tf.contrib.cudnn

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tf.contrib.cudnn

2024-01-31 12:58| 来源: 网络整理| 查看: 265

Class CudnnLSTM

Defined in tensorflow/contrib/cudnn_rnn/python/layers/cudnn_rnn.py.

Cudnn implementation of LSTM layer.

__init__ __init__( num_layers, num_units, input_mode=CUDNN_INPUT_LINEAR_MODE, direction=CUDNN_RNN_UNIDIRECTION, dropout=0.0, seed=None, dtype=tf.dtypes.float32, kernel_initializer=None, bias_initializer=None, name=None )

Creates a CudnnRNN model from model spec.

Args: num_layers: the number of layers for the RNN model. num_units: the number of units within the RNN model. input_mode: indicate whether there is a linear projection between the input and the actual computation before the first layer. It can be 'linear_input', 'skip_input' or 'auto_select'. 'linear_input' (default) always applies a linear projection of input onto RNN hidden state. (standard RNN behavior). 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: the direction model that the model operates. Can be either 'unidirectional' or 'bidirectional' dropout: dropout rate, a number between [0, 1]. Dropout is applied between each layer (no dropout is applied for a model with a single layer). When set to 0, dropout is disabled. seed: the op seed used for initializing dropout. See tf.set_random_seed for behavior. dtype: tf.float16, tf.float32 or tf.float64 kernel_initializer: starting value to initialize the weight. bias_initializer: starting value to initialize the bias (default is all zeros). name: VariableScope for the created subgraph; defaults to class name. This only serves the default scope if later no scope is specified when invoking call(). Raises: ValueError: if direction is invalid. Or dtype is not supported. Properties activity_regularizer

Optional regularizer function for the output of this layer.

canonical_bias_shapes

Shapes of Cudnn canonical bias tensors.

canonical_weight_shapes

Shapes of Cudnn canonical weight tensors.

direction

Returns unidirectional or bidirectional.

dtype graph input

Retrieves the input tensor(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.

Returns:

Input tensor or list of input tensors.

Raises: AttributeError: if the layer is connected to more than one incoming layers. Raises: RuntimeError: If called in Eager mode. AttributeError: If no inbound nodes are found. input_mask

Retrieves the input mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns:

Input mask tensor (potentially None) or list of input mask tensors.

Raises: AttributeError: if the layer is connected to more than one incoming layers. input_mode

Input mode of first layer.

Indicates whether there is a linear projection between the input and the actual computation before the first layer. It can be * 'linear_input': (default) always applies a linear projection of input onto RNN hidden state. (standard RNN behavior) * 'skip_input': 'skip_input' is only allowed when input_size == num_units. * 'auto_select'. implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'.

Returns:

'linear_input', 'skip_input' or 'auto_select'.

input_shape

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.

Returns:

Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor).

Raises: AttributeError: if the layer has no defined input_shape. RuntimeError: if called in Eager mode. input_size losses

Losses which are associated with this Layer.

Variable regularization tensors are created when this property is accessed, so it is eager safe: accessing losses under a tf.GradientTape will propagate gradients back to the corresponding variables.

Returns:

A list of tensors.

name non_trainable_variables non_trainable_weights num_dirs num_layers num_units output

Retrieves the output tensor(s) of a layer.

Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.

Returns:

Output tensor or list of output tensors.

Raises: AttributeError: if the layer is connected to more than one incoming layers. RuntimeError: if called in Eager mode. output_mask

Retrieves the output mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns:

Output mask tensor (potentially None) or list of output mask tensors.

Raises: AttributeError: if the layer is connected to more than one incoming layers. output_shape

Retrieves the output shape(s) of a layer.

Only applicable if the layer has one output, or if all outputs have the same shape.

Returns:

Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor).

Raises: AttributeError: if the layer has no defined output shape. RuntimeError: if called in Eager mode. rnn_mode

Type of RNN cell used.

Returns:

lstm, gru, rnn_relu or rnn_tanh.

saveable scope_name trainable_variables trainable_weights updates variables

Returns the list of all layer variables/weights.

Alias of self.weights.

Returns:

A list of variables.

weights

Returns the list of all layer variables/weights.

Returns:

A list of variables.

Methods tf.contrib.cudnn_rnn.CudnnLSTM.__call__ __call__( inputs, *args, **kwargs )

Wraps call, applying pre- and post-processing steps.

Arguments: inputs: input tensor(s). *args: additional positional arguments to be passed to self.call. **kwargs: additional keyword arguments to be passed to self.call. Note: kwarg scope is reserved for use by the layer. Returns:

Output tensor(s).

Note: - If the layer's call method takes a scope keyword argument, this argument will be automatically set to the current variable scope. - If the layer's call method takes a mask argument (as some Keras layers do), its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support. Raises: ValueError: if the layer's call method returns None (an invalid value). tf.contrib.cudnn_rnn.CudnnLSTM.__deepcopy__ __deepcopy__(memo) tf.contrib.cudnn_rnn.CudnnLSTM.__setattr__ __setattr__( name, value )

Implement setattr(self, name, value).

tf.contrib.cudnn_rnn.CudnnLSTM.apply apply( inputs, *args, **kwargs )

Apply the layer on a input.

