def build(self, input_shape):
self.input_spec = [InputSpec(shape=input_shape)]
if self.stateful:
self.reset_states()
else:
# initial states: all-zero tensor of shape (output_dim)
self.states = [None]
input_dim = input_shape[2]
self.input_dim = input_dim
self.W = self.init((input_dim, self.output_dim),
name='{}_W'.format(self.name))
# Only change in build compared to SimpleRNN:
# U is of shape (inner_input_dim, output_dim) now.
self.U = self.inner_init((self.inner_input_dim, self.output_dim),
name='{}_U'.format(self.name))
self.b = K.zeros((self.output_dim,), name='{}_b'.format(self.name))
self.regularizers = []
if self.W_regularizer:
self.W_regularizer.set_param(self.W)
self.regularizers.append(self.W_regularizer)
if self.U_regularizer:
self.U_regularizer.set_param(self.U)
self.regularizers.append(self.U_regularizer)
if self.b_regularizer:
self.b_regularizer.set_param(self.b)
self.regularizers.append(self.b_regularizer)
self.trainable_weights = [self.W, self.U, self.b]
if self.initial_weights is not None:
self.set_weights(self.initial_weights)
del self.initial_weights
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