From beee5503b517f5ac813d34e6150e3df231c7e860 Mon Sep 17 00:00:00 2001
From: Nikolai Hartmann <Nikolai.Hartmann@physik.uni-muenchen.de>
Date: Tue, 14 Aug 2018 17:43:43 +0200
Subject: [PATCH] load and plot before training RNN
---
toolkit.py | 12 +++++++++---
1 file changed, 9 insertions(+), 3 deletions(-)
diff --git a/toolkit.py b/toolkit.py
index 57ef3f3..1fcf30c 100755
--- a/toolkit.py
+++ b/toolkit.py
@@ -1361,6 +1361,7 @@ class ClassificationProjectRNN(ClassificationProject):
def __init__(self, name,
recurrent_field_names=None,
+ rnn_layer_nodes=32,
mask_value=-999,
**kwargs):
"""
@@ -1376,6 +1377,7 @@ class ClassificationProjectRNN(ClassificationProject):
self.recurrent_field_names = recurrent_field_names
if self.recurrent_field_names is None:
self.recurrent_field_names = []
+ self.rnn_layer_nodes = rnn_layer_nodes
self.mask_value = mask_value
# convert to of indices
@@ -1425,7 +1427,7 @@ 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(32)(channel)
+ channel = GRU(self.rnn_layer_nodes)(channel)
# TODO: configure dropout for recurrent layers
#channel = Dropout(0.3)(channel)
rnn_inputs.append(chan_inp)
@@ -1443,11 +1445,15 @@ class ClassificationProjectRNN(ClassificationProject):
combined = Dense(1, activation=self.activation_function_output)(combined)
self._model = Model(inputs=rnn_inputs+[flat_input], outputs=combined)
self._compile_or_load_model()
- else:
- return self._model
+ return self._model
def train(self, epochs=10):
+ self.load()
+
+ for branch_index, branch in enumerate(self.fields):
+ self.plot_input(branch_index)
+
try:
self.shuffle_training_data() # needed here too, in order to get correct validation data
self.is_training = True
--
GitLab