - accuracy: 0.9812 - val_loss: 0.0181 - val_accuracy: 0.9942 - lr: 0.0010 - 104s/epoch - 149ms/step\n",
      "Epoch 7/30\n",
      "697/697 - 118s - loss: 0.0652 - accuracy: 0.9819 - val_loss: 0.0161 - val_accuracy: 0.9952 - lr: 0.0010 - 118s/epoch - 169ms/step\n",
      "Epoch 8/30\n",
      "697/697 - 113s - loss: 0.0692 - accuracy: 0.9810 - val_loss: 0.0213 - val_accuracy: 0.9941 - lr: 0.0010 - 113s/epoch - 162ms/step\n",
      "Epoch 9/30\n",
      "697/697 - 104s - loss: 0.0656 - accuracy: 0.9824 - val_loss: 0.0206 - val_accuracy: 0.9948 - lr: 0.0010 - 104s/epoch - 149ms/step\n",
      "Epoch 10/30\n",
      "\n",
      "Epoch 10: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "697/697 - 102s - loss: 0.0696 - accuracy: 0.9820 - val_loss: 0.0249 - val_accuracy: 0.9930 - lr: 0.0010 - 102s/epoch - 147ms/step\n",
      "Epoch 11/30\n",
      "697/697 - 103s - loss: 0.0540 - accuracy: 0.9856 - val_loss: 0.0217 - val_accuracy: 0.9938 - lr: 5.0000e-04 - 103s/epoch - 147ms/step\n",
      "Epoch 12/30\n",
      "697/697 - 104s - loss: 0.0520 - accuracy: 0.9856 - val_loss: 0.0207 - val_accuracy: 0.9943 - lr: 5.0000e-04 - 104s/epoch - 149ms/step\n",
      "Epoch 13/30\n",
      "\n",
      "Epoch 13: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "697/697 - 103s - loss: 0.0533 - accuracy: 0.9866 - val_loss: 0.0241 - val_accuracy: 0.9937 - lr: 5.0000e-04 - 103s/epoch - 148ms/step\n",
      "Epoch 14/30\n",
      "697/697 - 104s - loss: 0.0431 - accuracy: 0.9880 - val_loss: 0.0185 - val_accuracy: 0.9946 - lr: 2.5000e-04 - 104s/epoch - 149ms/step\n",
      "Epoch 15/30\n",
      "697/697 - 103s - loss: 0.0429 - accuracy: 0.9883 - val_loss: 0.0166 - val_accuracy: 0.9948 - lr: 2.5000e-04 - 103s/epoch - 147ms/step\n",
      "Epoch 16/30\n",
      "697/697 - 102s - loss: 0.0429 - accuracy: 0.9888 - val_loss: 0.0164 - val_accuracy: 0.9957 - lr: 2.5000e-04 - 102s/epoch - 147ms/step\n",
      "Epoch 17/30\n",
      "697/697 - 105s - loss: 0.0424 - accuracy: 0.9882 - val_loss: 0.0196 - val_accuracy: 0.9940 - lr: 2.5000e-04 - 105s/epoch - 151ms/step\n",
      "Epoch 18/30\n",
      "697/697 - 103s - loss: 0.0420 - accuracy: 0.9891 - val_loss: 0.0155 - val_accuracy: 0.9959 - lr: 2.5000e-04 - 103s/epoch - 147ms/step\n",
      "Epoch 19/30\n",
      "697/697 - 114s - loss: 0.0444 - accuracy: 0.9879 - val_loss: 0.0184 - val_accuracy: 0.9950 - lr: 2.5000e-04 - 114s/epoch - 164ms/step\n",
      "Epoch 20/30\n",
      "697/697 - 135s - loss: 0.0438 - accuracy: 0.9883 - val_loss: 0.0152 - val_accuracy: 0.9961 - lr: 2.5000e-04 - 135s/epoch - 193ms/step\n",
      "Epoch 21/30\n",
      "697/697 - 131s - loss: 0.0441 - accuracy: 0.9881 - val_loss: 0.0187 - val_accuracy: 0.9959 - lr: 2.5000e-04 - 131s/epoch - 188ms/step\n",
      "Epoch 22/30\n",
      "697/697 - 115s - loss: 0.0437 - accuracy: 0.9881 - val_loss: 0.0187 - val_accuracy: 0.9957 - lr: 2.5000e-04 - 115s/epoch - 166ms/step\n",
      "Epoch 23/30\n",
      "\n",
      "Epoch 23: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
      "697/697 - 108s - loss: 0.0449 - accuracy: 0.9886 - val_loss: 0.0205 - val_accuracy: 0.9952 - lr: 2.5000e-04 - 108s/epoch - 155ms/step\n",
      "Epoch 24/30\n",
      "697/697 - 122s - loss: 0.0374 - accuracy: 0.9900 - val_loss: 0.0204 - val_accuracy: 0.9952 - lr: 1.2500e-04 - 122s/epoch - 175ms/step\n",
      "Epoch 25/30\n",
      "697/697 - 118s - loss: 0.0380 - accuracy: 0.9892 - val_loss: 0.0183 - val_accuracy: 0.9953 - lr: 1.2500e-04 - 118s/epoch - 169ms/step\n",
      "Epoch 26/30\n",
      "\n",
      "Epoch 26: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
