本文整理汇总了Python中tensorflow.keras.layers.InputSpec方法的典型用法代码示例。如果您正苦于以下问题:Python layers.InputSpec方法的具体用法?Python layers.InputSpec怎么用?Python layers.InputSpec使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.keras.layers
的用法示例。
在下文中一共展示了layers.InputSpec方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: build
# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import InputSpec [as 别名]
def build(self, input_shape):
channel_axis = -1
if input_shape[0][channel_axis] is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
input_dim = input_shape[0][channel_axis]
kernel_shape = self.kernel_size + (input_dim, self.filters)
if input_shape[1][-1] != input_dim:
raise ValueError('The last dimension of modulation input should be equal to input dimension.')
self.kernel = self.add_weight(shape=kernel_shape,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
# Set input spec.
self.input_spec = [InputSpec(ndim=4, axes={channel_axis: input_dim}),
InputSpec(ndim=2)]
self.built = True
示例2: __init__
# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import InputSpec [as 别名]
def __init__(self, first_threshold=None, second_threshold=None,
use_dimension_bias=False, use_intermediate_layer=False,
intermediate_dim=64, intermediate_activation=None,
from_logits=False, return_logits=False,
bias_initializer=1.0, **kwargs):
# if 'input_shape' not in kwargs:
# kwargs['input_shape'] = [(None, input_dim,), (None, input_dim)]
super(WeightedCombinationLayer, self).__init__(**kwargs)
self.first_threshold = first_threshold if first_threshold is not None else INFTY
self.second_threshold = second_threshold if second_threshold is not None else INFTY
self.use_dimension_bias = use_dimension_bias
self.use_intermediate_layer = use_intermediate_layer
self.intermediate_dim = intermediate_dim
self.intermediate_activation = tf.keras.activations.get(intermediate_activation)
self.from_logits = from_logits
self.return_logits = return_logits
self.bias_initializer = bias_initializer
self.input_spec = [InputSpec(), InputSpec(), InputSpec()]
示例3: __init__
# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import InputSpec [as 别名]
def __init__(
self,
kernel_size,
sigma,
upsample_factor,
index=None,
coordinate_scale=1.0,
confidence_scale=1.0,
data_format=None,
**kwargs
):
super(SubpixelMaxima2D, self).__init__(**kwargs)
self.data_format = normalize_data_format(data_format)
self.input_spec = InputSpec(ndim=4)
self.kernel_size = kernel_size
self.sigma = sigma
self.upsample_factor = upsample_factor
self.index = index
self.coordinate_scale = coordinate_scale
self.confidence_scale = confidence_scale
示例4: build
# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import InputSpec [as 别名]
def build(self, input_shape):
dim = input_shape[self.axis]
self.input_spec = InputSpec(ndim=len(input_shape), axes={self.axis: dim})
shape = (dim,)
self.tau = self.add_weight(shape=shape,
name='tau',
initializer=self.tau_initializer,
regularizer=self.tau_regularizer,
constraint=self.tau_constraint)
self.gamma = self.add_weight(shape=shape,
name='gamma',
initializer=self.gamma_initializer,
regularizer=self.gamma_regularizer,
constraint=self.gamma_constraint)
self.beta = self.add_weight(shape=shape,
name='beta',
initializer=self.beta_initializer,
regularizer=self.beta_regularizer,
constraint=self.beta_constraint)
self.built = True
示例5: __init__
# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import InputSpec [as 别名]
def __init__(self,
filters,
kernel_size,
strides=1,
padding='valid',
dilation_rate=1,
kernel_initializer='glorot_uniform',
kernel_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
demod=True,
**kwargs):
super(Conv2DMod, self).__init__(**kwargs)
self.filters = filters
self.rank = 2
self.kernel_size = conv_utils.normalize_tuple(kernel_size, 2, 'kernel_size')
self.strides = conv_utils.normalize_tuple(strides, 2, 'strides')
self.padding = conv_utils.normalize_padding(padding)
self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, 2, 'dilation_rate')
self.kernel_initializer = initializers.get(kernel_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.demod = demod
self.input_spec = [InputSpec(ndim = 4),
InputSpec(ndim = 2)]
示例6: build
# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import InputSpec [as 别名]
def build(self, input_shape):
dim = input_shape[self.axis]
if dim is None:
raise ValueError("Axis " + str(self.axis) + " of "
"input tensor should have a defined dimension "
"but the layer received an input with shape " +
str(input_shape) + ".")
if dim < self.groups:
raise ValueError("Number of groups (" + str(self.groups) + ") "
"cannot be more than the number of channels (" +
str(dim) + ").")
if dim % self.groups != 0:
raise ValueError("Number of groups (" + str(self.groups) + ") "
"must be a multiple of the number of channels (" +
str(dim) + ").")
