From c496862ff38b2904e07899b25273be5c82621f09 Mon Sep 17 00:00:00 2001
From: Nikolai Hartmann <Nikolai.Hartmann@physik.uni-muenchen.de>
Date: Thu, 23 Aug 2018 09:40:04 +0200
Subject: [PATCH] Adding support for SimpleRNN
---
toolkit.py | 14 +++++++++++---
1 file changed, 11 insertions(+), 3 deletions(-)
diff --git a/toolkit.py b/toolkit.py
index 5b33e00..3498ab3 100755
--- a/toolkit.py
+++ b/toolkit.py
@@ -34,7 +34,7 @@ from sklearn.externals import joblib
from sklearn.metrics import roc_curve, auc
from sklearn.utils.extmath import stable_cumsum
from keras.models import Sequential, Model, model_from_json
-from keras.layers import Dense, Dropout, Input, Masking, GRU, concatenate
+from keras.layers import Dense, Dropout, Input, Masking, GRU, concatenate, SimpleRNN
from keras.callbacks import History, EarlyStopping, CSVLogger, ModelCheckpoint, TensorBoard
from keras.optimizers import SGD
import keras.optimizers
@@ -716,7 +716,7 @@ class ClassificationProject(object):
# plot model
with open(os.path.join(self.project_dir, "model.svg"), "wb") as of:
- of.write(model_to_dot(self._model).create("dot", format="svg"))
+ of.write(model_to_dot(self._model, show_shapes=True).create("dot", format="svg"))
@property
@@ -1598,6 +1598,7 @@ class ClassificationProjectRNN(ClassificationProject):
recurrent_field_names=None,
rnn_layer_nodes=32,
mask_value=-999,
+ recurrent_unit_type="GRU",
**kwargs):
"""
recurrent_field_names example:
@@ -1616,6 +1617,7 @@ class ClassificationProjectRNN(ClassificationProject):
self.recurrent_field_names = []
self.rnn_layer_nodes = rnn_layer_nodes
self.mask_value = mask_value
+ self.recurrent_unit_type = recurrent_unit_type
# convert to of indices
self.recurrent_field_idx = []
@@ -1664,7 +1666,13 @@ class ClassificationProjectRNN(ClassificationProject):
for field_idx in self.recurrent_field_idx:
chan_inp = Input(field_idx.shape[1:])
channel = Masking(mask_value=self.mask_value)(chan_inp)
- channel = GRU(self.rnn_layer_nodes)(channel)
+ if self.recurrent_unit_type == "GRU":
+ channel = GRU(self.rnn_layer_nodes)(channel)
+ elif self.recurrent_unit_type == "SimpleRNN":
+ channel = SimpleRNN(self.rnn_layer_nodes)(channel)
+ else:
+ raise NotImplementedError("{} not implemented".format(self.recurrent_unit_type))
+ logger.info("Added {} unit".format(self.recurrent_unit_type))
# TODO: configure dropout for recurrent layers
#channel = Dropout(0.3)(channel)
rnn_inputs.append(chan_inp)
--
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