Porting Network from Keras to Matlab

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Hamza Yerlikaya
Hamza Yerlikaya 2019-11-18
回答： Sivylla Paraskevopoulou 2022-4-27
I am trying to port a simple network from python+keras to matlab. Model in question is the following,
def build_network(input_features=None):
# first we specify an input layer, with a shape == features
inputs = Input(shape=(input_features,), name="input")
x = Dense(32, activation='relu', name="hidden1")(inputs)
x = Dense(32, activation='relu', name="hidden2")(x)
x = Dense(32, activation='relu', name="hidden3")(x)
x = Dense(32, activation='relu', name="hidden4")(x)
x = Dense(16, activation='relu', name="hidden5")(x)
# for regression we will use a single neuron with linear (no) activation
prediction = Dense(1, activation='linear', name="final")(x)
model = Model(inputs=inputs, outputs=prediction)
model.compile(optimizer='adam', loss='mean_absolute_error')
return model
Looking throught the list of builtin layers [1]. What I've figured out is keras dense layer is matlab fullyConnectedLayer but i can not find a input layer that is not an lstm layer or an image layer. What would be the matlab equavelent of keras's Input layer?
[1] https://www.mathworks.com/help/deeplearning/ug/list-of-deep-learning-layers.html

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回答（1 个）

Sivylla Paraskevopoulou
The MATLAB Deep Learning Toolbox introduced featureInputLayer in R2020b. For more information on how the importTensorFlowNetwork function tranlates TensorFlow-Keras layers to MATLAB layers, see TensorFlow-Keras Layers Supported for Conversion into Built-In MATLAB Layers.

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