Using keras but got Error AttributeError: 'Tensor' object has no attribute '_keras_history'

Question

I am currently using keras to implement an autoencoder in order to upsampling images.

The idea is that I want to use the information of the neighboring images to create an upsampled center image. Both the vertical and horizontal neighbors should share the weights in the encoder. At the bottle neck the vertical and horizontal image patches were concatenated and begin the decoding.

Here is the code:

from keras.models import Model
from keras.layers import Input, Concatenate
from keras.layers import Conv3D, Conv3DTranspose, Conv2DTranspose
from keras.utils import np_utils
from keras import backend as K

# number of feature maps
L = 16
L0 = 24
L1 = 2*L #32
L2 = 4*L #64
L3 = 6*L #96
# input layers
input_LF_v = Input(shape=[5,12,12,3])
input_LF_h = Input(shape=[5,12,12,3])
# 5x12x12
# Encoding path
encoded_1 = Conv3D(L0,(3,3,3),strides=(1, 1, 1),input_shape=[5, 12, 12, 3], padding='SAME',data_format="channels_last")
encoded_2 = Conv3D(L0,(3,3,3),strides=(1, 1, 1),padding='SAME',data_format="channels_last")
encoded_3 = Conv3D(L1,(3,3,3),strides=(1, 2, 2),padding='SAME',data_format="channels_last")
# now 5x6x6
encoded_4 = Conv3D(L1,(3,3,3),strides=(1, 1, 1),padding='SAME',data_format="channels_last")
encoded_5 = Conv3D(L1,(3,3,3),strides=(1, 1, 1),padding='SAME',data_format="channels_last")
encoded_6 = Conv3D(L2,(3,3,3),strides=(1, 2, 2),padding='SAME',data_format="channels_last")
# now 5x3x3

encoded_v1 = encoded_1(input_LF_v)
encoded_v2 = encoded_2(encoded_v1)
encoded_v3 = encoded_3(encoded_v2)
encoded_v4 = encoded_4(encoded_v3)
encoded_v5 = encoded_5(encoded_v4)
encoded_v6 = encoded_6(encoded_v5)

encoded_h1 = encoded_1(input_LF_h)
encoded_h2 = encoded_2(encoded_h1)
encoded_h3 = encoded_3(encoded_h2)
encoded_h4 = encoded_4(encoded_h3)
encoded_h5 = encoded_5(encoded_h4)
encoded_h6 = encoded_6(encoded_h5)

# skip paths
encoded_vh2 = Concatenate()([encoded_v2,encoded_h2]) # 5x12x12x48
encoded_vh5 = Concatenate()([encoded_v5,encoded_h5]) # 5x6x6x64
# bottle neck of encoder
encoded_end = Concatenate()([encoded_v6,encoded_h6]) # 5x3x3x128
# print(encoded_vh2.shape)
# print(encoded_vh5.shape)
# print(encoded_end.shape)

# '''
# decoder starts here
decoded_6 = Conv3DTranspose(L3*2,(3,3,3),strides=(1, 1, 1),input_shape=[5, 3, 3, L3*2],padding='SAME',data_format="channels_last")(encoded_end)
decoded_5 = Conv3DTranspose(L3*2,(3,3,3),strides=(1, 1, 1),padding='SAME',data_format="channels_last")(decoded_6)
decoded_4 = Conv3DTranspose(L3,(3,3,3),strides=(1, 2, 2),padding='SAME',data_format="channels_last")(decoded_5)
# 5x6x6x64
decoded_vh4 = Concatenate()([decoded_4,encoded_vh5]) # 5x6x6x128
#
decoded_3 = Conv3DTranspose(L3*2,(3,3,3),strides=(1, 1, 1),padding='SAME',data_format="channels_last")(decoded_vh4)
decoded_2 = Conv3DTranspose(L3,(3,3,3),strides=(1, 1, 1),padding='SAME',data_format="channels_last")(decoded_3)
decoded_1 = Conv3DTranspose(L0*2,(3,3,3),strides=(1, 2, 2),padding='SAME',data_format="channels_last")(decoded_2)
# 5x12x12x48
decoded_vh1 = Concatenate()([decoded_1,encoded_vh2]) # 5x12x12x96
decoded_0 = Conv3DTranspose(L0*4,(3,3,3),strides=(1, 1, 1),padding='SAME',data_format="channels_last")(decoded_vh1)
decoded_00 = Conv3DTranspose(L0*2,(3,3,3),strides=(1, 1, 1),padding='SAME',data_format="channels_last")(decoded_0)
decoded_up1 = Conv3DTranspose(L0,(3,3,3),strides=(1, 2, 2),padding='SAME',data_format="channels_last")(decoded_00)
# 5x24x24x24
decoded_vh_up = Concatenate()([decoded_up1[:,0,:,:,:],decoded_up1[:,1,:,:,:],decoded_up1[:,2,:,:,:],decoded_up1[:,3,:,:,:],decoded_up1[:,4,:,:,:]])

