Python tensorflow.python.layers.utils.normalize_tuple() Examples

The following are 17 code examples of tensorflow.python.layers.utils.normalize_tuple(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.python.layers.utils , or try the search function .
Example #1
Source File: pooling.py    From lambda-packs with MIT License 5 votes vote down vote up 
def __init__(self, pool_function, pool_size, strides,
               padding='valid', data_format='channels_last',
               name=None, **kwargs):
    super(_Pooling2D, self).__init__(name=name, **kwargs)
    self.pool_function = pool_function
    self.pool_size = utils.normalize_tuple(pool_size, 2, 'pool_size')
    self.strides = utils.normalize_tuple(strides, 2, 'strides')
    self.padding = utils.normalize_padding(padding)
    self.data_format = utils.normalize_data_format(data_format)
    self.input_spec = base.InputSpec(ndim=4)
Example #2
Source File: convolutional.py    From keras-lambda with MIT License 5 votes vote down vote up 
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
Example #3
Source File: pooling.py    From keras-lambda with MIT License 5 votes vote down vote up 
def __init__(self, pool_function, pool_size, strides,
               padding='valid', data_format='channels_last',
               name=None, **kwargs):
    super(_Pooling3D, self).__init__(name=name, **kwargs)
    self.pool_function = pool_function
    self.pool_size = utils.normalize_tuple(pool_size, 3, 'pool_size')
    self.strides = utils.normalize_tuple(strides, 3, 'strides')
    self.padding = utils.normalize_padding(padding)
    self.data_format = utils.normalize_data_format(data_format)
Example #4
Source File: pooling.py    From keras-lambda with MIT License 5 votes vote down vote up 
def __init__(self, pool_function, pool_size, strides,
               padding='valid', data_format='channels_last',
               name=None, **kwargs):
    super(_Pooling2D, self).__init__(name=name, **kwargs)
    self.pool_function = pool_function
    self.pool_size = utils.normalize_tuple(pool_size, 2, 'pool_size')
    self.strides = utils.normalize_tuple(strides, 2, 'strides')
    self.padding = utils.normalize_padding(padding)
    self.data_format = utils.normalize_data_format(data_format)
Example #5
Source File: pooling.py    From keras-lambda with MIT License 5 votes vote down vote up 
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)
Example #6
Source File: convolutional.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up 
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,
               kernel_constraint=None,
               bias_constraint=None,
               trainable=True,
               name=None,
               **kwargs):
    super(_Conv, self).__init__(trainable=trainable, name=name,
                                activity_regularizer=activity_regularizer,
                                **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.kernel_constraint = kernel_constraint
    self.bias_constraint = bias_constraint
    self.input_spec = base.InputSpec(ndim=self.rank + 2)
Example #7
Source File: pooling.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up 
def __init__(self, pool_function, pool_size, strides,
               padding='valid', data_format='channels_last',
               name=None, **kwargs):
    super(_Pooling3D, self).__init__(name=name, **kwargs)
    self.pool_function = pool_function
    self.pool_size = utils.normalize_tuple(pool_size, 3, 'pool_size')
    self.strides = utils.normalize_tuple(strides, 3, 'strides')
    self.padding = utils.normalize_padding(padding)
    self.data_format = utils.normalize_data_format(data_format)
    self.input_spec = base.InputSpec(ndim=5)
Example #8
Source File: pooling.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up 
def __init__(self, pool_function, pool_size, strides,
               padding='valid', data_format='channels_last',
               name=None, **kwargs):
    super(_Pooling2D, self).__init__(name=name, **kwargs)
    self.pool_function = pool_function
    self.pool_size = utils.normalize_tuple(pool_size, 2, 'pool_size')
    self.strides = utils.normalize_tuple(strides, 2, 'strides')
    self.padding = utils.normalize_padding(padding)
    self.data_format = utils.normalize_data_format(data_format)
    self.input_spec = base.InputSpec(ndim=4)
Example #9
Source File: pooling.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up 
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)
    self.input_spec = base.InputSpec(ndim=3)
