Last active
April 21, 2020 11:19
Revisions
-
siemanko revised this gist
Mar 27, 2017 . 1 changed file with 1 addition and 0 deletions.There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -1,6 +1,7 @@ class MyLSTMCell(tf.nn.rnn_cell.RNNCell): """Simplified Version rnn_cell.BasicLSTMCell""" def __init__(self, num_units): super(MyLSTMCell, self).__init__() self._num_units = num_units def __call__(self, inputs, state, scope="LSTM"): -
siemanko created this gist
Oct 4, 2016 .There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,44 @@ class MyLSTMCell(tf.nn.rnn_cell.RNNCell): """Simplified Version rnn_cell.BasicLSTMCell""" def __init__(self, num_units): self._num_units = num_units def __call__(self, inputs, state, scope="LSTM"): with tf.variable_scope(scope): c, h = state gates = layers.fully_connected(tf.concat(1, [inputs, h]), num_outputs=4 * self._num_units, activation_fn=None) # i = input_gate, j = new_input, f = forget_gate, o = output_gate i, j, f, o = tf.split(1, 4, gates) forget_bias = 1.0 new_c = (c * tf.nn.sigmoid(f + forget_bias) + tf.nn.sigmoid(i) * tf.nn.tanh(j)) new_h = tf.nn.tanh(new_c) * tf.nn.sigmoid(o) return new_h, (new_c, new_h) def zero_state(self, batch_size, dtype=tf.float32, learnable=False, scope="LSTM"): if learnable: c = tf.get_variable("c_init", (1, self._num_units), initializer=tf.random_normal_initializer(dtype=dtype)) h = tf.get_variable("h_init", (1, self._num_units), initializer=tf.random_normal_initializer(dtype=dtype)) else: c = tf.zeros((1, self._num_units), dtype=dtype) h = tf.zeros((1, self._num_units), dtype=dtype) c = tf.tile(c, [batch_size, 1]) h = tf.tile(h, [batch_size, 1]) c.set_shape([None, self._num_units]) h.set_shape([None, self._num_units]) return (c, h) @property def state_size(self): return (self._num_units, self._num_units) @property def output_size(self): return self._num_units