# https://en.wikipedia.org/wiki/Convolutional_neural_network import os import numpy as np import matplotlib.pyplot as plt # keras from keras.datasets import mnist from keras.models import Sequential, load_model from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D from keras.utils import np_utils, plot_model # avoid libiomp5 error and less noise os.environ['KMP_DUPLICATE_LIB_OK'] = 'True' os.environ['CUDA_VISIBLE_DEVICES'] = '' # configuration variables # ----------------------------------------------------------------- batch_size = 128 n_classes = 10 epochs = 5 # load data (https://keras.io/datasets/) # ----------------------------------------------------------------- (X_train, Y_train), (X_test, Y_test) = mnist.load_data() # reshape and set type X_train = X_train.reshape(X_train.shape[0], 28, 28, 1) X_test = X_test.reshape(X_test.shape[0], 28, 28, 1) X_train = X_train.astype('float32') X_test = X_test.astype('float32') # normalize X_train = X_train / 255 X_test = X_test / 255 # one-hot encoding (https://en.wikipedia.org/wiki/One-hot) Y_train = np_utils.to_categorical(Y_train, n_classes) Y_test = np_utils.to_categorical(Y_test, n_classes) # print(Y_train.shape) # print(Y_test.shape) # neural network: sequential model (https://keras.io/models/sequential/) # ----------------------------------------------------------------- model = Sequential() # layers model.add(Conv2D(filters=32, kernel_size=(5, 5), activation='relu', input_shape=(28, 28, 1))) model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(n_classes, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(n_classes, activation='softmax')) # plot model # plot_model(model, to_file='plot.png', show_shapes=True) # training (https://keras.io/optimizers/) # ----------------------------------------------------------------- model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adadelta') # train model model.fit( X_train, Y_train, validation_data=(X_test, Y_test), epochs=epochs, batch_size=batch_size, verbose=2 ) # evaluate # ----------------------------------------------------------------- metrics = model.evaluate(X_test, Y_test, verbose=2) loss = metrics[0] accuracy = metrics[1] print(loss) print(accuracy) # 0.029002553918152263 # 0.9906