def __init__(self,
hidden_dim,
activation='tanh',
inner_init='orthogonal',
parameters=None,
return_sequences=True):
self.return_sequences = return_sequences
self.hidden_dim = hidden_dim
self.inner_init = get_initializer(inner_init)
self.activation = get_activation(activation)
self.activation_d = elementwise_grad(self.activation)
self.sigmoid_d = elementwise_grad(sigmoid)
if parameters is None:
self._params = Parameters()
else:
self._params = parameters
self.last_input = None
self.states = None
self.outputs = None
self.gates = None
self.hprev = None
self.input_dim = None
self.W = None
self.U = None
def __init__(self, init='glorot_uniform', scale=0.5, bias=1.0, regularizers=None, constraints=None):
"""A container for layer's parameters.
Parameters
----------
init : str, default 'glorot_uniform'.
The name of the weight initialization function.
scale : float, default 0.5
bias : float, default 1.0
Initial values for bias.
regularizers : dict
Weight regularizers.
{'W' : L2()}
constraints : dict
Weight constraints. {'b' : MaxNorm()}
"""
if constraints is None:
self.constraints = {}
else:
self.constraints = constraints
if regularizers is None:
self.regularizers = {}
else:
self.regularizers = regularizers
self.initial_bias = bias
self.scale = scale
self.init = get_initializer(init)
self._params = {}
self._grads = {}
def __init__(self, init='glorot_uniform', scale=0.5, bias=1.0, regularizers=None, constraints=None):
"""A container for layer's parameters.
Parameters
----------
init : str, default 'glorot_uniform'.
The name of the weight initialization function.
scale : float, default 0.5
bias : float, default 1.0
Initial values for bias.
regularizers : dict
Weight regularizers.
>>> {'W' : L2()}
constraints : dict
Weight constraints.
>>> {'b' : MaxNorm()}
"""
if constraints is None:
self.constraints = {}
else:
self.constraints = constraints
if regularizers is None:
self.regularizers = {}
else:
self.regularizers = regularizers
self.initial_bias = bias
self.scale = scale
self.init = get_initializer(init)
self._params = {}
self._grads = {}
def __init__(self, hidden_dim, activation='tanh', inner_init='orthogonal', parameters=None, return_sequences=True):
self.return_sequences = return_sequences
self.hidden_dim = hidden_dim
self.inner_init = get_initializer(inner_init)
self.activation = get_activation(activation)
self.activation_d = elementwise_grad(self.activation)
if parameters is None:
self._params = Parameters()
else:
self._params = parameters
self.last_input = None
self.states = None
self.hprev = None
self.input_dim = None