Keras: Use the same layer in different models (share weights)
Oh, nevermind.
I should have read the entire functional API: https://keras.io/getting-started/functional-api-guide/#shared-layers
Here's one of the predictions (maybe still lacking some training):
I'm guessing this could be a 3 ? Well at least it works now.
And for those with similar problems, here's the updated code:
inputs=Input((784,))
encode=Dense(10, input_shape=[784])(inputs)
decode=Dense(784, input_shape=[10])
model=Model(input=inputs, output=decode(encode))
model.compile(loss="mse",
optimizer="adadelta",
metrics=["accuracy"])
inputs_2=Input((10,))
decode_model=Model(input=inputs_2, output=decode(inputs_2))
I only compiled one of the models. For training you need to compile a model, for prediction that is not necessary.