def __init__(self,units,activation='tanh',initializer='glorotuniform',recurrent_initializer='orthogonal',**kwargs): super(SimpleRNNCell, self).__init__(**kwargs) self.units = units self.activator_cls = get_activator(activation).__class__ self.initializer = get_initializer(initializer) self.recurrent_initializer = get_initializer(recurrent_initializer) self.__first_initialize=True
def __init__(self,epsilon=1e-3,momentum=0.9,axis=-1,gamma_initializer='ones',beta_initializer='zeros',moving_mean_initializer='zeros', moving_variance_initializer='ones'): # axis=-1 when input Fully Connected Layers(data shape:(M,N),where M donotes Batch-size,and N represents feature nums) # axis=1 when input Convolution Layers(data shape:(M,C,H,W),represents Batch-size,Channels,Height,Width,respectively) self.epsilon=epsilon self.axis=axis self.momentum=momentum self.gamma_initializer=get_initializer(gamma_initializer) self.beta_initializer=get_initializer(beta_initializer) self.moving_mean_initializer=get_initializer(moving_mean_initializer) self.moving_variance_initializer=get_initializer(moving_variance_initializer) super(BatchNormalization,self).__init__()
def __init__(self, n_out, n_in=None, initializer='Normal', activation='linear', kernel_regularizer=None): self.n_out = n_out self.n_in = n_in self.initializer = get_initializer(initializer) self.activator = get_activator(activation) self.kernel_regularizer = get_regularizer(kernel_regularizer) super(Dense, self).__init__()
def __init__(self, input_dim, output_dim, embeddings_initializer='uniform', mask_zero=False, input_length=None, **kwargs): super(Embedding, self).__init__(**kwargs) self.input_dim = input_dim self.output_dim = output_dim self.initializer = get_initializer(embeddings_initializer) self.mask_zero = mask_zero self.input_length = input_length
def __init__(self, filter_nums, filter_size, input_shape=None, stride=1, padding='VALID', activation='linear', initializer='Normal'): self.filter_nums = filter_nums self.filter_size = filter_size self.input_shape = input_shape self.stride = stride self.padding = padding self.activator = get_activator(activation) self.initializer = get_initializer(initializer) super(Conv2D, self).__init__(input_shape=input_shape)