Saving a model that does not import the backend as "K" breaks model loading.

The following code produces this error: NameError: name 'backend' is not defined.

from keras import backend
from keras.layers import Dense, Input, Lambda
from keras.models import load_model, Model

x = Input((100, ), dtype = "float32")
x_i = Lambda(lambda x: x + backend.epsilon())(x)
o = Dense(10, activation = "softmax")(x_i)
model = Model(input = [x], output = o)
model.compile("sgd", loss = "categorical_crossentropy")
model.save("toy_model.h5")
model = load_model("toy_model.h5")

[marsan-ma]

I guess this is more readable way to solve: https://github.com/keras-team/keras/issues/4609#issuecomment-329292173 just wire the used lambda object so keras could know where it’s from, like:

from keras import backend
from keras.models import load_model
from keras.activations import softmax

model = load_model("yourmodel.h5", custom_objects={
    "backend": backend,
    "softmax": softmax,
})

looks like you have to add any extra module used in your lambda layer, even it’s from keras itself (ex: the softmax here)

[joelthchao]

Quick solution: change backend import name, keras doesn’t know your variable in lambda layer

from keras import backend as K
from keras.layers import Dense, Input, Lambda
from keras.models import load_model, Model

x = Input((100, ), dtype = "float32")
x_i = Lambda(lambda x: x + K.epsilon())(x)
o = Dense(10, activation = "softmax")(x_i)
model = Model(input = [x], output = o)
model.compile("sgd", loss = "categorical_crossentropy")
model.save("toy_model.h5")
model = load_model("toy_model.h5")