From e872a3888cb1135672c0e1d334a0fc3944907907 Mon Sep 17 00:00:00 2001
From: leufen1 <l.leufen@fz-juelich.de>
Date: Thu, 27 May 2021 12:14:26 +0200
Subject: [PATCH] added new parameter dropout_rnn
---
mlair/model_modules/recurrent_networks.py | 14 ++++++++++----
1 file changed, 10 insertions(+), 4 deletions(-)
diff --git a/mlair/model_modules/recurrent_networks.py b/mlair/model_modules/recurrent_networks.py
index cbe5d145..95c48bc8 100644
--- a/mlair/model_modules/recurrent_networks.py
+++ b/mlair/model_modules/recurrent_networks.py
@@ -31,7 +31,7 @@ class RNN(AbstractModelClass):
_rnn = {"lstm": keras.layers.LSTM, "gru": keras.layers.GRU}
def __init__(self, input_shape: list, output_shape: list, activation="relu", activation_output="linear",
- activation_rnn="tanh",
+ activation_rnn="tanh", dropout_rnn=0,
optimizer="adam", n_layer=1, n_hidden=10, regularizer=None, dropout=None, layer_configuration=None,
batch_normalization=False, rnn_type="lstm", add_dense_layer=False, **kwargs):
"""
@@ -42,8 +42,10 @@ class RNN(AbstractModelClass):
Customize this RNN model via the following parameters:
- :param activation: set your desired activation function. Chose from relu, tanh, sigmoid, linear, selu, prelu,
- leakyrelu. (Default relu)
+ :param activation: set your desired activation function for appended dense layers (add_dense_layer=True=. Choose
+ from relu, tanh, sigmoid, linear, selu, prelu, leakyrelu. (Default relu)
+ :param activation_rnn: set your desired activation function of the rnn output. Choose from relu, tanh, sigmoid,
+ linear, selu, prelu, leakyrelu. (Default tanh)
:param activation_output: same as activation parameter but exclusively applied on output layer only. (Default
linear)
:param optimizer: set optimizer method. Can be either adam or sgd. (Default adam)
@@ -55,6 +57,8 @@ class RNN(AbstractModelClass):
hidden layer. The number of hidden layers is equal to the total length of this list.
:param dropout: use dropout with given rate. If no value is provided, dropout layers are not added to the
network at all. (Default None)
+ :param dropout_rnn: use recurrent dropout with given rate. This is applied along the recursion and not after
+ a rnn layer. (Default 0)
:param batch_normalization: use batch normalization layer in the network if enabled. These layers are inserted
between the linear part of a layer (the nn part) and the non-linear part (activation function). No BN layer
is added if set to false. (Default false)
@@ -82,6 +86,8 @@ class RNN(AbstractModelClass):
self.kernel_initializer = self._initializer.get(activation, "glorot_uniform")
# self.kernel_regularizer = self._set_regularizer(regularizer, **kwargs)
self.dropout, self.dropout_rate = self._set_dropout(activation, dropout)
+ assert 0 <= dropout_rnn <= 1
+ self.dropout_rnn = dropout_rnn
# apply to model
self.set_model()
@@ -105,7 +111,7 @@ class RNN(AbstractModelClass):
for layer, n_hidden in enumerate(conf):
return_sequences = (layer < len(conf) - 1)
- x_in = self.RNN(n_hidden, return_sequences=return_sequences)(x_in)
+ x_in = self.RNN(n_hidden, return_sequences=return_sequences, recurrent_dropout=self.dropout_rnn)(x_in)
if self.bn is True:
x_in = keras.layers.BatchNormalization()(x_in)
x_in = self.activation_rnn(name=f"{self.activation_rnn_name}_{layer + 1}")(x_in)
--
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