This is an alias of self.__call__.

Arguments: inputs: Input tensor(s). *args: additional positional arguments to be passed to self.call. **kwargs: additional keyword arguments to be passed to self.call. Returns:

Output tensor(s).

tf.contrib.cudnn_rnn.CudnnLSTM.build build(input_shape)

Create variables of the Cudnn RNN.

It can be called manually before __call__() or automatically through __call__(). In the former case, subsequent __call__()s will skip creating variables.

Args: input_shape: network input tensor shape, a python list or a TensorShape object with 3 dimensions. Raises: ValueError: if input_shape has wrong dimension or unknown 3rd dimension. tf.contrib.cudnn_rnn.CudnnLSTM.compute_mask compute_mask( inputs, mask=None )

Computes an output mask tensor.

Arguments: inputs: Tensor or list of tensors. mask: Tensor or list of tensors. Returns:

None or a tensor (or list of tensors, one per output tensor of the layer).

tf.contrib.cudnn_rnn.CudnnLSTM.compute_output_shape compute_output_shape(input_shape)

Computes the output shape of the layer.

Assumes that the layer will be built to match that input shape provided.

Arguments: input_shape: Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer. Returns:

An input shape tuple.

tf.contrib.cudnn_rnn.CudnnLSTM.count_params count_params()

Count the total number of scalars composing the weights.

Returns:

An integer count.

Raises: ValueError: if the layer isn't yet built (in which case its weights aren't yet defined). tf.contrib.cudnn_rnn.CudnnLSTM.from_config from_config( cls, config )

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Arguments: config: A Python dictionary, typically the output of get_config. Returns:

A layer instance.

tf.contrib.cudnn_rnn.CudnnLSTM.get_config get_config()

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Returns:

Python dictionary.

tf.contrib.cudnn_rnn.CudnnLSTM.get_input_at get_input_at(node_index)

Retrieves the input tensor(s) of a layer at a given node.

Arguments: node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. Returns:

A tensor (or list of tensors if the layer has multiple inputs).

Raises: RuntimeError: If called in Eager mode. tf.contrib.cudnn_rnn.CudnnLSTM.get_input_mask_at get_input_mask_at(node_index)

Retrieves the input mask tensor(s) of a layer at a given node.

Arguments: node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. Returns:

A mask tensor (or list of tensors if the layer has multiple inputs).

tf.contrib.cudnn_rnn.CudnnLSTM.get_input_shape_at get_input_shape_at(node_index)

Retrieves the input shape(s) of a layer at a given node.

Arguments: node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. Returns:

A shape tuple (or list of shape tuples if the layer has multiple inputs).

Raises: RuntimeError: If called in Eager mode. tf.contrib.cudnn_rnn.CudnnLSTM.get_losses_for get_losses_for(inputs)

Retrieves losses relevant to a specific set of inputs.

Arguments: inputs: Input tensor or list/tuple of input tensors. Returns:

List of loss tensors of the layer that depend on inputs.

Raises: RuntimeError: If called in Eager mode. tf.contrib.cudnn_rnn.CudnnLSTM.get_output_at get_output_at(node_index)

Retrieves the output tensor(s) of a layer at a given node.

Arguments: node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. Returns:

A tensor (or list of tensors if the layer has multiple outputs).

Raises: RuntimeError: If called in Eager mode. tf.contrib.cudnn_rnn.CudnnLSTM.get_output_mask_at get_output_mask_at(node_index)

Retrieves the output mask tensor(s) of a layer at a given node.

Arguments: node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. Returns:

A mask tensor (or list of tensors if the layer has multiple outputs).

tf.contrib.cudnn_rnn.CudnnLSTM.get_output_shape_at get_output_shape_at(node_index)

Retrieves the output shape(s) of a layer at a given node.

Arguments: node_index: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. Returns:

A shape tuple (or list of shape tuples if the layer has multiple outputs).

Raises: RuntimeError: If called in Eager mode. tf.contrib.cudnn_rnn.CudnnLSTM.get_updates_for get_updates_for(inputs)

Retrieves updates relevant to a specific set of inputs.

Arguments: inputs: Input tensor or list/tuple of input tensors. Returns:

List of update ops of the layer that depend on inputs.

Raises: RuntimeError: If called in Eager mode. tf.contrib.cudnn_rnn.CudnnLSTM.get_weights get_weights()

Returns the current weights of the layer.

Returns:

Weights values as a list of numpy arrays.

tf.contrib.cudnn_rnn.CudnnLSTM.set_weights set_weights(weights)

Sets the weights of the layer, from Numpy arrays.

Arguments: weights: a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of get_weights). Raises: ValueError: If the provided weights list does not match the layer's specifications. tf.contrib.cudnn_rnn.CudnnLSTM.state_shape state_shape(batch_size)

Shape of Cudnn LSTM states.

Shape is a 2-element tuple. Each is [num_layers * num_dirs, batch_size, num_units]

Args: batch_size: an int Returns:

a tuple of python arrays.

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