def initialize_graph(self, input_statistics):
"""Save templates for components, which can then be used repeatedly.
This method is called every time a new graph is created. It's safe to start
adding ops to the current default graph here, but the graph should be
constructed from scratch.
Args:
input_statistics: A math_utils.InputStatistics object.
"""
super(_LSTMModel, self).initialize_graph(input_statistics=input_statistics)
self._lstm_cell = tf.nn.rnn_cell.LSTMCell(num_units=self._num_units)
# Create templates so we don't have to worry about variable reuse.
self._lstm_cell_run = tf.make_template(
name_="lstm_cell",
func_=self._lstm_cell,
create_scope_now_=True)
# Transforms LSTM output into mean predictions.
self._predict_from_lstm_output = tf.make_template(
name_="predict_from_lstm_output",
func_=lambda inputs: tf.layers.dense(inputs=inputs, units=self.num_features),
create_scope_now_=True)
train_lstm.py 文件源码
python
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