def testNormalizePadding(self): self.assertEqual( conv_utils.normalize_padding('SAME'), 'same') self.assertEqual( conv_utils.normalize_padding('VALID'), 'valid') with self.assertRaises(ValueError): conv_utils.normalize_padding('invalid')
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)