localization_cnn.py 文件源码

def train_cnn1(x_train, y_train, x_test, y_test, num_classes, input_shape):
    nb_filters = 32
    pool_size = (2, 2)
    kernel_size = (3, 3)

    model = Sequential()

    # model.add(Convolution2D(32, 3, 3, border_mode='same', input_shape=(64, 64, 1)))
    # model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1],
    #                         border_mode='valid'))
    #
    # model.add(Activation('relu'))
    # model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1]))
    # model.add(Activation('relu'))
    # model.add(MaxPooling2D(pool_size=pool_size))
    # model.add(Dropout(0.25))
    #
    # model.add(Flatten())
    # model.add(Dense(64))
    # model.add(Activation('relu'))
    # model.add(Dropout(0.5))
    # model.add(Dense(num_classes))
    # model.add(Activation('softmax'))

    model.add(Convolution2D(32, 3, 3, border_mode='same', input_shape=input_shape))

    model.add(Activation('relu'))
    model.add(Convolution2D(32, 3, 3))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    # model.add(Dropout(0.25))

    model.add(Convolution2D(32, 3, 3, border_mode='same'))
    model.add(Activation('relu'))
    model.add(Convolution2D(32, 3, 3))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    # model.add(Dropout(0.25))

    model.add(Flatten())
    model.add((Dense(512)))
    model.add(Activation('relu'))
    # model.add(Dropout(0.5))
    model.add(Dense(num_classes))
    model.add(Activation('softmax'))

    # model.summary()
    model.compile(loss='categorical_crossentropy', optimizer=Adamax(lr=0.01, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=1e-4), metrics=["accuracy"])
    model.fit(x_train, y_train,
              batch_size=24, nb_epoch=32, verbose=1,
              validation_data=(x_test, y_test))
    score = model.evaluate(x_test, y_test, verbose=1)
    return score[1]