self.input_spec = InputSpec(ndim=len(input_shape),
axes={self.axis: dim})
shape = (dim,)
if self.scale:
self.gamma = self.add_weight(shape=shape,
name="gamma",
initializer=self.gamma_initializer,
regularizer=self.gamma_regularizer,
constraint=self.gamma_constraint)
else:
self.gamma = None
if self.center:
self.beta = self.add_weight(shape=shape,
name="beta",
initializer=self.beta_initializer,
regularizer=self.beta_regularizer,
constraint=self.beta_constraint)
else:
self.beta = None
self.built = True
示例7: build
# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import InputSpec [as 别名]
def build(self, input_shape):
if len(input_shape) < 4:
raise ValueError(
"Inputs to `QDepthwiseConv2D` should have rank 4. "
"Received input shape:", str(input_shape))
if self.data_format == "channels_first":
channel_axis = 1
else:
channel_axis = 3
if input_shape[channel_axis] is None:
raise ValueError("The channel dimension of the inputs to "
"`QDepthwiseConv2D` "
"should be defined. Found `None`.")
input_dim = int(input_shape[channel_axis])
depthwise_kernel_shape = (self.kernel_size[0], self.kernel_size[1],
input_dim, self.depth_multiplier)
self.depthwise_kernel = self.add_weight(
shape=depthwise_kernel_shape,
initializer=self.depthwise_initializer,
name="depthwise_kernel",
regularizer=self.depthwise_regularizer,
constraint=self.depthwise_constraint)
if self.use_bias:
self.bias = self.add_weight(
shape=(input_dim * self.depth_multiplier,),
initializer=self.bias_initializer,
name="bias",
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
# Set input spec.
self.input_spec = InputSpec(ndim=4, axes={channel_axis: input_dim})
self.built = True
示例8: __init__
# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import InputSpec [as 别名]
def __init__(self, n_clusters, weights=None, alpha=1.0, **kwargs):
if 'input_shape' not in kwargs and 'input_dim' in kwargs:
kwargs['input_shape'] = (kwargs.pop('input_dim'),)
super(ClusteringLayer, self).__init__(**kwargs)
self.n_clusters = n_clusters
self.alpha = alpha
self.initial_weights = weights
self.input_spec = InputSpec(ndim=2)
示例9: build
# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import InputSpec [as 别名]
def build(self, input_shape):
assert len(input_shape) == 2
input_dim = input_shape.as_list()[1]
self.input_spec = InputSpec(dtype=K.floatx(), shape=(None, input_dim))
self.clusters = self.add_weight(shape=(self.n_clusters, input_dim), initializer='glorot_uniform', name='clusters')
if self.initial_weights is not None:
self.set_weights(self.initial_weights)
del self.initial_weights
self.built = True
示例10: __init__
# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import InputSpec [as 别名]
def __init__(self, padding=(1, 1), **kwargs):
super(ReflectionPadding2D, self).__init__(**kwargs)
padding = tuple(padding)
self.padding = ((0, 0), padding, padding, (0, 0))
self.input_spec = [InputSpec(ndim=4)]
示例11: __init__
# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import InputSpec [as 别名]
def __init__(self, padding=(1, 1), **kwargs):
self.padding = tuple(padding)
self.input_spec = [InputSpec(ndim=4)]
super(ReflectionPadding2D, self).__init__(**kwargs)
示例12: build
# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import InputSpec [as 别名]
def build(self, input_shape):
assert len(input_shape) >= 2
input_dim = input_shape[-1]
self.gate_kernel = self.add_weight(
shape=(input_dim, input_dim), initializer='uniform', name='gate_kernel')
self.gate_bias = self.add_weight(
shape=(input_dim,), initializer=self.bias_initializer, name='gate_bias')
self.dense_kernel = self.add_weight(
shape=(input_dim, input_dim), initializer='uniform', name='dense_kernel')
self.dense_bias = self.add_weight(
shape=(input_dim,), initializer=self.bias_initializer, name='dense_bias')
self.input_spec = InputSpec(min_ndim=2, axes={-1: input_dim})
self.built = True
示例13: __init__
# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import InputSpec [as 别名]
def __init__(
self,
index=None,
coordinate_scale=1.0,
confidence_scale=1.0,
data_format=None,
**kwargs
):
super(Maxima2D, self).__init__(**kwargs)
self.data_format = normalize_data_format(data_format)
self.input_spec = InputSpec(ndim=4)
self.index = index
self.coordinate_scale = coordinate_scale
self.confidence_scale = confidence_scale
示例14: __init__
# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import InputSpec [as 别名]
def __init__(self, upsampling=(2, 2), data_format=None, **kwargs):
super(BilinearUpsampling, self).__init__(**kwargs)
self.data_format = conv_utils.normalize_data_format(data_format)
self.upsampling = conv_utils.normalize_tuple(upsampling, 2, 'size')
self.input_spec = InputSpec(ndim=4)
示例15: build
# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import InputSpec [as 别名]
def build(self, input_shape):
self.input_spec = [InputSpec(shape=input_shape)]
shape = (int(input_shape[self.axis]),)
self.gamma = tf.Variable(self.gamma_init(shape),trainable=True)#, name='{}_gamma'.format(self.name)
self.beta = tf.Variable(self.beta_init(shape),trainable=True)#, name='{}_beta'.format(self.name)
#self.trainable_weights = [self.gamma, self.beta]
if self.initial_weights is not None:
self.set_weights(self.initial_weights)
del self.initial_weights