自学教程：Python layers.SpatialDropout2D方法代码示例

示例1: keras_dropout

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import SpatialDropout2D [as 别名]def keras_dropout(layer, rate):    """    Keras dropout layer.    """    from keras import layers    input_dim = len(layer.input.shape)    if input_dim == 2:        return layers.SpatialDropout1D(rate)    elif input_dim == 3:        return layers.SpatialDropout2D(rate)    elif input_dim == 4:        return layers.SpatialDropout3D(rate)    else:        return layers.Dropout(rate) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import SpatialDropout2D [as 别名]def test_dropout():    layer_test(layers.Dropout,               kwargs={'rate': 0.5},               input_shape=(3, 2))    layer_test(layers.Dropout,               kwargs={'rate': 0.5, 'noise_shape': [3, 1]},               input_shape=(3, 2))    layer_test(layers.Dropout,               kwargs={'rate': 0.5, 'noise_shape': [None, 1]},               input_shape=(3, 2))    layer_test(layers.SpatialDropout1D,               kwargs={'rate': 0.5},               input_shape=(2, 3, 4))    for data_format in ['channels_last', 'channels_first']:        for shape in [(4, 5), (4, 5, 6)]:            if data_format == 'channels_last':                input_shape = (2,) + shape + (3,)            else:                input_shape = (2, 3) + shape            layer_test(layers.SpatialDropout2D if len(shape) == 2 else layers.SpatialDropout3D,                       kwargs={'rate': 0.5,                               'data_format': data_format},                       input_shape=input_shape)            # Test invalid use cases            with pytest.raises(ValueError):                layer_test(layers.SpatialDropout2D if len(shape) == 2 else layers.SpatialDropout3D,                           kwargs={'rate': 0.5,                                   'data_format': 'channels_middle'},                           input_shape=input_shape) 

示例3: build_psp

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import SpatialDropout2D [as 别名]def build_psp(backbone,              psp_layer,              last_upsampling_factor,              classes=21,              activation='softmax',              conv_filters=512,              pooling_type='avg',              dropout=None,              final_interpolation='bilinear',              use_batchnorm=True):    input = backbone.input    x = extract_outputs(backbone, [psp_layer])[0]    x = PyramidPoolingModule(        conv_filters=conv_filters,        pooling_type=pooling_type,        use_batchnorm=use_batchnorm)(x)    x = Conv2DBlock(512, (1, 1), activation='relu', padding='same',                    use_batchnorm=use_batchnorm)(x)    if dropout is not None:        x = SpatialDropout2D(dropout)(x)    x = Conv2D(classes, (3,3), padding='same', name='final_conv')(x)    if final_interpolation == 'bilinear':        x = ResizeImage(to_tuple(last_upsampling_factor))(x)    elif final_interpolation == 'duc':        x = DUC(to_tuple(last_upsampling_factor))(x)    else:        raise ValueError('Unsupported interpolation type {}. '.format(final_interpolation) +                         'Use `duc` or `bilinear`.')    x = Activation(activation, name=activation)(x)    model = Model(input, x)    return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import SpatialDropout2D [as 别名]def decoder_a(self):        """ Decoder for side A """        kwargs = dict(kernel_size=5, kernel_initializer=self.kernel_initializer)        decoder_complexity = 320 if self.lowmem else self.config["complexity_decoder_a"]        dense_dim = 384 if self.lowmem else 512        decoder_shape = self.input_shape[0] // 16        input_ = Input(shape=(decoder_shape, decoder_shape, dense_dim))        var_x = input_        var_x = self.blocks.upscale(var_x, decoder_complexity, **kwargs)        var_x = SpatialDropout2D(0.25)(var_x)        var_x = self.blocks.upscale(var_x, decoder_complexity, **kwargs)        if self.lowmem:            var_x = SpatialDropout2D(0.15)(var_x)        else:            var_x = SpatialDropout2D(0.25)(var_x)        var_x = self.blocks.upscale(var_x, decoder_complexity // 2, **kwargs)        var_x = self.blocks.upscale(var_x, decoder_complexity // 4, **kwargs)        var_x = self.blocks.conv2d(var_x, 3,                                   kernel_size=5,                                   padding="same",                                   activation="sigmoid",                                   name="face_out")        outputs = [var_x]        if self.config.get("learn_mask", False):            var_y = input_            var_y = self.blocks.upscale(var_y, decoder_complexity)            var_y = self.blocks.upscale(var_y, decoder_complexity)            var_y = self.blocks.upscale(var_y, decoder_complexity // 2)            var_y = self.blocks.upscale(var_y, decoder_complexity // 4)            var_y = self.blocks.conv2d(var_y, 1,                                       kernel_size=5,                                       padding="same",                                       activation="sigmoid",                                       name="mask_out")            outputs.append(var_y)        return KerasModel(input_, outputs=outputs) 

