issue 27543 workaround, allow model with inputs not used to be saved successfully

[henrysky]

This is a workaround for #27543, allowing model with inputs not used to be saved successfully

I think this PR should also be merged to r1.14 and r2.0 branch, this issue does not exist before r1.14

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")

which performs successfully with this patch.

and to load the .h5 model back

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')

model = neuralnet.load_weights('test.h5')

which also performs successfully with this patch.

[k-w-w]

@henrysky Thanks for adding this workaround! This issue has been fixed in this change: 401bbfc#diff-4ee308ea180d49ae81691348531a2b6d