def __init__(self, output_size, input_size=None, ignore_bias=False, initializer=GlorotNormal(), weight_decay=0): self._size_o = output_size self._initializer = initializer self._ignore_bias = ignore_bias self._weight_decay = weight_decay super(Gru, self).__init__(input_size)
def __init__(self, output_size, input_size=None, initializer=GlorotNormal()): self._output_size = output_size self._initializer = initializer super(Weighted_test_model, self).__init__(input_size)
def __init__(self, output_size, input_size=None, initializer=GlorotNormal()): self._size_o = output_size self._initializer = initializer super(PeepholeLstm, self).__init__(input_size)
def __init__(self, output_size, input_size=None, initializer=GlorotNormal()): self._output_size = output_size self._initializer = initializer super(Embedding, self).__init__(input_size)
def __init__(self, channel=32, filter=3, padding=0, stride=1, input_size=None, initializer=GlorotNormal()): self._padding, self._stride, self._kernel = (tuplize(x) for x in (padding, stride, filter)) self._channel = channel self._initializer = initializer super(Conv2d, self).__init__(input_size)
def __init__(self, input_size=None, momentum=0.99, mode="activation", epsilon=1e-5, initializer=GlorotNormal()): self._mov_mean = 0 self._mov_std = 0 self._epsilon = epsilon self._momentum = momentum self._mode = mode_dict.get(mode, BATCH_NORMALIZE_ELEMENTWISE) self.inference = False self._initializer = initializer super(BatchNormalize, self).__init__(input_size)
def __init__(self, channel=32, filter=3, padding=0, stride=1, dilation=1, input_size=None, ignore_bias=False, initializer=GlorotNormal(), weight_decay=0): self._padding, self._stride, self._kernel, self._dilation = ( tuplize(x) for x in (padding, stride, filter, dilation)) self._channel = channel self._ignore_bias = ignore_bias self._initializer = initializer self._weight_decay = weight_decay super(Conv2d, self).__init__(input_size)
def __init__(self, input_size=None, momentum=0.99, mode="activation", epsilon=1e-5, ignore_bias=False, initializer=GlorotNormal(), weight_decay=0): assert momentum > 0, "The value of momentum must be lager than 0." self._mov_mean = 0 self._mov_std = 0 self._epsilon = epsilon self._momentum = momentum self._mode = mode_dict.get(mode, BATCH_NORMALIZE_ELEMENTWISE) self.inference = False self._ignore_bias = ignore_bias self._initializer = initializer self._weight_decay = weight_decay super(BatchNormalize, self).__init__(input_size)
def __init__(self, channel=32, filter=3, padding=0, stride=1, dilation=1, groups=1, input_size=None, ignore_bias=False, initializer=GlorotNormal(), weight_decay=0): self._padding, self._stride, self._kernel, self._dilation = ( tuplize(x) for x in (padding, stride, filter, dilation)) self._channel = channel self._groups = groups #print("self._groups: ", self._groups) assert isinstance( self._groups, int ) and self._groups > 0, "Please set groups to integer greater than 0" self._ignore_bias = ignore_bias self._initializer = initializer self._weight_decay = weight_decay super(GroupConv2d, self).__init__(input_size)
def __init__(self, output_size, initializer=GlorotNormal()): self._size_o = output_size self._initializer = initializer
def __init__(self, output_size, input_size, initializer=GlorotNormal(), weight_decay=None): self._output_size = output_size self._initializer = initializer self._weight_decay = weight_decay super(Embedding, self).__init__(input_size)