示例5: build_shallow_weight

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import SpatialDropout2D [as 别名]def build_shallow_weight(channels, width, height, output_size, nb_classes):	# input	inputs = Input(shape=(channels, height, width))	# 1 conv	conv1_1 = Convolution2D(8, 3, 3, border_mode='same', activation='relu', 		W_regularizer=l2(0.01))(inputs)	bn1 = BatchNormalization(mode=0, axis=1)(conv1_1)	pool1 = MaxPooling2D(pool_size=(2,2), strides=(2,2))(bn1)	gn1 = GaussianNoise(0.5)(pool1)	drop1 = SpatialDropout2D(0.5)(gn1)	# 2 conv	conv2_1 = Convolution2D(8, 3, 3, border_mode='same', activation='relu',		W_regularizer=l2(0.01))(gn1)	bn2 = BatchNormalization(mode=0, axis=1)(conv2_1)	pool2 = MaxPooling2D(pool_size=(2,2), strides=(2,2))(bn2)	gn2 = GaussianNoise(0.5)(pool2)	drop2 = SpatialDropout2D(0.5)(gn2)	# 3 conv	conv3_1 = Convolution2D(8, 3, 3, border_mode='same', activation='relu',		W_regularizer=l2(0.01))(drop2)	bn3 = BatchNormalization(mode=0, axis=1)(conv3_1)	pool3 = MaxPooling2D(pool_size=(2,2), strides=(2,2))(bn3)	gn3 = GaussianNoise(0.5)(pool3)	drop3 = SpatialDropout2D(0.5)(gn3)	# 4 conv	conv4_1 = Convolution2D(8, 3, 3, border_mode='same', activation='relu',		W_regularizer=l2(0.01))(gn3)	bn4 = BatchNormalization(mode=0, axis=1)(conv4_1)	pool4 = MaxPooling2D(pool_size=(2,2), strides=(2,2))(bn4)	gn4 = GaussianNoise(0.5)(pool4)	drop4 = SpatialDropout2D(0.5)(gn4)	# flaten	flat = Flatten()(gn4)	# 1 dense	dense1 = Dense(8, activation='relu', W_regularizer=l2(0.1))(flat)	bn6 = BatchNormalization(mode=0, axis=1)(dense1)	drop6 = Dropout(0.5)(bn6)	# output	out = []	for i in range(output_size):		out.append(Dense(nb_classes, activation='softmax')(bn6))	if output_size > 1:		merged_out = merge(out, mode='concat')		shaped_out = Reshape((output_size, nb_classes))(merged_out)		sample_weight_mode = 'temporal'	else:		shaped_out = out		sample_weight_mode = None	model = Model(input=[inputs], output=shaped_out)	model.summary()	model.compile(loss='categorical_crossentropy',				  optimizer='adam',				  metrics=[categorical_accuracy_per_sequence],				  sample_weight_mode = sample_weight_mode				  )	return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import SpatialDropout2D [as 别名]def unet_model1():    inputs = Input((1, 512, 512))    conv1 = Convolution2D(width, 3, 3, activation='relu', border_mode='same')(inputs)    conv1 = BatchNormalization(axis = 1)(conv1)    conv1 = Convolution2D(width, 3, 3, activation='relu', border_mode='same')(conv1)    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)    conv2 = Convolution2D(width*2, 3, 3, activation='relu', border_mode='same')(pool1)    conv2 = BatchNormalization(axis = 1)(conv2)    conv2 = Convolution2D(width*2, 3, 3, activation='relu', border_mode='same')(conv2)    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)    conv3 = Convolution2D(width*4, 3, 3, activation='relu', border_mode='same')(pool2)    conv3 = BatchNormalization(axis = 1)(conv3)    conv3 = Convolution2D(width*4, 3, 3, activation='relu', border_mode='same')(conv3)    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)    conv4 = Convolution2D(width*8, 3, 3, activation='relu', border_mode='same')(pool3)    conv4 = BatchNormalization(axis = 1)(conv4)    conv4 = Convolution2D(width*8, 3, 3, activation='relu', border_mode='same')(conv4)    pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)    conv5 = Convolution2D(width*16, 3, 3, activation='relu', border_mode='same')(pool4)    conv5 = BatchNormalization(axis = 1)(conv5)    conv5 = Convolution2D(width*16, 3, 3, activation='relu', border_mode='same')(conv5)    up6 = merge([UpSampling2D(size=(2, 2))(conv5), conv4], mode='concat', concat_axis=1)    conv6 = SpatialDropout2D(dropout_rate)(up6)    conv6 = Convolution2D(width*8, 3, 3, activation='relu', border_mode='same')(conv6)    conv6 = Convolution2D(width*8, 3, 3, activation='relu', border_mode='same')(conv6)    up7 = merge([UpSampling2D(size=(2, 2))(conv6), conv3], mode='concat', concat_axis=1)    conv7 = SpatialDropout2D(dropout_rate)(up7)    conv7 = Convolution2D(width*4, 3, 3, activation='relu', border_mode='same')(conv7)    conv7 = Convolution2D(width*4, 3, 3, activation='relu', border_mode='same')(conv7)    up8 = merge([UpSampling2D(size=(2, 2))(conv7), conv2], mode='concat', concat_axis=1)    conv8 = SpatialDropout2D(dropout_rate)(up8)    conv8 = Convolution2D(width*2, 3, 3, activation='relu', border_mode='same')(conv8)    conv8 = Convolution2D(width*2, 3, 3, activation='relu', border_mode='same')(conv8)    up9 = merge([UpSampling2D(size=(2, 2))(conv8), conv1], mode='concat', concat_axis=1)    conv9 = SpatialDropout2D(dropout_rate)(up9)    conv9 = Convolution2D(width, 3, 3, activation='relu', border_mode='same')(conv9)    conv9 = Convolution2D(width, 3, 3, activation='relu', border_mode='same')(conv9)    conv10 = Convolution2D(1, 1, 1, activation='sigmoid')(conv9)    model = Model(input=inputs, output=conv10)    model.compile(optimizer=Adam(lr=1e-5), loss=dice_coef_loss, metrics=[dice_coef])    return model 

注：本文中的keras.layers.SpatialDropout2D方法
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