Porting Network from Keras to Matlab

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

Answer 1

[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.