inputs = tf.keras.Input((height, width, 1)) x = inputs # Multi-Wiener deconvol utions x = WienerDeconvolution(initial_psf, initial_K)(x) skips = [] # Contracting path for c in encoding_cs: x, x_skip = encoder_block(x, c, kernel_size=3, padding='same', dilation_rate=1, pooling='average') skips.append(x_skip) skips = list(reversed(skips)) # Center x = conv2d_block(x, center_cs, kernel_size=3, padding='same') # Expansive path for i, c in enumerate(decoding_cs): if skip_connections[i]: x = decoder_block_resize(x, skips[i], c, kernel_size=3, padding='same', dilation_rate=1) else: x = decoder_block(x, None, c, kernel_size=3, padding='same', dilation_rate=1) # Classify x = layers.Conv2D(filters=1, kernel_size=1, use_bias=True, activation='relu')(x) outputs = tf.squeeze(x, axis=3) model = tf.keras.Model(inputs=[inputs], outputs=[outputs])

这段代码是一个基于深度学习的图像去模糊模型，使用了U-Net的结构。代码中包含了两个子模块：编码器和解码器。编码器通过多个卷积层将输入图像压缩为特征图，其中的池化操作可以缩小特征图的尺寸。在编码器的每一层中，都会保存一个跳跃连接，将该层的特征图保存下来，以便在解码器中恢复图像细节时使用。解码器则将特征图进行上采样，逐渐恢复图像细节，每一层都会将对应编码器层的跳跃连接特征图进行拼接，以恢复更多的细节。最后，使用一个卷积层将输出压缩为一维，得到最终的去模糊结果。

相关问题

inputs = tf.keras.layers.Input(shape=(128, 128, 1)) x = tf.keras.layers.UpSampling2D(size=(8, 8))(inputs)

The code above defines an input layer with a shape of (128, 128, 1) which means it expects inputs with a height and width of 128 and a single channel. Then, the layer applies an upsampling operation with a size of (8, 8) which means it will increase the height and width of the input by a factor of 8 in both dimensions. This will result in an output tensor with a shape of (1024, 1024, 1) which is 8 times larger than the input tensor.

修改下面代码使其具体生成器的卷积lstm更换为卷积RNN def build_generator(self): def res_net(inputs, filters): x = inputs net = conv2d(x, filters // 2, (1, 1), 1) net = conv2d(net, filters, (3, 3), 1) net = net + x # net=tf.keras.layers.LeakyReLU(0.2)(net) return net def conv2d(inputs, filters, kernel_size, strides): x = tf.keras.layers.Conv2D(filters, kernel_size, strides, 'same')(inputs) x = tf.keras.layers.BatchNormalization()(x) x = tf.keras.layers.LeakyReLU(alpha=0.2)(x) return x d0 = tf.keras.layers.Input(shape=(10, 128, 128, 3)) out= tf.keras.layers.ConvLSTM2D(filters=32, kernel_size=3,padding='same')(d0) out=tf.keras.layers.Conv2D(3,1,1,'same')(out) return keras.Model(inputs=d0, outputs=out)

修改如下：

def build_generator(self):
        def res_net(inputs, filters):
            x = inputs
            net = conv2d(x, filters // 2, (1, 1), 1)
            net = conv2d(net, filters, (3, 3), 1)
            net = net + x
            return net

        def conv2d(inputs, filters, kernel_size, strides):
            x = tf.keras.layers.Conv2D(filters, kernel_size, strides, 'same')(inputs)
            x = tf.keras.layers.BatchNormalization()(x)
            x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
            return x

        d0 = tf.keras.layers.Input(shape=(10, 128, 128, 3))
        out = tf.keras.layers.ConvRNN2D(filters=32, kernel_size=3, padding='same')(d0)
        out = tf.keras.layers.Conv2D(3, 1, 1, 'same')(out)
        return keras.Model(inputs=d0, outputs=out)

将 `ConvLSTM2D` 替换为 `ConvRNN2D` 即可，它们的参数意义和使用方法都是一样的。