CNN_example.py
""" To know more or get code samples, please visit my website: https://morvanzhou.github.io/tutorials/ Or search: 莫烦Python Thank you for supporting! """ # please note, all tutorial code are running under python3.5. # If you use the version like python2.7, please modify the code accordingly # 6 - CNN example # to try tensorflow, un-comment following two lines # import os # os.environ['KERAS_BACKEND']='tensorflow' import numpy as np np.random.seed(1337) # for reproducibility from keras.datasets import mnist from keras.utils import np_utils from keras.models import Sequential from keras.layers import Dense, Activation, Convolution2D, MaxPooling2D, Flatten from keras.optimizers import Adam # download the mnist to the path '~/.keras/datasets/' if it is the first time to be called # training X shape (60000, 28x28), Y shape (60000, ). test X shape (10000, 28x28), Y shape (10000, ) (X_train, y_train), (X_test, y_test) = mnist.load_data() # data pre-processing X_train = X_train.reshape(-1, 1,28, 28)/255. X_test = X_test.reshape(-1, 1,28, 28)/255. y_train = np_utils.to_categorical(y_train, num_classes=10) y_test = np_utils.to_categorical(y_test, num_classes=10) # Another way to build your CNN model = Sequential() # Conv layer 1 output shape (32, 28, 28) model.add(Convolution2D( batch_input_shape=(None, 1, 28, 28), filters=32, kernel_size=5, strides=1, padding='same', # Padding method data_format='channels_first', )) model.add(Activation('relu')) # Pooling layer 1 (max pooling) output shape (32, 14, 14) model.add(MaxPooling2D( pool_size=2, strides=2, padding='same', # Padding method data_format='channels_first', )) # Conv layer 2 output shape (64, 14, 14) model.add(Convolution2D(64, 5, strides=1, padding='same', data_format='channels_first')) model.add(Activation('relu')) # Pooling layer 2 (max pooling) output shape (64, 7, 7) model.add(MaxPooling2D(2, 2, 'same', data_format='channels_first')) # Fully connected layer 1 input shape (64 * 7 * 7) = (3136), output shape (1024) model.add(Flatten()) model.add(Dense(1024)) model.add(Activation('relu')) # Fully connected layer 2 to shape (10) for 10 classes model.add(Dense(10)) model.add(Activation('softmax')) # Another way to define your optimizer adam = Adam(lr=1e-4) # We add metrics to get more results you want to see model.compile(optimizer=adam, loss='categorical_crossentropy', metrics=['accuracy']) print('Training ------------') # Another way to train the model model.fit(X_train, y_train, epochs=1, batch_size=64,) print('\nTesting ------------') # Evaluate the model with the metrics we defined earlier loss, accuracy = model.evaluate(X_test, y_test) print('\ntest loss: ', loss) print('\ntest accuracy: ', accuracy)