Feedforward Net convert from Python

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I have an example of a feedforward network written in Python using an ADAM optimizer which I want to replicate in Matlab. The basics are
network = models.Sequential()
network.add(layers.Dense(units=64, activation='relu', input_shape=(len(features.columns),)))
network.add(layers.Dense(units=32, activation='relu'))
network.add(layers.Dense(units=1, activation='sigmoid'))
network.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
es = EarlyStopping(monitor='val_loss', mode='min', verbose=0, patience=500)
mc = ModelCheckpoint('data/best_model.h5', monitor='val_loss', mode='min', verbose=2, save_best_only=True)
history = network.fit(train_features, train_target,
epochs=1000, verbose=0, batch_size=128,
validation_data=(test_features, test_target), callbacks=[es, mc])
I believe I cannot use the Adam optimizer in the feedforward function so can I directly convert this or woud I have to create some layers myself rather than use the feedforward function?

Accepted Answer

Srivardhan Gadila
Srivardhan Gadila on 16 Mar 2020
You can train the above network in keras framework and import it to matlab using the importKerasLayers, importKerasNetwork functions.
Alternatively you can define the above network in matlab using the Deep Learning Layers in MATLAB and mention the 'adam' optimizer as the sovlerName in the trainingOptions.

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