def __init__(self, rank,
filters,
kernel_size,
strides=1,
padding='valid',
data_format='channels_last',
dilation_rate=1,
activation=None,
use_bias=True,
kernel_initializer=None,
bias_initializer=init_ops.zeros_initializer,
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
trainable=True,
name=None,
**kwargs):
super(_Conv, self).__init__(trainable=trainable,
name=name, **kwargs)
self.rank = rank
self.filters = filters
self.kernel_size = utils.normalize_tuple(kernel_size, rank, 'kernel_size')
self.strides = utils.normalize_tuple(strides, rank, 'strides')
self.padding = utils.normalize_padding(padding)
self.data_format = utils.normalize_data_format(data_format)
self.dilation_rate = utils.normalize_tuple(
dilation_rate, rank, 'dilation_rate')
self.activation = activation
self.use_bias = use_bias
self.kernel_initializer = kernel_initializer
self.bias_initializer = bias_initializer
self.kernel_regularizer = kernel_regularizer
self.bias_regularizer = bias_regularizer
self.activity_regularizer = activity_regularizer
def __init__(self, rank,
filters,
kernel_size,
strides=1,
padding='valid',
data_format='channels_last',
dilation_rate=1,
activation=None,
use_bias=True,
kernel_initializer=None,
bias_initializer=init_ops.zeros_initializer,
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
trainable=True,
name=None,
**kwargs):
super(_Conv, self).__init__(trainable=trainable,
name=name, **kwargs)
self.rank = rank
self.filters = filters
self.kernel_size = utils.normalize_tuple(kernel_size, rank, 'kernel_size')
self.strides = utils.normalize_tuple(strides, rank, 'strides')
self.padding = utils.normalize_padding(padding)
self.data_format = utils.normalize_data_format(data_format)
self.dilation_rate = utils.normalize_tuple(
dilation_rate, rank, 'dilation_rate')
self.activation = activation
self.use_bias = use_bias
self.kernel_initializer = kernel_initializer
self.bias_initializer = bias_initializer
self.kernel_regularizer = kernel_regularizer
self.bias_regularizer = bias_regularizer
self.activity_regularizer = activity_regularizer
def __init__(self, pool_function, pool_size, strides,
padding='valid', data_format='channels_last',
name=None, **kwargs):
super(_Pooling1D, self).__init__(name=name, **kwargs)
self.pool_function = pool_function
self.pool_size = utils.normalize_tuple(pool_size, 1, 'pool_size')
self.strides = utils.normalize_tuple(strides, 1, 'strides')
self.padding = utils.normalize_padding(padding)
self.data_format = utils.normalize_data_format(data_format)
def testNormalizeTuple(self):
self.assertEqual(
conv_utils.normalize_tuple(2, n=3, name='strides'), (2, 2, 2))
self.assertEqual(
conv_utils.normalize_tuple((2, 1, 2), n=3, name='strides'), (2, 1, 2))
with self.assertRaises(ValueError):
conv_utils.normalize_tuple((2, 1), n=3, name='strides')
with self.assertRaises(ValueError):
conv_utils.normalize_tuple(None, n=3, name='strides')