Also referred to as a downsampling layer and with maxpooling being the most popular, this basically takes a filter (normally of size 2×2) and a stride of the same length. It then applies it to the input volume and outputs the maximum number in every subregion that the filter convolves around.
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import numpy as np from matplotlib import pyplot as plt from keras.utils import np_utils from keras.models import Sequential from keras.layers.core import Dense, Dropout, Flatten from keras.callbacks import EarlyStopping, ModelCheckpoint from keras.layers import Conv2D, MaxPooling2D from keras.optimizers import Adam from keras.callbacks import EarlyStopping from keras.datasets import mnist (X_train, y_train), (X_test, y_test) = mnist.load_data() img_rows, img_cols = X_train[0].shape[0], X_train[0].shape[1] X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1) X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1) input_shape = (img_rows, img_cols, 1) X_train = X_train.astype('float32')/255. X_test = X_test.astype('float32')/255. n_classes = len(set(y_train)) y_train = np_utils.to_categorical(y_train, n_classes) y_test = np_utils.to_categorical(y_test, n_classes) model = Sequential() model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', input_shape=input_shape)) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(128, kernel_size=(3, 3), activation='relu', padding='same')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(256, kernel_size=(3, 3), activation='relu', padding='same')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.5)) model.add(Flatten()) model.add(Dense(128, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(n_classes, activation='softmax')) model.compile(loss=['categorical_crossentropy'], optimizer='adam', metrics=['accuracy']) model.summary() callbacks = [EarlyStopping(monitor='val_acc', patience=5)] batch_size = 128 n_epochs = 200 model.fit(X_train, y_train, batch_size=batch_size, epochs=n_epochs, verbose=1, validation_split=0.2, callbacks=callbacks) score = model.evaluate(X_test, y_test, verbose=0) print('Test loss:', score[0]) print('Test accuracy:', score[1]) Using TensorFlow backend. |
