类型错误：“Conv2DTranspose”类型的对象没有 len()

Question

我正在使用 Keras 编写自动编码器，但不断收到以下错误。我认为这与添加 arg 有关，keras_initializer因为我之前在 Conv2D 中遇到过这个错误，添加了初始化程序并且 Conv2D 有长度。虽然，因为我使用的是tf.keras.layers.reshape，这不是一个有效的论点。

这是整个错误回溯。

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-33-c8370b57aa14> in <module>()
     57 
     58 
---> 59 autoencoder = keras.Model(inputs = encoder_input, outputs = decoder_output, name='autoencoder')
     60 autoencoder.summary()
     61 

4 frames
/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
     89                 warnings.warn('Update your `' + object_name + '` call to the ' +
     90                               'Keras 2 API: ' + signature, stacklevel=2)
---> 91             return func(*args, **kwargs)
     92         wrapper._original_function = func
     93         return wrapper

/usr/local/lib/python3.6/dist-packages/keras/engine/network.py in __init__(self, *args, **kwargs)
     91                 'inputs' in kwargs and 'outputs' in kwargs):
     92             # Graph network
---> 93             self._init_graph_network(*args, **kwargs)
     94         else:
     95             # Subclassed network

/usr/local/lib/python3.6/dist-packages/keras/engine/network.py in _init_graph_network(self, inputs, outputs, name)
    229         # Keep track of the network's nodes and layers.
    230         nodes, nodes_by_depth, layers, layers_by_depth = _map_graph_network(
--> 231             self.inputs, self.outputs)
    232         self._network_nodes = nodes
    233         self._nodes_by_depth = nodes_by_depth

/usr/local/lib/python3.6/dist-packages/keras/engine/network.py in _map_graph_network(inputs, outputs)
   1364                   layer=layer,
   1365                   node_index=node_index,
-> 1366                   tensor_index=tensor_index)
   1367 
   1368     for node in reversed(nodes_in_decreasing_depth):

/usr/local/lib/python3.6/dist-packages/keras/engine/network.py in build_map(tensor, finished_nodes, nodes_in_progress, layer, node_index, tensor_index)
   1345 
   1346         # Propagate to all previous tensors connected to this node.
-> 1347         for i in range(len(node.inbound_layers)):
   1348             x = node.input_tensors[i]
   1349             layer = node.inbound_layers[i]

TypeError: object of type 'Conv2DTranspose' has no len()

这是我的代码：

import tensorflow as tf
import keras
import numpy as np 
import tensorflow.keras
from tensorflow.keras import layers
from tensorflow.keras.datasets import cifar10
from keras.layers import Input, Conv2DTranspose
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
num_classes = 10
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
num_classes = 10
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
#plt.imshow(x_train[1])

encoder_input = tf.keras.layers.Input(shape=(32, 32, 3), name="input")
x = tf.keras.layers.Conv2D(16, 3,activation = 'relu', kernel_initializer = keras.initializers.RandomUniform)(encoder_input)
x = tf.keras.layers.Conv2D(32, 3, activation = 'relu')(x)
x = tf.keras.layers.MaxPooling2D(3)(x)
x = tf.keras.layers.Conv2D(32, 3,activation = 'relu')(x)
x = tf.keras.layers.Conv2D(16, 3, activation = 'relu')(x)
encoder_output = tf.keras.layers.GlobalMaxPooling2D()(x)

encoder = tf.keras.Model(inputs=encoder_input, outputs=encoder_output, name = 'encoder')
encoder.summary()

#Decoder
decoder_input = tf.keras.layers.Reshape((4, 4, 1))(encoder_output)
x = tf.keras.layers.Conv2DTranspose(16, 3, activation = 'relu')(decoder_input)
x = tf.keras.layers.Conv2DTranspose(32, 3, activation = 'relu')(x)
x = tf.keras.layers.UpSampling2D(3)(x)
x = tf.keras.layers.Conv2DTranspose(16, 3, activation = 'relu')(x)
decoder_output = tf.keras.layers.Conv2DTranspose(1, 3, activation = 'relu')(x)


autoencoder = keras.Model(inputs = encoder_input, outputs = decoder_output, name='autoencoder')
autoencoder.summary()

Answer 1

您正在混合tf.keras和keras导入，这不受支持，也不会起作用。您需要选择一种实现并从中导入所有模块/类。