neulab/docprompting-conala huggingface dataset:
tf.compat.v1.keras.initializers.lecun_normal Initializer capable of adapting its scale to the shape of weights tensors. Inherits From: VarianceScaling 
tf.compat.v1.keras.initializers.lecun_normal(
    seed=None
)
 With distribution="truncated_normal" or "untruncated_normal", samples are drawn from a truncated/untruncated normal distribution with a mean of zero and a standard deviation (after truncation, if used) stddev = sqrt(scale / n) where n is:  number of input units in the weight tensor, if mode = "fan_in" number of output units, if mode = "fan_out" average of the numbers of input and output units, if mode = "fan_avg"  With distribution="uniform", samples are drawn from a uniform distribution within [-limit, limit], with limit = sqrt(3 * scale / n).
 


 Args
  scale   Scaling factor (positive float).  
  mode   One of "fan_in", "fan_out", "fan_avg".  
  distribution   Random distribution to use. One of "normal", "uniform".  
  seed   A Python integer. Used to create random seeds. See tf.compat.v1.set_random_seed for behavior.  
  dtype   Default data type, used if no dtype argument is provided when calling the initializer. Only floating point types are supported.   
 


 Raises
  ValueError   In case of an invalid value for the "scale", mode" or "distribution" arguments.    Methods from_config View source 
@classmethod
from_config(
    config
)
 Instantiates an initializer from a configuration dictionary. Example: initializer = RandomUniform(-1, 1)
config = initializer.get_config()
initializer = RandomUniform.from_config(config)

 


 Args
  config   A Python dictionary. It will typically be the output of get_config.   
 


 Returns   An Initializer instance.  
 get_config View source 
get_config()
 Returns the configuration of the initializer as a JSON-serializable dict.
 


 Returns   A JSON-serializable Python dict.  
 __call__ View source 
__call__(
    shape, dtype=None, partition_info=None
)
 Returns a tensor object initialized as specified by the initializer.
 


 Args
  shape   Shape of the tensor.  
  dtype   Optional dtype of the tensor. If not provided use the initializer dtype.  
  partition_info   Optional information about the possible partitioning of a tensor.
tf.compat.v1.keras.initializers.lecun_uniform Initializer capable of adapting its scale to the shape of weights tensors. Inherits From: VarianceScaling 
tf.compat.v1.keras.initializers.lecun_uniform(
    seed=None
)
 With distribution="truncated_normal" or "untruncated_normal", samples are drawn from a truncated/untruncated normal distribution with a mean of zero and a standard deviation (after truncation, if used) stddev = sqrt(scale / n) where n is:  number of input units in the weight tensor, if mode = "fan_in" number of output units, if mode = "fan_out" average of the numbers of input and output units, if mode = "fan_avg"  With distribution="uniform", samples are drawn from a uniform distribution within [-limit, limit], with limit = sqrt(3 * scale / n).
 


 Args
  scale   Scaling factor (positive float).  
  mode   One of "fan_in", "fan_out", "fan_avg".  
  distribution   Random distribution to use. One of "normal", "uniform".  
  seed   A Python integer. Used to create random seeds. See tf.compat.v1.set_random_seed for behavior.  
  dtype   Default data type, used if no dtype argument is provided when calling the initializer. Only floating point types are supported.   
 


 Raises
  ValueError   In case of an invalid value for the "scale", mode" or "distribution" arguments.    Methods from_config View source 
@classmethod
from_config(
    config
)
 Instantiates an initializer from a configuration dictionary. Example: initializer = RandomUniform(-1, 1)
config = initializer.get_config()
initializer = RandomUniform.from_config(config)

 


 Args
  config   A Python dictionary. It will typically be the output of get_config.   
 


 Returns   An Initializer instance.  
 get_config View source 
get_config()
 Returns the configuration of the initializer as a JSON-serializable dict.
 


 Returns   A JSON-serializable Python dict.  
 __call__ View source 
__call__(
    shape, dtype=None, partition_info=None
)
 Returns a tensor object initialized as specified by the initializer.
 


