def standard_nextstep_inference(cell, inputs, output_size, scope=None,
return_states=False):
"""Gets the forward pass of a standard model for next step prediction.
Args:
cell (tf.nn.rnn_cell.RNNCell): the cell to use at each step. For
multiple layers, wrap them up in tf.nn.rnn_cell.MultiRNNCell.
inputs: [sequence_length, batch_size, num_features] float input tensor.
If the inputs are discrete, we expect this to already be embedded.
output_size (int): the size we project the output to (ie. the number of
symbols in the vocabulary).
scope (Optional): variable scope, defaults to "rnn".
return_states (Optional[bool]): whether or not to return the states
as well as the projected logits.
Returns:
tuple: (initial_state, logits, final_state)
- *initial_state* are float tensors representing the first state of
the rnn, to pass states across batches.
- *logits* is a list of [batch_size, vocab_size] float tensors
representing the output of the network at each timestep.
- *final_state* are float tensors containing the final state of
the network, evaluate these to get a state to feed into
initial_state next batch.
"""
with tf.variable_scope(scope or "rnn") as scope:
inputs = tf.unpack(inputs)
batch_size = inputs[0].get_shape()[0].value
initial_state = cell.zero_state(batch_size, tf.float32)
states, final_state = tf.nn.rnn(
cell, inputs, initial_state=initial_state, dtype=tf.float32,
scope=scope)
# we want to project them all at once, because it's faster
logits = tf.concat(0, states)
with tf.variable_scope('output_layer'):
# output layer needs weights
weights = tf.get_variable('weights',
[cell.output_size, output_size])
biases = tf.get_variable('bias',
[output_size])
logits = tf.nn.bias_add(tf.matmul(logits, weights), biases)
# and split them back up to what we expect
logits = tf.split(0, len(inputs), logits)
if return_states:
logits = (states, logits)
return [initial_state], logits, [final_state]
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