tf.raw_ops.CudnnRNNBackpropV2

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Backprop step of CudnnRNN.

tf.raw_ops.CudnnRNNBackpropV2(
    input,
    input_h,
    input_c,
    params,
    output,
    output_h,
    output_c,
    output_backprop,
    output_h_backprop,
    output_c_backprop,
    reserve_space,
    host_reserved,
    rnn_mode='lstm',
    input_mode='linear_input',
    direction='unidirectional',
    dropout=0,
    seed=0,
    seed2=0,
    name=None
)

Compute the backprop of both data and weights in a RNN. Takes an extra "host_reserved" inupt than CudnnRNNBackprop, which is used to determine RNN cudnnRNNAlgo_t and cudnnMathType_t.

rnn_mode: Indicates the type of the RNN model. input_mode: Indicates whether there is a linear projection between the input and the actual computation before the first layer. '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: Indicates whether a bidirectional model will be used. Should be "unidirectional" or "bidirectional". dropout: Dropout probability. When set to 0., dropout is disabled. seed: The 1st part of a seed to initialize dropout. seed2: The 2nd part of a seed to initialize dropout. input: A 3-D tensor with the shape of [seq_length, batch_size, input_size]. input_h: A 3-D tensor with the shape of [num_layer * dir, batch_size, num_units]. input_c: For LSTM, a 3-D tensor with the shape of [num_layer * dir, batch, num_units]. For other models, it is ignored. params: A 1-D tensor that contains the weights and biases in an opaque layout. The size must be created through CudnnRNNParamsSize, and initialized separately. Note that they might not be compatible across different generations. So it is a good idea to save and restore output: A 3-D tensor with the shape of [seq_length, batch_size, dir * num_units]. output_h: The same shape has input_h. output_c: The same shape as input_c for LSTM. An empty tensor for other models. output_backprop: A 3-D tensor with the same shape as output in the forward pass. output_h_backprop: A 3-D tensor with the same shape as output_h in the forward pass. output_c_backprop: A 3-D tensor with the same shape as output_c in the forward pass. reserve_space: The same reserve_space produced in the forward operation. host_reserved: The same host_reserved produced in the forward operation. input_backprop: The backprop to input in the forward pass. Has the same shape as input. input_h_backprop: The backprop to input_h in the forward pass. Has the same shape as input_h. input_c_backprop: The backprop to input_c in the forward pass. Has the same shape as input_c. params_backprop: The backprop to the params buffer in the forward pass. Has the same shape as params.