From 3a94611ecb5c13f0afd93baafdd66bd1c206a0fe Mon Sep 17 00:00:00 2001
From: leufen1 <l.leufen@fz-juelich.de>
Date: Thu, 27 May 2021 11:16:27 +0200
Subject: [PATCH] add dense layer between rnn and output
---
mlair/model_modules/recurrent_networks.py | 6 +++++-
1 file changed, 5 insertions(+), 1 deletion(-)
diff --git a/mlair/model_modules/recurrent_networks.py b/mlair/model_modules/recurrent_networks.py
index 7adc9111..ab28085b 100644
--- a/mlair/model_modules/recurrent_networks.py
+++ b/mlair/model_modules/recurrent_networks.py
@@ -32,7 +32,7 @@ class RNN(AbstractModelClass):
def __init__(self, input_shape: list, output_shape: list, activation="relu", activation_output="linear",
optimizer="adam", n_layer=1, n_hidden=10, regularizer=None, dropout=None, layer_configuration=None,
- batch_normalization=False, rnn_type="lstm", **kwargs):
+ batch_normalization=False, rnn_type="lstm", add_dense_layer=False, **kwargs):
"""
Sets model and loss depending on the given arguments.
@@ -72,6 +72,7 @@ class RNN(AbstractModelClass):
self.activation_output_name = activation_output
self.optimizer = self._set_optimizer(optimizer.lower(), **kwargs)
self.bn = batch_normalization
+ self.add_dense_layer = add_dense_layer
self.layer_configuration = (n_layer, n_hidden) if layer_configuration is None else layer_configuration
self.RNN = self._rnn.get(rnn_type.lower())
self._update_model_name(rnn_type)
@@ -108,6 +109,9 @@ class RNN(AbstractModelClass):
if self.dropout is not None:
x_in = self.dropout(self.dropout_rate)(x_in)
+ if self.add_dense_layer is True:
+ x_in = keras.layers.Dense(min(self._output_shape ** 2, conf[-1]), ame=f"Dense_{len(conf) + 1}")(x_in)
+ x_in = self.activation(name=f"{self.activation_name}_{len(conf) + 1}")(x_in)
x_in = keras.layers.Dense(self._output_shape)(x_in)
out = self.activation_output(name=f"{self.activation_output_name}_output")(x_in)
self.model = keras.Model(inputs=x_input, outputs=[out])
--
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