 Args
  shape   Shape of the tensor.  
  dtype   Optional dtype of the tensor. If not provided use the initializer dtype.  
  partition_info   Optional information about the possible partitioning of a tensor.
tf.compat.v1.keras.initializers.Ones Initializer that generates tensors initialized to 1.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.initializers.ones, tf.compat.v1.keras.initializers.ones, tf.compat.v1.ones_initializer  
tf.compat.v1.keras.initializers.Ones(
    dtype=tf.dtypes.float32
)
 Methods from_config View source 
@classmethod
from_config(
    config
)
 Instantiates an initializer from a configuration dictionary. Example: initializer = RandomUniform(-1, 1)
config = initializer.get_config()
initializer = RandomUniform.from_config(config)

 


 Args
  config   A Python dictionary. It will typically be the output of get_config.   
 


 Returns   An Initializer instance.  
 get_config View source 
get_config()
 Returns the configuration of the initializer as a JSON-serializable dict.
 


 Returns   A JSON-serializable Python dict.  
 __call__ View source 
__call__(
    shape, dtype=None, partition_info=None
)
 Returns a tensor object initialized as specified by the initializer.
 


 Args
  shape   Shape of the tensor.  
  dtype   Optional dtype of the tensor. If not provided use the initializer dtype.  
  partition_info   Optional information about the possible partitioning of a tensor.
tf.compat.v1.keras.initializers.Orthogonal Initializer that generates an orthogonal matrix.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.initializers.orthogonal, tf.compat.v1.keras.initializers.orthogonal, tf.compat.v1.orthogonal_initializer  
tf.compat.v1.keras.initializers.Orthogonal(
    gain=1.0, seed=None, dtype=tf.dtypes.float32
)
 If the shape of the tensor to initialize is two-dimensional, it is initialized with an orthogonal matrix obtained from the QR decomposition of a matrix of random numbers drawn from a normal distribution. If the matrix has fewer rows than columns then the output will have orthogonal rows. Otherwise, the output will have orthogonal columns. If the shape of the tensor to initialize is more than two-dimensional, a matrix of shape (shape[0] * ... * shape[n - 2], shape[n - 1]) is initialized, where n is the length of the shape vector. The matrix is subsequently reshaped to give a tensor of the desired shape.
 


 Args
  gain   multiplicative factor to apply to the orthogonal matrix  
  seed   A Python integer. Used to create random seeds. See tf.compat.v1.set_random_seed for behavior.  
  dtype   Default data type, used if no dtype argument is provided when calling the initializer. Only floating point types are supported.    References: Saxe et al., 2014 (pdf) Methods from_config View source 
@classmethod
from_config(
    config
)
 Instantiates an initializer from a configuration dictionary. Example: initializer = RandomUniform(-1, 1)
config = initializer.get_config()
initializer = RandomUniform.from_config(config)

 


 Args
  config   A Python dictionary. It will typically be the output of get_config.   
 


 Returns   An Initializer instance.  
 get_config View source 
get_config()
 Returns the configuration of the initializer as a JSON-serializable dict.
 


 Returns   A JSON-serializable Python dict.  
 __call__ View source 
__call__(
    shape, dtype=None, partition_info=None
)
 Returns a tensor object initialized as specified by the initializer.
 


 Args
  shape   Shape of the tensor.  
  dtype   Optional dtype of the tensor. If not provided use the initializer dtype.  
  partition_info   Optional information about the possible partitioning of a tensor.
tf.compat.v1.keras.initializers.RandomNormal Initializer that generates tensors with a normal distribution. Inherits From: random_normal_initializer  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.keras.initializers.normal, tf.compat.v1.keras.initializers.random_normal  
tf.compat.v1.keras.initializers.RandomNormal(
    mean=0.0, stddev=0.05, seed=None, dtype=tf.dtypes.float32
)

 


 Args
  mean   a python scalar or a scalar tensor. Mean of the random values to generate.  
  stddev   a python scalar or a scalar tensor. Standard deviation of the random values to generate.  
  seed   A Python integer. Used to create random seeds. See tf.compat.v1.set_random_seed for behavior.  
  dtype   Default data type, used if no dtype argument is provided when calling the initializer. Only floating point types are supported.    Methods from_config View source 
@classmethod
from_config(
    config
)
 Instantiates an initializer from a configuration dictionary. Example: initializer = RandomUniform(-1, 1)
config = initializer.get_config()
initializer = RandomUniform.from_config(config)

 


 Args
  config   A Python dictionary. It will typically be the output of get_config.   
 