# 24x24x120
decoded_vh_up0 = Conv2DTranspose(L0*5,(3,3),strides=    (1,1),padding='SAME',data_format="channels_last")(decoded_vh_up)
decoded_vh_up1 = Conv2DTranspose(L2,(3,3),strides=(1,1),padding='SAME',data_format="channels_last")(decoded_vh_up0)
decoded_vh_up2 = Conv2DTranspose(L1,(3,3),strides=(1,1),padding='SAME',data_format="channels_last")(decoded_vh_up1)
decoded_vh_up3 = Conv2DTranspose(L,(3,3),strides=(2,2),padding='SAME',data_format="channels_last")(decoded_vh_up2)
# 48x48x16
decoded_vh_up4 = Conv2DTranspose(L,(3,3),strides=(1,1),padding='SAME',data_format="channels_last")(decoded_vh_up3)
decoded_vh_up5 = Conv2DTranspose(3,(1,1),strides=(1,1),padding='SAME',data_format="channels_last")(decoded_vh_up4)
# 48x48x3

learning_rate = 0.0001
adam = keras.optimizers.Adam(lr=learning_rate)
batch = 20
epoch = 5000
SR_Autoencoder = Model(inputs=[input_LF_v, input_LF_h], outputs = decoded_vh_up5)

SR_Autoencoder.compile(optimizer = adam, loss = 'mse')

SR_Autoencoder.fit([stack_v,stack_h], cv, batch_size = batch, epochs = epoch, validation_split = 0.1, shuffle = True)

When I tried to run it, I got the error message:

Using TensorFlow backend.
Traceback (most recent call last):
  File "/home/z/PycharmProjects/SR/SR_keras.py", line 129, in 
    SR_Autoencoder = Model(inputs=[input_LF_v, input_LF_h], outputs = decoded_vh_up5)
  File "/home/z/tensorflow/lib/python3.4/site-packages/keras/legacy/interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
  File "/home/z/tensorflow/lib/python3.4/site-packages/keras/engine/topology.py", line 1734, in __init__
    build_map_of_graph(x, finished_nodes, nodes_in_progress)
  File "/home/z/tensorflow/lib/python3.4/site-packages/keras/engine/topology.py", line 1724, in build_map_of_graph
    layer, node_index, tensor_index)
  File "/home/z/tensorflow/lib/python3.4/site-packages/keras/engine/topology.py", line 1724, in build_map_of_graph
    layer, node_index, tensor_index)
  File "/home/z/tensorflow/lib/python3.4/site-packages/keras/engine/topology.py", line 1724, in build_map_of_graph
    layer, node_index, tensor_index)
  File "/home/z/tensorflow/lib/python3.4/site-packages/keras/engine/topology.py", line 1724, in build_map_of_graph
    layer, node_index, tensor_index)
  File "/home/z/tensorflow/lib/python3.4/site-packages/keras/engine/topology.py", line 1724, in build_map_of_graph
    layer, node_index, tensor_index)
  File "/home/z/tensorflow/lib/python3.4/site-packages/keras/engine/topology.py", line 1724, in build_map_of_graph
    layer, node_index, tensor_index)
  File "/home/z/tensorflow/lib/python3.4/site-packages/keras/engine/topology.py", line 1724, in build_map_of_graph
    layer, node_index, tensor_index)
  File "/home/z/tensorflow/lib/python3.4/site-packages/keras/engine/topology.py", line 1695, in build_map_of_graph
    layer, node_index, tensor_index = tensor._keras_history
AttributeError: 'Tensor' object has no attribute '_keras_history'

I've searched some answers on stackoverflow, but I can't find any layer that is not a keras layer.

Could anyone give some tips where I got wrong?

Thanks in advance!

Apparently someone has a workaround here. github.com/keras-team/keras/issues/7362 — Marco
yes of course, but I've checked my code, there are only keras layers but no other operations. I'm also a beginner using keras. At this moment I couldn't find any similar mistakes mentioned there. — Minchen Zhu

Daniel Möller Daniel Möller · Accepted Answer · 2018-06-22T14:59:38

Every operation must be performed by "keras layers", it's not possible to have operations outside them.

decoded_vh_up = Lambda(lambda x: K.concatenate(
    [x[:,0,:,:,:],x[:,1,:,:,:],x[:,2,:,:,:],x[:,3,:,:,:],x[:,4,:,:,:]]
))(decoded_up1)

Using keras but got Error AttributeError: 'Tensor' object has no attribute '_keras_history'

1 Answers