      "697/697 - 133s - loss: 0.0380 - accuracy: 0.9896 - val_loss: 0.0163 - val_accuracy: 0.9955 - lr: 1.2500e-04 - 133s/epoch - 190ms/step\n",
      "Epoch 27/30\n",
      "697/697 - 115s - loss: 0.0350 - accuracy: 0.9903 - val_loss: 0.0157 - val_accuracy: 0.9956 - lr: 6.2500e-05 - 115s/epoch - 165ms/step\n",
      "Epoch 28/30\n",
      "697/697 - 111s - loss: 0.0355 - accuracy: 0.9902 - val_loss: 0.0172 - val_accuracy: 0.9957 - lr: 6.2500e-05 - 111s/epoch - 159ms/step\n",
      "Epoch 29/30\n",
      "\n",
      "Epoch 29: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05.\n",
      "697/697 - 112s - loss: 0.0362 - accuracy: 0.9898 - val_loss: 0.0180 - val_accuracy: 0.9961 - lr: 6.2500e-05 - 112s/epoch - 161ms/step\n",
      "Epoch 30/30\n",
      "697/697 - 113s - loss: 0.0345 - accuracy: 0.9905 - val_loss: 0.0160 - val_accuracy: 0.9962 - lr: 3.1250e-05 - 113s/epoch - 162ms/step\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(datagen.flow(trainX,trainY, batch_size=batch_size),\n",
    "                              epochs = epochs, validation_data = (testX,testY),\n",
    "                              verbose = 2, steps_per_epoch=trainX.shape[0] // batch_size\n",
    "                              , callbacks=[learning_rate_reduction])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "    plt.grid(visible=False)\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 29s 15ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y_pred = model.predict(trainX)\n",
    "# Convert predictions classes to one hot vectors \n",
    "Y_pred_classes = np.argmax(Y_pred,axis = 1) \n",
    "# Convert validation observations to one hot vectors\n",
    "Y_true = np.argmax(trainY,axis = 1) \n",
    "# compute the confusion matrix\n",
    "confusion_mtx = confusion_matrix(Y_true, Y_pred_classes) \n",
    "# plot the confusion matrix\n",
    "plot_confusion_matrix(confusion_mtx, classes = range(10), cmap='winter_r')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use(\"cyberpunk\")\n",
    "\n",
    "fig, ax = plt.subplots(2,1)\n",
    "ax[0].plot(history.history['loss'], color='C0', label=\"Training loss\")\n",
    "ax[0].plot(history.history['val_loss'], color='C3', label=\"validation loss\",axes =ax[0])\n",
    "legend = ax[0].legend(loc='best', shadow=True)\n",
    "\n",
    "ax[1].plot(history.history['accuracy'], color='C0', label=\"Training accuracy\")\n",
    "ax[1].plot(history.history['val_accuracy'], color='C3',label=\"Validation accuracy\")\n",
    "legend = ax[1].legend(loc='best', shadow=True)\n",
    "\n",
    "mplcyberpunk.make_lines_glow(ax[0], alpha_line=0.3)\n",
    "mplcyberpunk.make_lines_glow(ax[1], alpha_line=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('modeloteste1.keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 4s 14ms/step - loss: 0.0160 - accuracy: 0.9962\n"
     ]
    }
   ],
   "source": [
    "_, acc = model.evaluate(testX, testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_image(filename):\n",
    "    # load the image\n",
    "    img = load_img(filename, color_mode=\"grayscale\", target_size=(28, 28))\n",
    "    # convert to array\n",
    "    img = img_to_array(img)\n",
    "    # reshape into a single sample with 1 channel\n",
    "    img = img.reshape(1, 28, 28, 1)\n",
    "    # prepare pixel data\n",
    "    img = img.astype('float32')\n",
    "    img = img / 255.0\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    }
   ],
   "source": [
    "img = load_image('sample_image.png')\n",
    "\n",
    "predict_value = model.predict(img, verbose=0)\n",
    "digit = np.argmax(predict_value)\n",
    "print(digit)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}