tf2.0 failed to save model if an input is used not used in the model directly #27543
Description
System information
- Have I written custom code (as opposed to using a stock example script provided in TensorFlow): No
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Win 10 x64
- Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: No
- TensorFlow installed from (source or binary): Binary
- TensorFlow version (use command below): 2.0a0
- Python version: 3.7
- Bazel version (if compiling from source): N/A
- GCC/Compiler version (if compiling from source): N/A
- CUDA/cuDNN version: N/A
- GPU model and memory: N/A
Describe the current behavior
I have this use case where the model requires multiple inputs and some inputs go into the model layers and some inputs go into the loss function directly. It works fine in the past until I updated my package unit test to tf2.0 to test drive and find out tf2.0 failed test that run fine with tf1.x
I noticed that its mainly due to the tensor that goes directly to the loss function in this case is not presented in self._network_nodes in the line if node_key not in nn.keras_model._network_nodes: in tensorflow keras network.py.
Describe the expected behavior
I expect the model to be saved successfully with tf2.0 just as tf1.x
Code to reproduce the issue
import numpy as np
import tensorflow as tf
import tensorflow.keras as tfk
Sequence = tfk.utils.Sequence
Dense = tfk.layers.Dense
Input = tfk.layers.Input
Model = tfk.models.Model
def special_loss(weights):
def special_loss_internal(true, pred):
return (true - pred / weights)
return special_loss_internal
# Model 1 which does not have Flatten
input_tensor1 = Input(shape=[200], name='input_1')
input_tensor2 = Input(shape=[10], name='input_2')
output_tensor1 = Dense(units=10, name='output_1')(input_tensor1)
output_tensor2 = Dense(units=10, name='output_2')(input_tensor1)
neuralnet = Model(inputs=[input_tensor1, input_tensor2], outputs=[output_tensor1, output_tensor2])
neuralnet.compile(loss=special_loss(input_tensor2), optimizer='adam')
neuralnet.save("test.h5")Other info / logs
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
~\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\util\nest.py in pack_sequence_as(structure, flat_sequence, expand_composites)
430 final_index, packed = _packed_nest_with_indices(structure, flat_sequence,
--> 431 0, is_seq)
432 if final_index < len(flat_sequence):
~\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\util\nest.py in _packed_nest_with_indices(structure, flat, index, is_seq)
380 else:
--> 381 packed.append(flat[index])
382 index += 1
IndexError: list index out of range
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-1-6f88ca6a69df> in <module>
26 neuralnet.compile(loss=special_loss(input_tensor2), optimizer='adam')
27
---> 28 neuralnet.save("test.h5")
~\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\keras\engine\network.py in save(self, filepath, overwrite, include_optimizer)
1312
1313 from tensorflow.python.keras.models import save_model # pylint: disable=g-import-not-at-top
-> 1314 save_model(self, filepath, overwrite, include_optimizer)
1315
1316 def save_weights(self, filepath, overwrite=True, save_format=None):
~\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\keras\saving\hdf5_format.py in save_model(model, filepath, overwrite, include_optimizer)
99 {
100 'class_name': model.__class__.__name__,
--> 101 'config': model.get_config()
102 },
103 default=serialization.get_json_type).encode('utf8')
~\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\keras\engine\network.py in get_config(self)
1107 model_inputs.append(
1108 tf_utils.ListWrapper([layer.name, new_node_index, tensor_index]))
-> 1109 model_inputs = nest.pack_sequence_as(self._nested_inputs, model_inputs)
1110 model_inputs = tf_utils.convert_inner_node_data(model_inputs)
1111 config['input_layers'] = model_inputs
~\Anaconda3\envs\tf2\lib\site-packages\tensorflow\python\util\nest.py in pack_sequence_as(structure, flat_sequence, expand_composites)
438 "Could not pack sequence. Structure had %d elements, but "
439 "flat_sequence had %d elements. Structure: %s, flat_sequence: %s." %
--> 440 (len(flat_structure), len(flat_sequence), structure, flat_sequence))
441 return _sequence_like(structure, packed)
442
ValueError: Could not pack sequence. Structure had 2 elements, but flat_sequence had 1 elements. Structure: [<tf.Tensor 'input_1:0' shape=(None, 200) dtype=float32>, <tf.Tensor 'input_2:0' shape=(None, 10) dtype=float32>], flat_sequence: [<tensorflow.python.keras.utils.tf_utils.ListWrapper object at 0x000001958CF2DDA0>].