From 81b4626193d10992f6c8a1b9e76a8caaa4037f71 Mon Sep 17 00:00:00 2001 From: Yaguang Date: Fri, 18 Jan 2019 19:00:34 -0800 Subject: [PATCH] Remove unused code. --- model/dcrnn_model.py | 8 -------- 1 file changed, 8 deletions(-) diff --git a/model/dcrnn_model.py b/model/dcrnn_model.py index 30c74e4..43797fa 100644 --- a/model/dcrnn_model.py +++ b/model/dcrnn_model.py @@ -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)