Example #10
Source File: convolutional.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
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
Example #11
Source File: pooling.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def __init__(self, pool_function, pool_size, strides,
               padding='valid', data_format='channels_last',
               name=None, **kwargs):
    super(_Pooling3D, self).__init__(name=name, **kwargs)
    self.pool_function = pool_function
    self.pool_size = utils.normalize_tuple(pool_size, 3, 'pool_size')
    self.strides = utils.normalize_tuple(strides, 3, 'strides')
    self.padding = utils.normalize_padding(padding)
    self.data_format = utils.normalize_data_format(data_format)
Example #12
Source File: pooling.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def __init__(self, pool_function, pool_size, strides,
               padding='valid', data_format='channels_last',
               name=None, **kwargs):
    super(_Pooling2D, self).__init__(name=name, **kwargs)
    self.pool_function = pool_function
    self.pool_size = utils.normalize_tuple(pool_size, 2, 'pool_size')
    self.strides = utils.normalize_tuple(strides, 2, 'strides')
    self.padding = utils.normalize_padding(padding)
    self.data_format = utils.normalize_data_format(data_format)
Example #13
Source File: pooling.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
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)
Example #14
Source File: convolutional.py    From lambda-packs with MIT License 5 votes vote down vote up 
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
    self.input_spec = base.InputSpec(ndim=self.rank + 2)
Example #15
Source File: pooling.py    From lambda-packs with MIT License 5 votes vote down vote up 
def __init__(self, pool_function, pool_size, strides,
               padding='valid', data_format='channels_last',
               name=None, **kwargs):
    super(_Pooling3D, self).__init__(name=name, **kwargs)
    self.pool_function = pool_function
    self.pool_size = utils.normalize_tuple(pool_size, 3, 'pool_size')
    self.strides = utils.normalize_tuple(strides, 3, 'strides')
    self.padding = utils.normalize_padding(padding)
    self.data_format = utils.normalize_data_format(data_format)
    self.input_spec = base.InputSpec(ndim=5)
Example #16
Source File: pooling.py    From lambda-packs with MIT License 5 votes vote down vote up 
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)
    self.input_spec = base.InputSpec(ndim=3)
Example #17
Source File: signal_conv.py    From pcc_geo_cnn with MIT License 4 votes vote down vote up 
def __init__(self, rank, filters, kernel_support,
               corr=False, strides_down=1, strides_up=1, padding="valid",
               extra_pad_end=True, channel_separable=False,
               data_format="channels_last",
               activation=None, use_bias=False,
               kernel_initializer=init_ops.VarianceScaling(),
               bias_initializer=init_ops.Zeros(),
               kernel_regularizer=None, bias_regularizer=None,
               kernel_parameterizer=parameterizers.RDFTParameterizer(),
               bias_parameterizer=None,
               **kwargs):
    super(_SignalConv, self).__init__(**kwargs)
    self._rank = int(rank)
    self._filters = int(filters)
    self._kernel_support = utils.normalize_tuple(
        kernel_support, self._rank, "kernel_support")
    self._corr = bool(corr)
    self._strides_down = utils.normalize_tuple(
        strides_down, self._rank, "strides_down")
    self._strides_up = utils.normalize_tuple(
        strides_up, self._rank, "strides_up")
    self._padding = str(padding).lower()
    try:
      self._pad_mode = {
          "valid": None,
          "same_zeros": "CONSTANT",
          "same_reflect": "REFLECT",
      }[self.padding]
    except KeyError:
      raise ValueError("Unsupported padding mode: '{}'".format(padding))
    self._extra_pad_end = bool(extra_pad_end)
    self._channel_separable = bool(channel_separable)
    self._data_format = utils.normalize_data_format(data_format)
    self._activation = activation
    self._use_bias = bool(use_bias)
    self._kernel_initializer = kernel_initializer
    self._bias_initializer = bias_initializer
    self._kernel_regularizer = kernel_regularizer
    self._bias_regularizer = bias_regularizer
    self._kernel_parameterizer = kernel_parameterizer
    self._bias_parameterizer = bias_parameterizer
    self.input_spec = base.InputSpec(ndim=self._rank + 2)