czzhangheng
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DCRNN
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Remove unused code.

This commit is contained in:
Yaguang 2019-01-18 19:00:34 -08:00
parent 734ecbc138
commit 81b4626193
1 changed files with 0 additions and 8 deletions
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model/dcrnn_model.py
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@ -32,7 +32,6 @@ class DCRNNModel(object):
use_curriculum_learning = bool(model_kwargs.get('use_curriculum_learning', False))
input_dim = int(model_kwargs.get('input_dim', 1))
output_dim = int(model_kwargs.get('output_dim', 1))
aux_dim = input_dim - output_dim
# Input (batch_size, timesteps, num_sensor, input_dim)
self._inputs = tf.placeholder(tf.float32, shape=(batch_size, seq_len, num_nodes, input_dim), name='inputs')
@ -57,9 +56,6 @@ class DCRNNModel(object):
inputs = tf.unstack(tf.reshape(self._inputs, (batch_size, seq_len, num_nodes * input_dim)), axis=1)
labels = tf.unstack(
tf.reshape(self._labels[..., :output_dim], (batch_size, horizon, num_nodes * output_dim)), axis=1)
if aux_dim > 0:
aux_info = tf.unstack(self._labels[..., output_dim:], axis=1)
aux_info.insert(0, None)
labels.insert(0, GO_SYMBOL)
def _loop_function(prev, i):
@ -74,10 +70,6 @@ class DCRNNModel(object):
else:
# Return the prediction of the model in testing.
result = prev
if False and aux_dim > 0:
result = tf.reshape(result, (batch_size, num_nodes, output_dim))
result = tf.concat([result, aux_info[i]], axis=-1)
result = tf.reshape(result, (batch_size, num_nodes * input_dim))
return result
_, enc_state = tf.contrib.rnn.static_rnn(encoding_cells, inputs, dtype=tf.float32)