Question

我正在尝试建立一个Densenet，但遇到以下错误。这是要重现的最小示例：

import tensorflow as tf
from tensorflow import keras
import tensorflow.keras.layers as tfkl

def create_model(input_dim):

    feature_list = list()
    input_x = tfkl.Input(shape=input_dim, name="Inputs")
    feature_list.append(input_x)
    x = tfkl.Conv2D(16, kernel_size=(3, 3), padding="same", name="Conv1")(input_x)
    feature_list.append(x)

    x = tfkl.Concatenate(axis=-1, name="Concat1")(feature_list)
    x = tfkl.Conv2D(16, kernel_size=(3, 3), padding="same", name="Conv2")(x)
    feature_list.append(x)
    x = tfkl.Concatenate(axis=-1, name="Concat2")(feature_list)

    return tf.keras.Model(inputs=input_x, outputs=x)

z = create_model((128,128,2))

我得到的错误：

ValueError: Graph disconnected: cannot obtain value for tensor Tensor("Conv2/Identity:0", shape=(None, 128, 128, 16), dtype=float32) at layer "Concat1". The following previous layers were accessed without issue: ['Inputs', 'Conv1']

我不知道这个模型有什么问题吗？

在这里使用feature_list的想法是，稍后我可以创建一个具有可变卷积层数的密集块，即：

    for i in range(nb_layers):
          nb_filter += growth_rate
          x = tfkl.Concatenate(axis=-1)(feature_list)
          x = tfkl.Conv2D(nb_filter , kernel_size=(3, 3), padding="same")(x)
          feature_list.append(x)

此外，如果我在create_model函数中注释掉以下两行：

    #feature_list.append(x)
    #x = tfkl.Concatenate(axis=-1, name="Concat2")(feature_list)

然后该模型可以正常工作（因此，通常意味着Concatenate可以正常工作）：

z.summary()
Model: "model"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
Inputs (InputLayer)             [(None, 128, 128, 2) 0                                            
__________________________________________________________________________________________________
Conv1 (Conv2D)                  (None, 128, 128, 16) 304         Inputs[0][0]                     
__________________________________________________________________________________________________
Concat1 (Concatenate)           (None, 128, 128, 18) 0           Inputs[0][0]                     
                                                                 Conv1[0][0]                      
__________________________________________________________________________________________________
Conv2 (Conv2D)                  (None, 128, 128, 16) 2608        Concat1[0][0]                    
==================================================================================================
Total params: 2,912
Trainable params: 2,912
Non-trainable params: 0
__________________________________________________________________________________________________

Answer 1

认为keras功能模型的创建并不急切，列表只是参考。因此，当您将Conv2输出附加到功能列表时，它也会反映在以下行：x = tfkl.Concatenate(axis=-1, name="Concat1")(feature_list)中您实际上没有Conv2输出的地方。

您应该做的是使用功能列表的副本调用Concat图层。请参见下面的示例：

import copy
import tensorflow as tf
from tensorflow import keras
import tensorflow.keras.layers as tfkl

def create_model(input_dim):

    feature_list = list()
    input_x = tfkl.Input(shape=input_dim, name="Inputs")
    feature_list.append(input_x)
    x = tfkl.Conv2D(16, kernel_size=(3, 3), padding="same", name="Conv1")(input_x)
    feature_list.append(x)

    x = tfkl.Concatenate(axis=-1, name="Concat1")(copy.copy(feature_list))
    x = tfkl.Conv2D(16, kernel_size=(3, 3), padding="same", name="Conv2")(x)
    feature_list.append(x)
    print(feature_list)
    x = tfkl.Concatenate(axis=-1, name="Concat2")(copy.copy(feature_list))

    return tf.keras.Model(inputs=input_x, outputs=x)

z = create_model((128,128,2))

Answer 2

我认为这是由于您的图层名称所致，您试图连接具有相同图层名称的数组：[input_x，x，x]，请尝试以下操作：

import tensorflow as tf
from tensorflow import keras
import tensorflow.keras.layers as tfkl

def create_model(input_dim):

    feature_list = list()
    input_x = tfkl.Input(shape=input_dim)
    feature_list.append(input_x)
    conv1 = tfkl.Conv2D(16, kernel_size=(3, 3), padding="same")(input_x)
    feature_list.append(conv1)

    concat1 = tfkl.Concatenate(axis=-1)(feature_list)
    conv2 = tfkl.Conv2D(16, kernel_size=(3, 3), padding="same")(concat1)
    feature_list.append(conv2)

    concat2 = tfkl.Concatenate(axis=-1)(feature_list)

    return tf.keras.Model(inputs=input_x, outputs=concat2)

z = create_model((128,128,2))

DenseNet：图形断开：无法获得张量值

2 个答案: