我正在尝试使用模型子类化方法实现一个类似于UNet的简单模型。这是我的代码：
import tensorflow as tf
from tensorflow import keras as K
class Enc_block(K.layers.Layer):
def __init__(self, in_dim):
super(Enc_block, self).__init__()
self.conv_layer = K.layers.SeparableConv2D(in_dim,3, padding='same', activation='relu')
self.batchnorm_layer = K.layers.BatchNormalization()
self.pool_layer = K.layers.SeparableConv2D(in_dim,3, padding='same',strides=2, activation='relu')
def call(self, x):
x = self.conv_layer(x)
x = self.batchnorm_layer(x)
x = self.conv_layer(x)
x = self.batchnorm_layer(x)
return self.pool_layer(x), x
class Dec_block(K.layers.Layer):
def __init__(self, in_dim):
super(Dec_block, self).__init__()
self.conv_layer = K.layers.SeparableConv2D(in_dim,3, padding='same', activation='relu')
self.batchnorm_layer = K.layers.BatchNormalization()
def call(self, x):
x = self.conv_layer(x)
x = self.batchnorm_layer(x)
x = self.conv_layer(x)
x = self.batchnorm_layer(x)
return x
class Bottleneck(K.layers.Layer):
def __init__(self, in_dim):
super(Bottleneck, self).__init__()
self.conv_1layer = K.layers.SeparableConv2D(in_dim,1, padding='same', activation='relu')
self.conv_3layer = K.layers.SeparableConv2D(in_dim,3, padding='same', activation='relu')
self.batchnorm_layer = K.layers.BatchNormalization()
def call(self, x):
x = self.conv_1layer(x)
x = self.batchnorm_layer(x)
x = self.conv_3layer(x)
x = self.batchnorm_layer(x)
return x
class Output_block(K.layers.Layer):
def __init__(self, in_dim):
super(Output_block, self).__init__()
self.logits = K.layers.SeparableConv2D(in_dim,3, padding='same', activation=None)
self.out = K.layers.Softmax()
def call(self, x):
x_logits = self.logits(x)
x = self.out(x_logits)
return x_logits, x
class UNetModel(K.Model):
def __init__(self,in_dim):
super(UNetModel, self).__init__()
self.encoder_block = Enc_block(in_dim)
self.bottleneck = Bottleneck(in_dim)
self.decoder_block = Dec_block(in_dim)
self.output_block = Output_block(in_dim)
def call(self, inputs, training=None):
x, x_skip1 = self.encoder_block(32)(inputs)
x, x_skip2 = self.encoder_block(64)(x)
x, x_skip3 = self.encoder_block(128)(x)
x, x_skip4 = self.encoder_block(256)(x)
x = self.bottleneck(x)
x = K.layers.UpSampling2D(size=(2,2))(x)
x = K.layers.concatenate([x,x_skip4],axis=-1)
x = self.decoder_block(256)(x)
x = K.layers.UpSampling2D(size=(2,2))(x) #56x56
x = K.layers.concatenate([x,x_skip3],axis=-1)
x = self.decoder_block(128)(x)
x = K.layers.UpSampling2D(size=(2,2))(x) #112x112
x = K.layers.concatenate([x,x_skip2],axis=-1)
x = self.decoder_block(64)(x)
x = K.layers.UpSampling2D(size=(2,2))(x) #224x224
x = K.layers.concatenate([x,x_skip1],axis=-1)
x = self.decoder_block(32)(x)
x_logits, x = self.output_block(2)(x)
return x_logits, x
我遇到以下错误：
ValueError: Input 0 of layer separable_conv2d is incompatible with the layer: expected ndim=4, found ndim=0. Full shape received: []
我不确定这是否是在tf.keras中实现网络的正确方法 这个想法是通过子类化keras层并在以后子类化Model来实现编码器和解码器块。
答案 0 :(得分：1)
看看UNetModel类中的这一行：
x, x_skip1 = self.encoder_block(32)(inputs)
其中self.encoder_block()是由
self.encoder_block = Enc_block(in_dim)
encoder_block是class的实例。通过执行self.encoder_block(32)，您正在调用__call__()类的End_block方法，该方法期望接收到rank=4的可迭代图像输入。相反，您传递的是32的整数rank=0，您会得到ValueError，它正好说明了我刚才解释的内容：expected ndim=4, found ndim=0。您可能打算做的是：
x, x_skip1 = self.encoder_block(inputs)
您在后续各行中也会重复同样的错误。在每个自定义图层上都定义相同的in_dim的地方还有其他错误：
self.encoder_block = Enc_block(in_dim)
self.bottleneck = Bottleneck(in_dim)
self.decoder_block = Dec_block(in_dim)
self.output_block = Output_block(in_dim)
Bottleneck层的输入形状应与Enc_Block层的输出形状相同，因此应相同。我建议您先尝试了解简单的示例，然后再尝试实施更复杂的示例。看一下这个例子。它具有两个自定义层：
import tensorflow as tf
import numpy as np
from tensorflow.keras import layers
class CustomLayer1(layers.Layer):
def __init__(self, outshape=4):
super(CustomLayer1, self).__init__()
self.outshape = outshape
def build(self, input_shape):
self.kernel = self.add_weight(name='kernel',
shape=(int(input_shape[1]), self.outshape),
trainable=True)
super(CustomLayer1, self).build(input_shape)
def call(self, inputs):
return tf.matmul(inputs, self.kernel)
class CustomLayer2(layers.Layer):
def __init__(self):
super(CustomLayer2, self).__init__()
def call(self, inputs):
return inputs / tf.reshape(tf.reduce_sum(inputs, 1), (-1, 1))
现在，我将在新的CombinedLayers类中使用这两个层：
class CombinedLayers(layers.Layer):
def __init__(self, units=3):
super(CombinedLayers, self).__init__()
# `units` defines a number of units in the layer. It is the
# output shape of the `CustomLayer`
self.layer1 = CustomLayer1(units)
# The input shape is inferred dynamically in the `build()`
# method of the `CustomLayer1` class
self.layer2 = CustomLayer1(units)
# Some layers such as this one do not need to know the shape
self.layer3 = CustomLayer2()
def call(self, inputs):
x = self.layer1(inputs)
x = self.layer2(x)
x = self.layer3(x)
return x
请注意，CustomLayer1的输入形状是通过build()方法动态推断的。现在让我们用一些输入来测试它：
x_train = [np.random.normal(size=(3, )) for _ in range(5)]
x_train_tensor = tf.convert_to_tensor(x_train)
combined = CombinedLayers(3)
result = combined(x_train_tensor)
result.numpy()
# array([[ 0.50822063, -0.0800476 , 0.57182697],
# [ -0.76052217, 0.50127872, 1.25924345],
# [-19.5887986 , 9.23529798, 11.35350062],
# [ -0.33696137, 0.22741248, 1.10954888],
# [ 0.53079047, -0.08941536, 0.55862488]])
这就是您应该采取的方式。一层一层地创建。每次添加新层时，都需要使用一些输入来测试所有内容，以验证您的操作是否正确。