Hi.
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?
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