我们从Python开源项目中，提取了以下6个代码示例，用于说明如何使用keras.layers.convolutional.Cropping2D()。
def get_unet0(num_start_filters=32): inputs = Input((img_rows, img_cols, num_channels)) conv1 = ConvBN2(inputs, num_start_filters) pool1 = MaxPooling2D(pool_size=(2, 2))(conv1) conv2 = ConvBN2(pool1, 2 * num_start_filters) pool2 = MaxPooling2D(pool_size=(2, 2))(conv2) conv3 = ConvBN2(pool2, 4 * num_start_filters) pool3 = MaxPooling2D(pool_size=(2, 2))(conv3) conv4 = ConvBN2(pool3, 8 * num_start_filters) pool4 = MaxPooling2D(pool_size=(2, 2))(conv4) conv5 = ConvBN2(pool4, 16 * num_start_filters) up6 = concatenate([UpSampling2D(size=(2, 2))(conv5), conv4]) conv6 = ConvBN2(up6, 8 * num_start_filters) up7 = concatenate([UpSampling2D(size=(2, 2))(conv6), conv3]) conv7 = ConvBN2(up7, 4 * num_start_filters) up8 = concatenate([UpSampling2D(size=(2, 2))(conv7), conv2]) conv8 = ConvBN2(up8, 2 * num_start_filters) up9 = concatenate([UpSampling2D(size=(2, 2))(conv8), conv1]) conv9 = Conv2D(num_start_filters, (3, 3), padding="same", kernel_initializer="he_uniform")(up9) conv9 = BatchNormalization()(conv9) conv9 = Activation('selu')(conv9) conv9 = Conv2D(num_start_filters, (3, 3), padding="same", kernel_initializer="he_uniform")(conv9) crop9 = Cropping2D(cropping=((16, 16), (16, 16)))(conv9) conv9 = BatchNormalization()(crop9) conv9 = Activation('selu')(conv9) conv10 = Conv2D(num_mask_channels, (1, 1))(conv9) model = Model(inputs=inputs, outputs=conv10) return model
def test_cropping_2d(): nb_samples = 2 stack_size = 2 input_len_dim1 = 8 input_len_dim2 = 8 cropping = ((2, 2), (3, 3)) dim_ordering = K.image_dim_ordering() if dim_ordering == 'th': input = np.random.rand(nb_samples, stack_size, input_len_dim1, input_len_dim2) else: input = np.random.rand(nb_samples, input_len_dim1, input_len_dim2, stack_size) # basic test layer_test(convolutional.Cropping2D, kwargs={'cropping': cropping, 'dim_ordering': dim_ordering}, input_shape=input.shape) # correctness test layer = convolutional.Cropping2D(cropping=cropping, dim_ordering=dim_ordering) layer.build(input.shape) output = layer(K.variable(input)) np_output = K.eval(output) # compare with numpy if dim_ordering == 'th': expected_out = input[:, :, cropping[0][0]: -cropping[0][1], cropping[1][0]: -cropping[1][1]] else: expected_out = input[:, cropping[0][0]: -cropping[0][1], cropping[1][0]: -cropping[1][1], :] assert_allclose(np_output, expected_out)
def test_cropping_2d(): nb_samples = 2 stack_size = 2 input_len_dim1 = 8 input_len_dim2 = 8 cropping = ((2, 2), (3, 3)) dim_ordering = K.image_dim_ordering() if dim_ordering == 'th': input = np.random.rand(nb_samples, stack_size, input_len_dim1, input_len_dim2) else: input = np.random.rand(nb_samples, input_len_dim1, input_len_dim2, stack_size) # basic test layer_test(convolutional.Cropping2D, kwargs={'cropping': cropping, 'dim_ordering': dim_ordering}, input_shape=input.shape) # correctness test layer = convolutional.Cropping2D(cropping=cropping, dim_ordering=dim_ordering) layer.build(input.shape) output = layer(K.variable(input)) np_output = K.eval(output) # compare with numpy if dim_ordering == 'th': expected_out = input[:, :, cropping[0][0]: -cropping[0][1], cropping[1][0]: -cropping[1][1]] else: expected_out = input[:, cropping[0][0]: -cropping[0][1], cropping[1][0]: -cropping[1][1], :] assert_allclose(np_output, expected_out) # another correctness test (no cropping) cropping = ((0, 0), (0, 0)) layer = convolutional.Cropping2D(cropping=cropping, dim_ordering=dim_ordering) layer.build(input.shape) output = layer(K.variable(input)) np_output = K.eval(output) # compare with input assert_allclose(np_output, input)
def CarND(input_shape, crop_shape): model = Sequential() # Crop # model.add(Cropping2D(((80,20),(1,1)), input_shape=input_shape, name="Crop")) model.add(Cropping2D(crop_shape, input_shape=input_shape, name="Crop")) # Resize model.add(AveragePooling2D(pool_size=(1,4), name="Resize", trainable=False)) # Normalize input. model.add(BatchNormalization(axis=1, name="Normalize")) # Reduce dimensions through trainable convolution, activation, and # pooling layers. model.add(Convolution2D(24, 3, 3, subsample=(2,2), name="Convolution2D1", activation="relu")) model.add(MaxPooling2D(name="MaxPool1")) model.add(Convolution2D(36, 3, 3, subsample=(1,1), name="Convolution2D2", activation="relu")) model.add(MaxPooling2D(name="MaxPool2")) model.add(Convolution2D(48, 3, 3, subsample=(1,1), name="Convolution2D3", activation="relu")) model.add(MaxPooling2D(name="MaxPool3")) # Dropout for regularization model.add(Dropout(0.1, name="Dropout")) # Flatten input in a non-trainable layer before feeding into # fully-connected layers. model.add(Flatten(name="Flatten")) # Model steering through trainable layers comprising dense units # as ell as dropout units for regularization. model.add(Dense(100, activation="relu", name="FC2")) model.add(Dense(50, activation="relu", name="FC3")) model.add(Dense(10, activation="relu", name="FC4")) # Generate output (steering angles) with a single non-trainable # node. model.add(Dense(1, name="Readout", trainable=False)) return model # #+RESULTS: # Here is a summary of the actual model, as generated directly by # =model.summary= in Keras.
