CODEWITHSUNDEEP
pythontensorflowmachine-learningkeras
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Reputation: 53

Keras + Tensorflow: 'ConvLSTM2D' object has no attribute 'outbound_nodes'

I’m trying to have a ConvLSTM as part of my functioning tensorflow network, because I had some issues with using the tensorflow ConvLSTM implementation, I settled for using the ConvLSTM2D Keras Layer instead.

To make Keras available in my Tensorflow session I used the blogposts suggestion (I’m using the Tensorflow backend): https://blog.keras.io/keras-as-a-simplified-interface-to-tensorflow-tutorial.html

import tensorflow as tf
sess = tf.Session()

from keras import backend as K
K.set_session(sess)

A snippet of my code (that what causes the issues):

# state has a shape of [1, 75, 32, 32] with batchsize=1
state = tf.concat([screen, screen2, non_spatial], axis=1)

# Reshaping state to get time=1 to have the right shape for the ConvLSTM
state_reshaped = tf.reshape(state, [1, 1, 75, 32, 32])

# Keras ConvLSTM2D Layer
# I tried leaving out the batch_size for the input_shape but it didn't make a difference for the error and it seems to be fine
lstm_layer = ConvLSTM2D(filters=5, kernel_size=(3, 3), input_shape=(1, 1, 75, 32, 32), data_format='channels_first', stateful=True)(state_reshaped)

fc1 = layers.fully_connected(inputs=layers.flatten(lstm_layer), num_outputs=256, activation_fn=tf.nn.relu)

This gives me the following error: AttributeError: 'ConvLSTM2D' object has no attribute 'outbound_nodes’”

I have no idea what this means. I thought it might has to do with mixing Keras ConvLSTM and tensorflows flatten. So I tried using Keras Flatten() instead like this:

# lstm_layer shape is (5, 5, 30, 30)
lstm_layer = Flatten(data_format='channels_first')(lstm_layer)

fc1 = layers.fully_connected(inputs=lstm_layer, num_outputs=256, activation_fn=tf.nn.relu)

and got the following error: ValueError: The last dimension of the inputs to 'Dense' should be defined. Found 'None'. This error is caused by Flatten(), for whatever reason, having an output shape of (?, ?) and the fullyconnected layer needing to have a defined shape for the last dimension but I don't understand why it would be undefined. It was defined before. Using Reshape((4500,))(lstm_layer) instead gives me the same no attribute 'outbound_nodes' error.

I googled the issue and I seem to not be the only one but I couldn't find a solution.

How can I solve this issue? Is the unknown output shape of Flatten() a bug or wanted behavior, if so why?

Upvotes: 2

Views: 7982

Answers (4)

Skum
Skum

Reputation: 506

As others have pointed out, this is because of a mismatch between your installed tensorflow and keras libraries.

Their solutions work, but in my opinion, the cleanest and easiest way to solve this is by using the keras layers contained within the tensorflow package itself rather than by using the keras library directly.

i.e, replace

from keras.layers import ConvLSTM2D

by

from tensorflow.python.keras.layers import ConvLSTM2D

This will ensure that your tensorflow and keras function calls / objects are always compatible, and solved this issue for me.

Upvotes: 0

Georgi Urumov
Georgi Urumov

Reputation: 21

In my case I was getting the error on a custom subclass, but the following solution can be applied nonetheless, if you subclass ConvLSTM2D and add this to your new class:

@property
def outbound_nodes(self):
    if hasattr(self, '_outbound_nodes'):
        print("outbound_nodes called but _outbound_nodes found")
    return getattr(self, '_outbound_nodes', [])

Upvotes: 2

whatasunnday14
whatasunnday14

Reputation: 76

I encountered the same problem and had a bit of a dig into the tensorflow code. The problem is that there was some refactoring done for Keras 2.2.0 and tf.keras hasn't yet been updated to this new API.

The 'outbound_nodes' attribute was renamed to '_outbound_nodes' in Keras 2.2.0. It's pretty easy to fix, there's two references in base.py you need to update:

/site-packages/tensorflow/python/layers/base.py

After updating it works fine for me.

Upvotes: 4

Seen
Seen

Reputation: 53

I found the solution, even though I don't know why it works.

Currently I'm using Tensorflow 1.8 and Keras 2.2. If you downgrade Keras to ~2.1.1 it works without any problems and you can easily use Keras layers together with tensorflow. This fixed AttributeError: 'ConvLSTM2D' object has no attribute 'outbound_nodes’” and then I just used layers.flatten(lstm_layer) and everything worked.

Upvotes: 1

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