tf.compat.v1.nn.sparse_softmax_cross_entropy_with

Creates a bidirectional recurrent neural network. (deprecated)

tf.compat.v1.nn.static_bidirectional_rnn(
    cell_fw, cell_bw, inputs, initial_state_fw=None, initial_state_bw=None,
    dtype=None, sequence_length=None, scope=None
)

Similar to the unidirectional case above (rnn) but takes input and builds independent forward and backward RNNs with the final forward and backward outputs depth-concatenated, such that the output will have the format [time][batch][cell_fw.output_size + cell_bw.output_size]. The input_size of forward and backward cell must match. The initial state for both directions is zero by default (but can be set optionally) and no intermediate states are ever returned -- the network is fully unrolled for the given (passed in) length(s) of the sequence(s) or completely unrolled if length(s) is not given.

Args
`cell_fw`	An instance of RNNCell, to be used for forward direction.
`cell_bw`	An instance of RNNCell, to be used for backward direction.
`inputs`	A length T list of inputs, each a tensor of shape [batch_size, input_size], or a nested tuple of such elements.
`initial_state_fw`	(optional) An initial state for the forward RNN. This must be a tensor of appropriate type and shape `[batch_size, cell_fw.state_size]`. If `cell_fw.state_size` is a tuple, this should be a tuple of tensors having shapes `[batch_size, s] for s in cell_fw.state_size`.
`initial_state_bw`	(optional) Same as for `initial_state_fw`, but using the corresponding properties of `cell_bw`.
`dtype`	(optional) The data type for the initial state. Required if either of the initial states are not provided.
`sequence_length`	(optional) An int32/int64 vector, size `[batch_size]`, containing the actual lengths for each of the sequences.
`scope`	VariableScope for the created subgraph; defaults to "bidirectional_rnn"

Returns
A tuple (outputs, output_state_fw, output_state_bw) where: outputs is a length `T` list of outputs (one for each input), which are depth-concatenated forward and backward outputs. output_state_fw is the final state of the forward rnn. output_state_bw is the final state of the backward rnn.

Raises
`TypeError`	If `cell_fw` or `cell_bw` is not an instance of `RNNCell`.
`ValueError`	If inputs is None or an empty list.

TensorFlow

tf.compat / v1 / v1.nn.sparse_softmax_cross_entropy_with_logits

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tf.compat / v1 / v1.nn.sparse_softmax_cross_entropy_with_logits

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