def build(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape)
if input_shape[-1].value is None:
raise ValueError('The last dimension of the inputs to `Dense` '
'should be defined. Found `None`.')
self.input_spec = base.InputSpec(min_ndim=2,
axes={-1: input_shape[-1].value})
self.kernel = self.add_variable(
'kernel',
shape=[input_shape[-1].value, self.units],
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
dtype=self.dtype,
trainable=True)
if self.use_bias:
self.bias = self.add_variable('bias',
shape=[
self.units,
],
initializer=self.bias_initializer,
regularizer=self.bias_regularizer,
dtype=self.dtype,
trainable=True)
else:
self.bias = None
self.built = True
def build(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape)
if not input_shape.ndims:
raise ValueError('Input has undefined rank:', input_shape)
ndim = input_shape.ndims
if self.axis < 0:
axis = ndim + self.axis
else:
axis = self.axis
if axis < 0 or axis >= ndim:
raise ValueError('Value of `axis` argument ' + str(self.axis) +
' is out of range for input with rank ' + str(ndim))
if axis + 1 == ndim:
self._data_format = 'NHWC'
elif axis == 1:
self._data_format = 'NCHW'
else:
raise ValueError(
'Only axis 1 or last axis are currently supported dimensions for '
'batch norm. Got `axis` dimension: ', axis)
param_dim = input_shape[axis]
if not param_dim.value:
raise ValueError('Input has undefined `axis` dimension. Input shape: ', input_shape)
self.input_spec = base.InputSpec(ndim=ndim, axes={self.axis: param_dim.value})
self.moving_mean = self.add_variable(
name='moving_mean',
shape=(param_dim.value,),
initializer=self.moving_mean_initializer,
trainable=False)
self.moving_variance = self.add_variable(
name='moving_variance',
shape=(param_dim.value,),
initializer=self.moving_variance_initializer,
trainable=False)
self.gamma = self.add_variable(
name='gamma',
shape=(param_dim.value,),
initializer=self.gamma_initializer,
regularizer=self.gamma_regularizer,
trainable=self.scale)
self.beta = self.add_variable(
name='beta',
shape=(param_dim.value,),
initializer=self.beta_initializer,
regularizer=self.beta_regularizer,
trainable=self.center)
self.built = True
def build(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape)
if self.data_format == 'channels_first':
channel_axis = 1
else:
channel_axis = -1
if input_shape[channel_axis].value is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
input_dim = input_shape[channel_axis].value
if self.data_format == 'channels_first':
# For channels first: (n_out, n_in, k_h, k_w)
kernel_shape = (self.filters, input_dim) + self.kernel_size
else:
# For channels last: (k_h, k_w, n_in, n_out)
kernel_shape = self.kernel_size + (input_dim, self.filters)
self.kernel = self.add_variable(
name='kernel',
shape=kernel_shape,
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
dtype=self.dtype,
)
if self.use_bias:
self.bias = self.add_variable(
name='bias',
shape=(self.filters, ),
initializer=self.bias_initializer,
regularizer=self.bias_regularizer,
dtype=self.dtype,
)
else:
self.bias = None
self.input_spec = base.InputSpec(ndim=self.rank + 2,
axes={channel_axis: input_dim})
self.built = True
