MATLAB equivalent functions in Keras

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Ruhi Thomas
Ruhi Thomas 2021-1-2
回答： Aneela 2024-9-9
layers = [ ...
sequenceInputLayer(inputSize)
lstmLayer(numHiddenUnits1)
lstmLayer(numHiddenUnits2)
fullyConnectedLayer(numResponses)
regressionLayer
];
What would be these layers be in Keras?

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回答（1 个）

Aneela
Aneela 2024-9-9
Hi Ruhi Thomas,
If “tf.keras” is the way you imported Keras from TensorFlow, the above layers are equivalent to the following layers in Keras:
sequenceInputLayer(inputSize) –
inputLayer= tf.keras.layers.Input(shape=(None, inputSize))
lstmLayer(numHiddenUnits1) –
lstm_layer1=tf.keras.layers.LSTM(numHiddenUnits1, return_sequences=True)(inputLayer)
lstmLayer(numHiddenUnits2) –
lstm_layer2=tf.keras.layers.LSTM(numHiddenUnits2, return_sequences=True)(inputLayer)
fullyConnectedLayer(numResponses) –
dense_layer = tf.keras.Layers.Dense(numResponses)(lstm_layer2)
regressionLayer –
  • In keras, there is no separate need for regression layer, instead we specify the loss function as part of the model compilation.
  • For a regression task, loss functions like “mean_squared_error”, “mean_absolute_error” are typically used.
model = Model(inputs=input_layer, outputs=dense_layer)
model.compile(optimizer='adam', loss='mean_squared_error')
Hope this helps!!

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