 Returns   An Initializer instance.  
 get_config View source 
get_config()
 Returns the configuration of the initializer as a JSON-serializable dict.
 


 Returns   A JSON-serializable Python dict.  
 __call__ View source 
__call__(
    shape, dtype=None, partition_info=None
)
 Returns a tensor object initialized as specified by the initializer.
 


 Args
  shape   Shape of the tensor.  
  dtype   Optional dtype of the tensor. If not provided use the initializer dtype.  
  partition_info   Optional information about the possible partitioning of a tensor.
tf.compat.v1.keras.initializers.RandomUniform Initializer that generates tensors with a uniform distribution. Inherits From: random_uniform_initializer  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.keras.initializers.random_uniform, tf.compat.v1.keras.initializers.uniform  
tf.compat.v1.keras.initializers.RandomUniform(
    minval=-0.05, maxval=0.05, seed=None, dtype=tf.dtypes.float32
)

 


 Args
  minval   A python scalar or a scalar tensor. Lower bound of the range of random values to generate.  
  maxval   A python scalar or a scalar tensor. Upper bound of the range of random values to generate. Defaults to 1 for float types.  
  seed   A Python integer. Used to create random seeds. See tf.compat.v1.set_random_seed for behavior.  
  dtype   Default data type, used if no dtype argument is provided when calling the initializer.    Methods from_config View source 
@classmethod
from_config(
    config
)
 Instantiates an initializer from a configuration dictionary. Example: initializer = RandomUniform(-1, 1)
config = initializer.get_config()
initializer = RandomUniform.from_config(config)

 


 Args
  config   A Python dictionary. It will typically be the output of get_config.   
 


 Returns   An Initializer instance.  
 get_config View source 
get_config()
 Returns the configuration of the initializer as a JSON-serializable dict.
 


 Returns   A JSON-serializable Python dict.  
 __call__ View source 
__call__(
    shape, dtype=None, partition_info=None
)
 Returns a tensor object initialized as specified by the initializer.
 


 Args
  shape   Shape of the tensor.  
  dtype   Optional dtype of the tensor. If not provided use the initializer dtype.  
  partition_info   Optional information about the possible partitioning of a tensor.
tf.compat.v1.keras.initializers.TruncatedNormal Initializer that generates a truncated normal distribution. Inherits From: truncated_normal_initializer  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.keras.initializers.truncated_normal  
tf.compat.v1.keras.initializers.TruncatedNormal(
    mean=0.0, stddev=0.05, seed=None, dtype=tf.dtypes.float32
)
 These values are similar to values from a random_normal_initializer except that values more than two standard deviations from the mean are discarded and re-drawn. This is the recommended initializer for neural network weights and filters.
 


 Args
  mean   a python scalar or a scalar tensor. Mean of the random values to generate.  
  stddev   a python scalar or a scalar tensor. Standard deviation of the random values to generate.  
  seed   A Python integer. Used to create random seeds. See tf.compat.v1.set_random_seed for behavior.  
  dtype   Default data type, used if no dtype argument is provided when calling the initializer. Only floating point types are supported.    Methods from_config View source 
@classmethod
from_config(
    config
)
 Instantiates an initializer from a configuration dictionary. Example: initializer = RandomUniform(-1, 1)
config = initializer.get_config()
initializer = RandomUniform.from_config(config)

 


 Args
  config   A Python dictionary. It will typically be the output of get_config.   
 


 Returns   An Initializer instance.  
 get_config View source 
get_config()
 Returns the configuration of the initializer as a JSON-serializable dict.
 


 Returns   A JSON-serializable Python dict.  
 __call__ View source 
__call__(
    shape, dtype=None, partition_info=None
)
 Returns a tensor object initialized as specified by the initializer.
 


