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recurrent_constraint(self)
bias_constraint(self)
dropout(self)
recurrent_dropout(self)
implementation(self)
get_config(self)
activations.serialize(self.activation)
activations.serialize(self.recurrent_activation)
initializers.serialize(self.kernel_initializer)
initializers.serialize(self.recurrent_initializer)
initializers.serialize(self.bias_initializer)
regularizers.serialize(self.kernel_regularizer)
regularizers.serialize(self.recurrent_regularizer)
regularizers.serialize(self.bias_regularizer)
regularizers.serialize(self.activity_regularizer)
constraints.serialize(self.kernel_constraint)
constraints.serialize(self.recurrent_constraint)
constraints.serialize(self.bias_constraint)
super(GRU, self)
get_config()
dict(list(base_config.items()
list(config.items()
from_config(cls, config)
cls(**config)
LSTMCell(Layer)
use (see [activations](../activations.md)
applied (ie. "linear" activation: `a(x)
step (see [activations](../activations.md)
inputs (see [initializers](../initializers.md)
state (see [initializers](../initializers.md)
vector (see [initializers](../initializers.md)
matrix (see [regularizer](../regularizers.md)
matrix (see [regularizer](../regularizers.md)
vector (see [regularizer](../regularizers.md)
matrix (see [constraints](../constraints.md)
matrix (see [constraints](../constraints.md)
vector (see [constraints](../constraints.md)
super(LSTMCell, self)
__init__(**kwargs)
activations.get(activation)
activations.get(recurrent_activation)
initializers.get(kernel_initializer)
initializers.get(recurrent_initializer)
initializers.get(bias_initializer)
regularizers.get(kernel_regularizer)
regularizers.get(recurrent_regularizer)
regularizers.get(bias_regularizer)
constraints.get(kernel_constraint)
constraints.get(recurrent_constraint)
constraints.get(bias_constraint)
min(1., max(0., dropout)
min(1., max(0., recurrent_dropout)
build(self, input_shape)
self.add_weight(shape=(input_dim, self.units * 4)
bias_initializer(_, *args, **kwargs)
self.bias_initializer((self.units,)
initializers.Ones()
self.bias_initializer((self.units * 2,)
self.add_weight(shape=(self.units * 4,)
call(self, inputs, states, training=None)
_generate_dropout_ones(inputs, K.shape(inputs)
_generate_dropout_ones(inputs, self.units)
K.dot(inputs_i, self.kernel_i)
K.dot(inputs_f, self.kernel_f)
K.dot(inputs_c, self.kernel_c)
K.dot(inputs_o, self.kernel_o)
K.bias_add(x_i, self.bias_i)
K.bias_add(x_f, self.bias_f)
K.bias_add(x_c, self.bias_c)
K.bias_add(x_o, self.bias_o)
K.dot(inputs, self.kernel)
K.dot(h_tm1, self.recurrent_kernel)
K.bias_add(z, self.bias)
self.recurrent_activation(z0)
self.recurrent_activation(z1)
self.activation(z2)
self.recurrent_activation(z3)
self.activation(c)
get_config(self)
activations.serialize(self.activation)
activations.serialize(self.recurrent_activation)
initializers.serialize(self.kernel_initializer)
initializers.serialize(self.recurrent_initializer)
initializers.serialize(self.bias_initializer)
regularizers.serialize(self.kernel_regularizer)
regularizers.serialize(self.recurrent_regularizer)
regularizers.serialize(self.bias_regularizer)
constraints.serialize(self.kernel_constraint)
constraints.serialize(self.recurrent_constraint)
constraints.serialize(self.bias_constraint)
super(LSTMCell, self)
get_config()
dict(list(base_config.items()
list(config.items()
LSTM(RNN)
use (see [activations](../activations.md)
applied (ie. "linear" activation: `a(x)
step (see [activations](../activations.md)
inputs. (see [initializers](../initializers.md)
state. (see [initializers](../initializers.md)