Lambda layer error when loading a model

[jumutc]

Hi, I have a strange TypeError when loading a model with Lambda layer.

Versions of TF and Keras:

tensorflow==1.12.0
Keras==2.2.4

Code snippet:

from keras.layers import LSTM, Dropout, RepeatVector, TimeDistributed, Dense, Input, Lambda, concatenate
from keras.models import Sequential, Model, load_model
from keras.optimizers import RMSprop
from keras import backend as K
import tensorflow as tf

def zero_loss(y_true, _):
    return K.zeros(K.shape(y_true))

def _build_encoder(input_shape):
    model = Sequential(name='encoder')
    model.add(Dropout(0.1, input_shape=input_shape))
    model.add(LSTM(10, name="recurrent"))
    model.add(Dropout(0.1))

    return model

def _build_decoder(output_shape):
    model = Sequential(name='decoder')
    model.add(RepeatVector(10, input_shape=(10,)))
    model.add(LSTM(10, name="recurrent", return_sequences=True))
    model.add(Dropout(0.1))
    model.add(TimeDistributed(Dense(output_shape[-1])))

    return model

def _build_model():
    with tf.name_scope("keras_model"):
        input_shape = (10, 50)

        encoder = _build_encoder(input_shape)
        decoder = _build_decoder(input_shape)

        input_data = Input(shape=input_shape)

        encoder_out = Lambda(lambda x: encoder(x), name='encoder_out')(input_data)
        losses = {'decoder': 'mse', 'encoder_out': zero_loss}

        model = Model(inputs=[input_data], outputs=[decoder(encoder_out), encoder_out])
        model.compile(loss=losses, optimizer=RMSprop())

        return model

model = _build_model()
model.save("keras.model")

load_model("keras.model", custom_objects={'zero_loss': zero_loss})

Error:

Traceback (most recent call last):
  File "keras_test_snippet.py", line 47, in <module>
    load_model("keras.model", custom_objects={'zero_loss': zero_loss})
  File "/usr/local/lib/python3.5/dist-packages/keras/engine/saving.py", line 419, in load_model
    model = _deserialize_model(f, custom_objects, compile)
  File "/usr/local/lib/python3.5/dist-packages/keras/engine/saving.py", line 225, in _deserialize_model
    model = model_from_config(model_config, custom_objects=custom_objects)
  File "/usr/local/lib/python3.5/dist-packages/keras/engine/saving.py", line 458, in model_from_config
    return deserialize(config, custom_objects=custom_objects)
  File "/usr/local/lib/python3.5/dist-packages/keras/layers/__init__.py", line 55, in deserialize
    printable_module_name='layer')
  File "/usr/local/lib/python3.5/dist-packages/keras/utils/generic_utils.py", line 145, in deserialize_keras_object
    list(custom_objects.items())))
  File "/usr/local/lib/python3.5/dist-packages/keras/engine/network.py", line 1032, in from_config
    process_node(layer, node_data)
  File "/usr/local/lib/python3.5/dist-packages/keras/engine/network.py", line 991, in process_node
    layer(unpack_singleton(input_tensors), **kwargs)
  File "/usr/local/lib/python3.5/dist-packages/keras/engine/base_layer.py", line 457, in __call__
    output = self.call(inputs, **kwargs)
  File "/usr/local/lib/python3.5/dist-packages/keras/layers/core.py", line 687, in call
    return self.function(inputs, **arguments)
  File "keras_test_snippet.py", line 36, in <lambda>
    encoder_out = Lambda(lambda x: encoder(x), name='encoder_out')(input_data)
TypeError: 'dict' object is not callable

[gabrieldemarmiesse]

Please do not put a model in a layer. Please do encoder_out = encoder(input_data)

[jumutc]

Isn't it better to raise an exception earlier on save to prevent such situations @gabrieldemarmiesse?