我们从Python开源项目中，提取了以下5个代码示例，用于说明如何使用keras.layers.convolutional.ZeroPadding3D()。
def seq3DCNN(n_flow=4, seq_len=3, map_height=32, map_width=32): model=Sequential() # model.add(ZeroPadding3D(padding=(0, 1, 1), input_shape=(n_flow, seq_len, map_height, map_width))) # model.add(Convolution3D(64, 2, 3, 3, border_mode='valid')) model.add(Convolution3D(64, 2, 3, 3, border_mode='same', input_shape=(n_flow, seq_len, map_height, map_width))) model.add(Activation('relu')) model.add(Convolution3D(128, 2, 3, 3, border_mode='same')) model.add(Activation('relu')) model.add(Convolution3D(64, 2, 3, 3, border_mode='same')) model.add(Activation('relu')) model.add(ZeroPadding3D(padding=(0, 1, 1))) model.add(Convolution3D(n_flow, seq_len, 3, 3, border_mode='valid')) # model.add(Convolution3D(n_flow, seq_len-2, 3, 3, border_mode='same')) model.add(Activation('tanh')) return model
def test_zero_padding_3d(): nb_samples = 2 stack_size = 2 input_len_dim1 = 4 input_len_dim2 = 5 input_len_dim3 = 3 input = np.ones((nb_samples, input_len_dim1, input_len_dim2, input_len_dim3, stack_size)) # basic test layer_test(convolutional.ZeroPadding3D, kwargs={'padding': (2, 2, 2)}, input_shape=input.shape) # correctness test layer = convolutional.ZeroPadding3D(padding=(2, 2, 2)) layer.build(input.shape) output = layer(K.variable(input)) np_output = K.eval(output) for offset in [0, 1, -1, -2]: assert_allclose(np_output[:, offset, :, :, :], 0.) assert_allclose(np_output[:, :, offset, :, :], 0.) assert_allclose(np_output[:, :, :, offset, :], 0.) assert_allclose(np_output[:, 2:-2, 2:-2, 2:-2, :], 1.) layer.get_config()
def get_model(summary=False, backend='tf'): """ Return the Keras model of the network """ model = Sequential() if backend == 'tf': input_shape=(16, 112, 112, 3) # l, h, w, c else: input_shape=(3, 16, 112, 112) # c, l, h, w model.add(Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same', name='conv1', input_shape=input_shape)) model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), border_mode='valid', name='pool1')) # 2nd layer group model.add(Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same', name='conv2')) model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool2')) # 3rd layer group model.add(Convolution3D(256, 3, 3, 3, activation='relu', border_mode='same', name='conv3a')) model.add(Convolution3D(256, 3, 3, 3, activation='relu', border_mode='same', name='conv3b')) model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool3')) # 4th layer group model.add(Convolution3D(512, 3, 3, 3, activation='relu', border_mode='same', name='conv4a')) model.add(Convolution3D(512, 3, 3, 3, activation='relu', border_mode='same', name='conv4b')) model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool4')) # 5th layer group model.add(Convolution3D(512, 3, 3, 3, activation='relu', border_mode='same', name='conv5a')) model.add(Convolution3D(512, 3, 3, 3, activation='relu', border_mode='same', name='conv5b')) model.add(ZeroPadding3D(padding=((0, 0), (0, 1), (0, 1)), name='zeropad5')) model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool5')) model.add(Flatten()) # FC layers group model.add(Dense(4096, activation='relu', name='fc6')) model.add(Dropout(.5)) model.add(Dense(4096, activation='relu', name='fc7')) model.add(Dropout(.5)) model.add(Dense(487, activation='softmax', name='fc8')) if summary: print(model.summary()) return model