 Args
  shape   Shape of the tensor.  
  dtype   Optional dtype of the tensor. If not provided use the initializer dtype.  
  partition_info   Optional information about the possible partitioning of a tensor.
tf.compat.v1.keras.initializers.VarianceScaling Initializer capable of adapting its scale to the shape of weights tensors.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.initializers.variance_scaling, tf.compat.v1.variance_scaling_initializer  
tf.compat.v1.keras.initializers.VarianceScaling(
    scale=1.0, mode='fan_in', distribution='truncated_normal',
    seed=None, dtype=tf.dtypes.float32
)
 With distribution="truncated_normal" or "untruncated_normal", samples are drawn from a truncated/untruncated normal distribution with a mean of zero and a standard deviation (after truncation, if used) stddev = sqrt(scale / n) where n is:  number of input units in the weight tensor, if mode = "fan_in" number of output units, if mode = "fan_out" average of the numbers of input and output units, if mode = "fan_avg"  With distribution="uniform", samples are drawn from a uniform distribution within [-limit, limit], with limit = sqrt(3 * scale / n).
 


 Args
  scale   Scaling factor (positive float).  
  mode   One of "fan_in", "fan_out", "fan_avg".  
  distribution   Random distribution to use. One of "normal", "uniform".  
  seed   A Python integer. Used to create random seeds. See tf.compat.v1.set_random_seed for behavior.  
  dtype   Default data type, used if no dtype argument is provided when calling the initializer. Only floating point types are supported.   
 


 Raises
  ValueError   In case of an invalid value for the "scale", mode" or "distribution" arguments.    Methods from_config View source 
@classmethod
from_config(
    config
)
 Instantiates an initializer from a configuration dictionary. Example: initializer = RandomUniform(-1, 1)
config = initializer.get_config()
initializer = RandomUniform.from_config(config)

 


 Args
  config   A Python dictionary. It will typically be the output of get_config.   
 


 Returns   An Initializer instance.  
 get_config View source 
get_config()
 Returns the configuration of the initializer as a JSON-serializable dict.
 


 Returns   A JSON-serializable Python dict.  
 __call__ View source 
__call__(
    shape, dtype=None, partition_info=None
)
 Returns a tensor object initialized as specified by the initializer.
 


 Args
  shape   Shape of the tensor.  
  dtype   Optional dtype of the tensor. If not provided use the initializer dtype.  
  partition_info   Optional information about the possible partitioning of a tensor.
tf.compat.v1.keras.initializers.Zeros Initializer that generates tensors initialized to 0.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.initializers.zeros, tf.compat.v1.keras.initializers.zeros, tf.compat.v1.zeros_initializer  
tf.compat.v1.keras.initializers.Zeros(
    dtype=tf.dtypes.float32
)
 Methods from_config View source 
@classmethod
from_config(
    config
)
 Instantiates an initializer from a configuration dictionary. Example: initializer = RandomUniform(-1, 1)
config = initializer.get_config()
initializer = RandomUniform.from_config(config)

 


 Args
  config   A Python dictionary. It will typically be the output of get_config.   
 


 Returns   An Initializer instance.  
 get_config View source 
get_config()
 Returns the configuration of the initializer as a JSON-serializable dict.
 


 Returns   A JSON-serializable Python dict.  
 __call__ View source 
__call__(
    shape, dtype=None, partition_info=None
)
 Returns a tensor object initialized as specified by the initializer.
 


 Args
  shape   Shape of the tensor.  
  dtype   Optional dtype of the tensor. If not provided use the initializer dtype.  
  partition_info   Optional information about the possible partitioning of a tensor.
tf.compat.v1.keras.layers.CuDNNGRU Fast GRU implementation backed by cuDNN. Inherits From: RNN, Layer, Module 
tf.compat.v1.keras.layers.CuDNNGRU(
    units, kernel_initializer='glorot_uniform',
    recurrent_initializer='orthogonal',
    bias_initializer='zeros', kernel_regularizer=None,
    recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None,
    kernel_constraint=None, recurrent_constraint=None, bias_constraint=None,
    return_sequences=False, return_state=False, go_backwards=False, stateful=False,
    **kwargs
)
 More information about cuDNN can be found on the NVIDIA developer website. Can only be run on GPU.
 


