contrib.cudnn_rnn.CudnnGRU

tf.contrib.cudnn_rnn.CudnnGRU

class tf.contrib.cudnn_rnn.CudnnGRU

Defined in tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py.

Cudnn implementation of the GRU model. Cudnn RNN has an opaque parameter buffer that can be used for inference and training. But it is possible that the layout of the parameter buffers changes between generations. So it is highly recommended to use RNNParamsSaveable to save and restore weights and biases in a canonical format.

This is a typical use case: * The user creates a CudnnRNN model. * The user query that parameter buffer size. * The user creates a variable of that size that serves as the parameter buffers. * The user either initialize the parameter buffer, or load the canonical weights into the parameter buffer. * The user calls the model with the parameter buffer for inference, or training. * If training, the user creates a Saver object. * If training, the user creates a RNNParamsSaveable object from the parameter buffer for it to be later saved in the canonical format. When creating a RNNParamsSaveable object, a name could be provided, which is useful in distinguishing the names of multiple RNNParamsSaveable objects (e.g. for an encoder-decoder model). * Once a while, the user saves the parameter buffer into model checkpoints with Saver.save(). * When restoring, the user creates a RNNParamsSaveable object and uses Saver.restore() to restore the paramter buffer from the canonical format to a user-defined format, as well as to restore other savable objects in the checkpoint file.

Methods

__init__

__init__(
    num_layers,
    num_units,
    input_size,
    input_mode='auto_select',
    direction='unidirectional',
    dropout=0.0,
    seed=0
)

Creates a Cudnn RNN model from model without hidden-state C.

Args:

• num_layers: the number of layers for the RNN model.
• num_units: the number of units within the RNN model.
• input_size: the size of the input, it could be different from the num_units.
• input_mode: indicate whether there is a linear projection between the input and The actual computation before the first layer. It could be 'skip_input', 'linear_input' or 'auto_select'. '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. Could be either 'unidirectional' or 'bidirectional'
• dropout: whether to enable dropout. With it is 0, dropout is disabled.
• seed: the seed used for initializing dropout.

__call__

__call__(
    input_data,
    input_h,
    params,
    is_training=True
)

Runs the forward step for the Cudnn LSTM model.

Args:

• input_data: the input sequence to the LSTM model.
• input_h: the initial hidden state for h.
• params: the parameter buffer created for this model.
• is_training: whether this operation will be used in training or inference.

Returns:

• output: the output sequuence.
• output_h: the final state for h.

canonical_to_params

canonical_to_params(
    weights,
    biases
)

Converts params from the canonical format to a specific format of cuDNN.

Args:

• weights: a Tensor for weight parameters.
• biases: a Tensor for bias parameters.

Returns:

A function for the canonical-to-params-to-specific conversion..

params_size

params_size()

Calculates the size of the opaque parameter buffer needed for this model.

Returns:

The calculated parameter buffer size.

params_to_canonical

params_to_canonical(params)

Converts params from a specific format of cuDNN to the canonical format.

Args:

• params: a Variable for weight and bias parameters.

Returns:

A function for the specific-to-canonical conversion.