tf.contrib.rnn.DropoutWrapper
Defined in tensorflow/python/ops/rnn_cell_impl.py.
def __init__(self, cell, input_keep_prob=1.0, output_keep_prob=1.0, state_keep_prob=1.0, variational_recurrent=False, input_size=None, dtype=None, seed=None): Args: cell: an RNNCell, a projection to output_size is added to it. input_keep_prob: unit Tensor or float between 0 and 1, input keep probability; if it is constant and 1, no input dropout will be added. output_keep_prob: unit Tensor or float between 0 and 1, output keep probability; if it is constant and 1, no output dropout will be added. state_keep_prob: unit Tensor or float between 0 and 1, output keep probability; if it is constant and 1, no output dropout will be added. State dropout is performed on the *output* states of the cell. variational_recurrent: Python bool. If `True`, then the same dropout pattern is applied across all time steps per run call. If this parameter is set, `input_size` **must** be provided. input_size: (optional) (possibly nested tuple of) `TensorShape` objects containing the depth(s) of the input tensors expected to be passed in to the `DropoutWrapper`. Required and used **iff** `variational_recurrent = True` and `input_keep_prob < 1`. dtype: (optional) The `dtype` of the input, state, and output tensors. Required and used **iff** `variational_recurrent = True`. seed: (optional) integer, the randomness seed.
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