我是Keras的新手，我遇到了一个问题，给定一个图像，我必须制作一个卷积神经网络，根据它输出另一个图像。
现在我在互联网上看到的所有例子都包含分类问题，其中每个图像都有一个带有一个热编码的标签。我想把图像作为标签。
答案 0 :(得分：0)
一系列渐进卷积之后可以进行一系列调整大小的插值，例如：如在以下层中实现的那样：
class Interpolation(Layer):
def __init__(self, output_dim, num_channels, **kwargs):
self.num_channels = num_channels
self.output_dim = output_dim
super(Interpolation, self).__init__(**kwargs)
def build(self, input_shape):
super(Interpolation, self).build(input_shape)
def call(self, x):
return K.tf.image.resize_bilinear(x, self.output_dim)
def compute_output_shape(self, input_shape):
return input_shape[0], input_shape[1] *2 , input_shape[2]* 2, self.num_channels
然后，您可以应用一系列转换，这些转换将导致输出形状与输入形状相匹配。下面是一个示例代码，展示了该层的使用：
# grayscale in
uncolored = Input(shape=(200,200,1,))
# first block 200x200x3
conv0 = Conv2D(3, (3,3), padding='same', activation='relu', data_format='channels_last', name='0', kernel_regularizer='l2')(uncolored)
bn0 = BatchNormalization()(conv0)
# second block 200x200x64
conv1 = Conv2D(64, (3,3), padding='same', activation='relu', data_format='channels_last', kernel_regularizer='l2')(conv0)
bn1 = BatchNormalization()(conv1) # 200x200x64
pool0 = MaxPooling2D(pool_size=2, padding='same')(conv0) # 100x100x64
# third block # 100x100x128
conv2 = Conv2D(128, (3,3), padding='same', activation='relu', data_format='channels_last', kernel_regularizer='l2')(pool0)
bn2 = BatchNormalization()(conv2) # 100 x 100 x 128
pool1 = MaxPooling2D(pool_size=2, padding='same')(conv2) # 50x50x128
# fourth block 50x50x256
conv3 = Conv2D(256, (3,3), padding='same', activation='relu', data_format='channels_last', name='2', kernel_regularizer='l2')(pool1)
bn3 = BatchNormalization()(conv3) # 50 x 50 x 256
pool2 = MaxPooling2D(pool_size=2, padding='same')(conv3) # 25 x 25 x 256
# fifth block 25 x 25 x 512
conv4 = Conv2D(512, (3,3), padding='same', activation='relu', data_format='channels_last', kernel_regularizer='l2')(pool2)
bn4 = BatchNormalization()(conv4)
rconv0 = Conv2D(256, (1,1), padding='same', activation='sigmoid', data_format='channels_last', kernel_regularizer='l2')(conv4)
# first upscale
interp_layer0 = Interpolation(output_dim=(50,50),
num_channels=256) (rconv0) #
# first addition
intermediate_0 = Add()([interp_layer0, bn3])
rconv1 = Conv2D(128, (3,3), padding='same',
activation='sigmoid', data_format='channels_last')(intermediate_0)
# second upscale
interp_layer1 = Interpolation(output_dim=(100,100),
num_channels=128)(rconv1)
# second addition
intermediate_1 = Add()([interp_layer1, bn2])
rconv2 = Conv2D(64, (3,3), padding='same',
activation='sigmoid', data_format='channels_last')(intermediate_1)
# third upscale
interp_layer2 = Interpolation(output_dim=(200,200),
num_channels= 64)(rconv2)
# third addition
intermediate_2 = Add()([interp_layer2, bn1 ])
rconv3 = Conv2D(3, (3,3), padding='same',
activation='sigmoid', data_format='channels_last')(intermediate_2)
# fourth addition
intermediate_3 = Add()([rconv3,bn0])
rconv4 = Conv2D(3, (3,3), padding='same', activation='sigmoid', data_format='channels_last')(intermediate_3)
model = Model(inputs=[uncolored], outputs=[rconv4])
模型摘要：
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_5 (InputLayer) (None, 200, 200, 1) 0
__________________________________________________________________________________________________
0 (Conv2D) (None, 200, 200, 3) 30 input_5[0][0]
__________________________________________________________________________________________________
max_pooling2d_13 (MaxPooling2D) (None, 100, 100, 3) 0 0[0][0]
__________________________________________________________________________________________________
conv2d_34 (Conv2D) (None, 100, 100, 128 3584 max_pooling2d_13[0][0]
__________________________________________________________________________________________________
max_pooling2d_14 (MaxPooling2D) (None, 50, 50, 128) 0 conv2d_34[0][0]
__________________________________________________________________________________________________
2 (Conv2D) (None, 50, 50, 256) 295168 max_pooling2d_14[0][0]
__________________________________________________________________________________________________
max_pooling2d_15 (MaxPooling2D) (None, 25, 25, 256) 0 2[0][0]
__________________________________________________________________________________________________
conv2d_35 (Conv2D) (None, 25, 25, 512) 1180160 max_pooling2d_15[0][0]
__________________________________________________________________________________________________
conv2d_36 (Conv2D) (None, 25, 25, 256) 131328 conv2d_35[0][0]
__________________________________________________________________________________________________
interpolation_13 (Interpolation (None, 50, 50, 256) 0 conv2d_36[0][0]
__________________________________________________________________________________________________
batch_normalization_24 (BatchNo (None, 50, 50, 256) 1024 2[0][0]
__________________________________________________________________________________________________
add_17 (Add) (None, 50, 50, 256) 0 interpolation_13[0][0]
batch_normalization_24[0][0]
__________________________________________________________________________________________________
conv2d_37 (Conv2D) (None, 50, 50, 128) 295040 add_17[0][0]
__________________________________________________________________________________________________
interpolation_14 (Interpolation (None, 100, 100, 128 0 conv2d_37[0][0]
__________________________________________________________________________________________________
batch_normalization_23 (BatchNo (None, 100, 100, 128 512 conv2d_34[0][0]
__________________________________________________________________________________________________
add_18 (Add) (None, 100, 100, 128 0 interpolation_14[0][0]
batch_normalization_23[0][0]
__________________________________________________________________________________________________
conv2d_38 (Conv2D) (None, 100, 100, 64) 73792 add_18[0][0]
__________________________________________________________________________________________________
conv2d_33 (Conv2D) (None, 200, 200, 64) 1792 0[0][0]
__________________________________________________________________________________________________
interpolation_15 (Interpolation (None, 200, 200, 64) 0 conv2d_38[0][0]
__________________________________________________________________________________________________
batch_normalization_22 (BatchNo (None, 200, 200, 64) 256 conv2d_33[0][0]
__________________________________________________________________________________________________
add_19 (Add) (None, 200, 200, 64) 0 interpolation_15[0][0]
batch_normalization_22[0][0]
__________________________________________________________________________________________________
conv2d_39 (Conv2D) (None, 200, 200, 3) 195 add_19[0][0]
__________________________________________________________________________________________________
batch_normalization_21 (BatchNo (None, 200, 200, 3) 12 0[0][0]
__________________________________________________________________________________________________
add_20 (Add) (None, 200, 200, 3) 0 conv2d_39[0][0]
batch_normalization_21[0][0]
__________________________________________________________________________________________________
conv2d_40 (Conv2D) (None, 200, 200, 1) 28 add_20[0][0]
==================================================================================================
Total params: 1,982,921
Trainable params: 1,982,019
Non-trainable params: 902
_____________________________
在该示例中，我们从单通道图像移动到多通道图像。对于任何大小/数量的通道的图像，可以复制相同的想法。当然，确切的网络架构取决于您所需的功能。