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

Question

I'm coding an autoencoder using Keras and I keep getting the below error. I think it's related to adding the arg keras_initializer since I got this error before for Conv2D, added the initializer and Conv2D had length. Although, since I'm using tf.keras.layers.reshape, this isn't a valid argument.

Here is the entire error traceback.

---------------------------------------------------------------------------
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()

This is my code:

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

Use from tensorflow import keras for above case.

Updated code:

import tensorflow as tf
from tensorflow import keras

num_classes = 10
(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')


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()

output:

x_train shape: (60000, 32, 32, 3)
60000 train samples
10000 test samples
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input (InputLayer)           (None, 32, 32, 3)         0         
_________________________________________________________________
conv2d_35 (Conv2D)           (None, 30, 30, 16)        448       
_________________________________________________________________
conv2d_36 (Conv2D)           (None, 28, 28, 32)        4640      
_________________________________________________________________
max_pooling2d_8 (MaxPooling2 (None, 9, 9, 32)          0         
_________________________________________________________________
conv2d_37 (Conv2D)           (None, 7, 7, 32)          9248      
_________________________________________________________________
conv2d_38 (Conv2D)           (None, 5, 5, 16)          4624      
_________________________________________________________________
global_max_pooling2d_8 (Glob (None, 16)                0         
=================================================================
Total params: 18,960
Trainable params: 18,960
Non-trainable params: 0
_________________________________________________________________
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input (InputLayer)           (None, 32, 32, 3)         0         
_________________________________________________________________
conv2d_35 (Conv2D)           (None, 30, 30, 16)        448       
_________________________________________________________________
conv2d_36 (Conv2D)           (None, 28, 28, 32)        4640      
_________________________________________________________________
max_pooling2d_8 (MaxPooling2 (None, 9, 9, 32)          0         
_________________________________________________________________
conv2d_37 (Conv2D)           (None, 7, 7, 32)          9248      
_________________________________________________________________
conv2d_38 (Conv2D)           (None, 5, 5, 16)          4624      
_________________________________________________________________
global_max_pooling2d_8 (Glob (None, 16)                0         
_________________________________________________________________
reshape_6 (Reshape)          (None, 4, 4, 1)           0         
_________________________________________________________________
conv2d_transpose_16 (Conv2DT (None, 6, 6, 16)          160       
_________________________________________________________________
conv2d_transpose_17 (Conv2DT (None, 8, 8, 32)          4640      
_________________________________________________________________
up_sampling2d_4 (UpSampling2 (None, 24, 24, 32)        0         
_________________________________________________________________
conv2d_transpose_18 (Conv2DT (None, 26, 26, 16)        4624      
_________________________________________________________________
conv2d_transpose_19 (Conv2DT (None, 28, 28, 1)         145       
=================================================================
Total params: 28,529
Trainable params: 28,529
Non-trainable params: 0
_________________________________________________________________

Answer 2

You are mixing tf.keras and keras imports, and this is not supported and it will not work. You need to choose one implementation and import all modules/classes from it.