48 lines
1.6 KiB
Python
48 lines
1.6 KiB
Python
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import tensorflow as tf
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from tensorflow.python.ops.rnn_cell import RNNCell
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from my.tensorflow.nn import linear
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class SHCell(RNNCell):
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"""
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Super-Highway Cell
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"""
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def __init__(self, input_size, logit_func='tri_linear', scalar=False):
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self._state_size = input_size
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self._output_size = input_size
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self._logit_func = logit_func
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self._scalar = scalar
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@property
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def state_size(self):
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return self._state_size
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@property
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def output_size(self):
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return self._output_size
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def __call__(self, inputs, state, scope=None):
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with tf.variable_scope(scope or "SHCell"):
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a_size = 1 if self._scalar else self._state_size
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h, u = tf.split(axis=1, num_or_size_splits=2, value=inputs)
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if self._logit_func == 'mul_linear':
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args = [h * u, state * u]
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a = tf.nn.sigmoid(linear(args, a_size, True))
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elif self._logit_func == 'linear':
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args = [h, u, state]
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a = tf.nn.sigmoid(linear(args, a_size, True))
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elif self._logit_func == 'tri_linear':
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args = [h, u, state, h * u, state * u]
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a = tf.nn.sigmoid(linear(args, a_size, True))
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elif self._logit_func == 'double':
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args = [h, u, state]
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a = tf.nn.sigmoid(linear(tf.tanh(linear(args, a_size, True)), self._state_size, True))
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else:
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raise Exception()
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new_state = a * state + (1 - a) * h
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outputs = state
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return outputs, new_state
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