def __call__(self, x, **kwargs):
"""Applies the lstm layer.
Args:
x (~chainer.Variable): Time-Batch of input vectors.
Returns:
~chainer.Variable: Output of the lstm layer.
"""
dropout_rate = kwargs.get('dropout', 0.)
dropout_rate_hidden_hidden = kwargs.get('dropout_hidden_hidden', 0.)
x = dropout(x, dropout_rate)
lstm_in = sequence_linear_function(x, self.W_x, self.b)
if self.normalized:
lstm_in = sequence_batch_normalization_function(
lstm_in, self.gamma, self.beta)
if self.stateful:
c_prev = self.c_prev
h_prev = self.h_prev
else:
c_prev = None
h_prev = None
lstm_out, self.h_prev, self.c_prev = \
sequence_lstm_function(lstm_in, self.W_h, c_prev, h_prev,
self.reverse, dropout_rate_hidden_hidden)
return lstm_out
def __call__(self, x, **kwargs):
"""Applies the lstm layer.
Args:
x (~chainer.Variable): Time-Batch of input vectors.
Returns:
~chainer.Variable: Output of the lstm layer.
"""
dropout_rate = kwargs.get('dropout', 0.)
dropout_rate_hidden_hidden = kwargs.get('dropout_hidden_hidden', 0.)
x = dropout(x, dropout_rate)
lstm_in = sequence_linear_function(x, self.W_x, self.b)
if self.normalized:
lstm_in = sequence_batch_normalization_function(lstm_in, self.gamma,
self.beta)
if self.stateful:
c_prev = self.c_prev
h_prev = self.h_prev
else:
c_prev = None
h_prev = None
lstm_out, self.h_prev, self.c_prev = \
sequence_lstm_function(lstm_in, self.W_h, c_prev, h_prev,
self.reverse, dropout_rate_hidden_hidden)
return lstm_out
def __call__(self, x, **kwargs):
"""Applies the linear layer.
Args:
x (~chainer.Variable): Time-Batch of input vectors.
Returns:
~chainer.Variable: Output of the linear layer.
"""
dropout_rate = kwargs.get('dropout', 0.)
x = dropout(x, dropout_rate)
x = sequence_linear_function(x, self.W, self.b)
if self.normalized:
x = sequence_batch_normalization_function(x, self.gamma, self.beta)
return x
def __call__(self, x, **kwargs):
"""Applies the linear layer.
Args:
x (~chainer.Variable): Time-Batch of input vectors.
Returns:
~chainer.Variable: Output of the linear layer.
"""
dropout_rate = kwargs.get('dropout', 0.)
x = dropout(x, dropout_rate)
x = sequence_linear_function(x, self.W, self.b)
if self.normalized:
x = sequence_batch_normalization_function(x, self.gamma, self.beta)
return x
