Keras versioning error about tybalt

[gwaybio]

Thanks @ajlee21 - if possible, can you post the error here?

Tybalt currently uses Keras 2.0.6 and tensorflow 1.0.1 but its probably time for an update and for this to be fixed.

[ajlee21]

Using TensorFlow backend.
/home/alexandra/anaconda2/envs/python3/lib/python3.6/site-packages/ipykernel_launcher.py:135: UserWarning: Output "custom_variational_layer_1" missing from loss dictionary. We assume this was done on purpose, and we will not be expecting any data to be passed to "custom_variational_layer_1" during training.

---

ValueError Traceback (most recent call last)
in ()
139 hist = vae.fit(np.array(rnaseq_train_df), shuffle=True, epochs=epochs, batch_size=batch_size,
140 validation_data=(np.array(rnaseq_test_df), np.array(rnaseq_test_df)),
--> 141 callbacks=[WarmUpCallback(beta, kappa)])
142
143 # Save training performance

~/anaconda2/envs/python3/lib/python3.6/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
1612 sample_weight=val_sample_weight,
1613 check_batch_axis=False,
-> 1614 batch_size=batch_size)
1615 if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
1616 val_ins = val_x + val_y + val_sample_weights + [0.]

~/anaconda2/envs/python3/lib/python3.6/site-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_batch_axis, batch_size)
1428 output_shapes,
1429 check_batch_axis=False,
-> 1430 exception_prefix='target')
1431 sample_weights = _standardize_sample_weights(sample_weight,
1432 self._feed_output_names)

~/anaconda2/envs/python3/lib/python3.6/site-packages/keras/engine/training.py in _standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
53 raise ValueError('Error when checking model ' +
54 exception_prefix + ': '
---> 55 'expected no data, but got:', data)
56 return []
57 if data is None:

ValueError: ('Error when checking model target: expected no data, but got:', array([[5.000000e+00, 2.440000e+02, 4.000000e+00, ..., 1.486718e+06,
7.000000e+00, 6.000000e+00],
[0.000000e+00, 2.880000e+02, 0.000000e+00, ..., 3.668960e+05,
0.000000e+00, 2.000000e+00],
[7.000000e+00, 1.620000e+02, 4.000000e+00, ..., 6.256400e+05,
3.000000e+00, 2.000000e+00],
...,
[1.300000e+01, 3.045000e+03, 6.000000e+00, ..., 5.851519e+06,
4.100000e+01, 4.800000e+01],
[7.000000e+00, 5.060000e+02, 2.000000e+00, ..., 1.172282e+06,
2.000000e+00, 6.000000e+00],
[2.000000e+00, 6.970000e+02, 1.000000e+00, ..., 3.982010e+05,
1.000000e+00, 0.000000e+00]]))

[gwaybio]

It looks like Keras 2.1.3 (source) expects the argument to be:

validation_data=(x_array, None)

# instead of the previously required
validation_data=(x_array, x_array)

I saw this solution in keras-team/keras#7856

I can confirm this works in fitting Tybalt

print(keras.__version__)
tf.__version__
2.1.3
'1.4.1'