def __init__(self, input_dim, output_dim, init='glorot_uniform', activation='tanh', name='Dense', learn_bias=True, negative_bias=False): super(Dense, self).__init__() self.init = initializations.get(init) self.activation = activations.get(activation) self.input_dim = input_dim self.output_dim = output_dim self.linear = (activation == 'linear') # self.input = T.matrix() self.W = self.init((self.input_dim, self.output_dim)) if not negative_bias: self.b = shared_zeros((self.output_dim)) else: self.b = shared_ones((self.output_dim)) self.learn_bias = learn_bias if self.learn_bias: self.params = [self.W, self.b] else: self.params = [self.W] if name is not None: self.set_name(name)
def __init__(self, input_dim1, input_dim2, output_dim, init='glorot_uniform', activation='tanh', name='Dense', learn_bias=True): super(Dense2, self).__init__() self.init = initializations.get(init) self.activation = activations.get(activation) self.input_dim1 = input_dim1 self.input_dim2 = input_dim2 self.output_dim = output_dim self.linear = (activation == 'linear') # self.input = T.matrix() self.W1 = self.init((self.input_dim1, self.output_dim)) self.W2 = self.init((self.input_dim2, self.output_dim)) self.b = shared_zeros((self.output_dim)) self.learn_bias = learn_bias if self.learn_bias: self.params = [self.W1, self.W2, self.b] else: self.params = [self.W1, self.W2] if name is not None: self.set_name(name)
def __init__(self, input_dim, output_dim, init=None, activation='tanh', name='Bias'): super(Constant, self).__init__() assert input_dim == output_dim, 'Bias Layer needs to have the same input/output nodes.' self.init = initializations.get(init) self.activation = activations.get(activation) self.input_dim = input_dim self.output_dim = output_dim self.b = shared_zeros(self.output_dim) self.params = [self.b] if name is not None: self.set_name(name)
def __init__(self, input_dim, input_wdth, init='glorot_uniform', activation='tanh', name='Bias', has_input=True): super(MemoryLinear, self).__init__() self.init = initializations.get(init) self.activation = activations.get(activation) self.input_dim = input_dim self.input_wdth = input_wdth self.b = self.init((self.input_dim, self.input_wdth)) self.params = [self.b] if has_input: self.P = self.init((self.input_dim, self.input_wdth)) self.params += [self.P] if name is not None: self.set_name(name)
def __init__(self, activation): super(Activation, self).__init__() self.activation = activations.get(activation)