问题
I have a model composed of 3 LSTM layers followed by a batch norm layer and finally dense layer. Here is the code:
def build_uncomplied_model(hparams):
inputs = tf.keras.Input(shape=(None, hparams["n_features"]))
x = return_RNN(hparams["rnn_type"])(hparams["cell_size_1"], return_sequences=True, recurrent_dropout=hparams['dropout'])(inputs)
x = return_RNN(hparams["rnn_type"])(hparams["cell_size_2"], return_sequences=True)(x)
x = return_RNN(hparams["rnn_type"])(hparams["cell_size_3"], return_sequences=True)(x)
x = layers.BatchNormalization()(x)
outputs = layers.TimeDistributed(layers.Dense(hparams["n_features"]))(x)
model = tf.keras.Model(inputs, outputs, name=RNN_type + "_model")
return model
Now I am aware that to apply MCDropout, we can apply the following code:
y_predict = np.stack([my_model(X_test, training=True) for x in range(100)])
y_proba = y_predict.mean(axis=0)
However, setting training = True
will force the batch norm layer to overfit the testing dataset.
Additionally, building a custom Dropout layer while setting training to True isn't a solution in my case because I am using LSTM.
class MCDropout(tf.keras.layers.Dropout):
def call(self, inputs):
return super().call(inputs, training=True)
Any help is much appreciated!!
回答1:
A possible solution could be to create a custom LSTM layer. You should override the call method to force the training flag to be True
class MCLSTM(keras.layers.LSTM):
def __init__(self, units, **kwargs):
super(MCLSTM, self).__init__(units, **kwargs)
def call(self, inputs, mask=None, training=None, initial_state=None):
return super(MCLSTM, self).call(
inputs,
mask=mask,
training=True,
initial_state=initial_state,
)
Then you can use it in your code
def build_uncomplied_model(hparams):
inputs = tf.keras.Input(shape=(None, hparams["n_features"]))
x = MCLSTM(hparams["cell_size_1"], return_sequences=True, recurrent_dropout=hparams['dropout'])(inputs)
x = return_RNN(hparams["rnn_type"])(hparams["cell_size_2"], return_sequences=True)(x)
x = return_RNN(hparams["rnn_type"])(hparams["cell_size_3"], return_sequences=True)(x)
x = layers.BatchNormalization()(x)
outputs = layers.TimeDistributed(layers.Dense(hparams["n_features"]))(x)
model = tf.keras.Model(inputs, outputs, name=RNN_type + "_model")
return model
or add it to your return_RNN
factory (a more elegant way)
===== EDIT =====
Another solution could be to add the training flag when creating the model. Something like this:
def build_uncomplied_model(hparams):
inputs = tf.keras.Input(shape=(None, hparams["n_features"]))
# This the Monte Carlo LSTM
x = LSTM(hparams["cell_size_1"], return_sequences=True, recurrent_dropout=hparams['dropout'])(inputs, training=True)
x = return_RNN(hparams["rnn_type"])(hparams["cell_size_2"], return_sequences=True)(x)
x = return_RNN(hparams["rnn_type"])(hparams["cell_size_3"], return_sequences=True)(x)
x = layers.BatchNormalization()(x)
outputs = layers.TimeDistributed(layers.Dense(hparams["n_features"]))(x)
model = tf.keras.Model(inputs, outputs, name=RNN_type + "_model")
return model
来源:https://stackoverflow.com/questions/62031302/how-to-apply-monte-carlo-dropout-in-tensorflow-for-an-lstm-if-batch-normalizat