4acd61442955baa2509fdc2961284c9d2a986f34,tflearn/layers/recurrent.py,BasicLSTMCell,__call__,#BasicLSTMCell#Any#Any#Any#,637

Before Change

    def __call__(self, inputs, state, scope):
        // Parameters of gates are concatenated into one multiply for efficiency.
        c, h = array_ops.split(1, 2, state)
        self.W, self.b, concat = _linear([inputs, h], 4 * self._num_units,
                                         self.bias, self.W, self.b,
                                         self.W_init,
                                         trainable=self.trainable,

After Change

    def __call__(self, inputs, state, scope=None):
        Long short-term memory cell (LSTM).
        with tf.variable_scope(scope or type(self).__name__):  // "BasicLSTMCell"
            // Parameters of gates are concatenated into one multiply for efficiency.
            if self._state_is_tuple:
                c, h = state
            else:
                c, h = array_ops.split(1, 2, state)
            concat = _linear([inputs, h], 4 * self._num_units, True, 0.,
                             self.weights_init, self.trainable, self.restore,
                             self.reuse)

            // i = input_gate, j = new_input, f = forget_gate, o = output_gate
            i, j, f, o = array_ops.split(1, 4, concat)

            new_c = (c * self._inner_activation(f + self._forget_bias) +
                     self._inner_activation(i) *
                     self._activation(j))
            new_h = self._activation(new_c) * self._inner_activation(o)

            if self._state_is_tuple:
                new_state = _rnn_cell.LSTMStateTuple(new_c, new_h)
            else:
                new_state = array_ops.concat(1, [new_c, new_h])

            // Retrieve RNN Variables
            with tf.variable_scope("Linear", reuse=True):
                self.W = tf.get_variable("Matrix")
                self.b = tf.get_variable("Bias")

            return new_h, new_state


class GRUCell(_rnn_cell.RNNCell):
     TF GRU Cell with extra customization params. 

    def __init__(self, num_units, input_size=None, activation=tf.tanh,

Instances