 Arguments
  units   Positive integer, dimensionality of the output space.  
  kernel_initializer   Initializer for the kernel weights matrix, used for the linear transformation of the inputs.  
  recurrent_initializer   Initializer for the recurrent_kernel weights matrix, used for the linear transformation of the recurrent state.  
  bias_initializer   Initializer for the bias vector.  
  kernel_regularizer   Regularizer function applied to the kernel weights matrix.  
  recurrent_regularizer   Regularizer function applied to the recurrent_kernel weights matrix.  
  bias_regularizer   Regularizer function applied to the bias vector.  
  activity_regularizer   Regularizer function applied to the output of the layer (its "activation").  
  kernel_constraint   Constraint function applied to the kernel weights matrix.  
  recurrent_constraint   Constraint function applied to the recurrent_kernel weights matrix.  
  bias_constraint   Constraint function applied to the bias vector.  
  return_sequences   Boolean. Whether to return the last output in the output sequence, or the full sequence.  
  return_state   Boolean. Whether to return the last state in addition to the output.  
  go_backwards   Boolean (default False). If True, process the input sequence backwards and return the reversed sequence.  
  stateful   Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch.   
 


 Attributes
  cell  
 
  states  
   Methods reset_states View source 
reset_states(
    states=None
)
 Reset the recorded states for the stateful RNN layer. Can only be used when RNN layer is constructed with stateful = True. Args: states: Numpy arrays that contains the value for the initial state, which will be feed to cell at the first time step. When the value is None, zero filled numpy array will be created based on the cell state size.
 


 Raises
  AttributeError   When the RNN layer is not stateful.  
  ValueError   When the batch size of the RNN layer is unknown.  
  ValueError   When the input numpy array is not compatible with the RNN layer state, either size wise or dtype wise.
tf.compat.v1.keras.layers.CuDNNLSTM Fast LSTM implementation backed by cuDNN. Inherits From: RNN, Layer, Module 
tf.compat.v1.keras.layers.CuDNNLSTM(
    units, kernel_initializer='glorot_uniform',
    recurrent_initializer='orthogonal',
    bias_initializer='zeros', unit_forget_bias=True,
    kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None,
    activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None,
    bias_constraint=None, return_sequences=False, return_state=False,
    go_backwards=False, stateful=False, **kwargs
)
 More information about cuDNN can be found on the NVIDIA developer website. Can only be run on GPU.
 


 Arguments
  units   Positive integer, dimensionality of the output space.  
  kernel_initializer   Initializer for the kernel weights matrix, used for the linear transformation of the inputs.  
  unit_forget_bias   Boolean. If True, add 1 to the bias of the forget gate at initialization. Setting it to true will also force bias_initializer="zeros". This is recommended in Jozefowicz et al.  
  recurrent_initializer   Initializer for the recurrent_kernel weights matrix, used for the linear transformation of the recurrent state.  
  bias_initializer   Initializer for the bias vector.  
  kernel_regularizer   Regularizer function applied to the kernel weights matrix.  
  recurrent_regularizer   Regularizer function applied to the recurrent_kernel weights matrix.  
  bias_regularizer   Regularizer function applied to the bias vector.  
  activity_regularizer   Regularizer function applied to the output of the layer (its "activation").  
  kernel_constraint   Constraint function applied to the kernel weights matrix.  
  recurrent_constraint   Constraint function applied to the recurrent_kernel weights matrix.  
  bias_constraint   Constraint function applied to the bias vector.  
  return_sequences   Boolean. Whether to return the last output. in the output sequence, or the full sequence.  
  return_state   Boolean. Whether to return the last state in addition to the output.  
  go_backwards   Boolean (default False). If True, process the input sequence backwards and return the reversed sequence.  
  stateful   Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch.   
 


 Attributes
  cell  
 
  states  
   Methods reset_states View source 
reset_states(
    states=None
)
 Reset the recorded states for the stateful RNN layer. Can only be used when RNN layer is constructed with stateful = True. Args: states: Numpy arrays that contains the value for the initial state, which will be feed to cell at the first time step. When the value is None, zero filled numpy array will be created based on the cell state size.
 


 Raises
  AttributeError   When the RNN layer is not stateful.  
  ValueError   When the batch size of the RNN layer is unknown.  
  ValueError   When the input numpy array is not compatible with the RNN layer state, either size wise or dtype wise.