text stringlengths 1 93.6k |
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return float(num_correct) / len(gold_seq)
|
def get_utterances(item, history_size=1):
|
""" Gets all of the relevant utterances for an example.
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Input:
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item (Utterance): The example.
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history_size (int, optional): The number of utterances to include.
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Returns:
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list of list of str, representing all of the sequences.
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"""
|
utterances = item.histories(history_size - 1)
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utterances.append(item.input_sequence())
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return utterances
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def forward_one_multilayer(lstm_input, layer_states, dropout_amount=0.):
|
""" Goes forward for one multilayer RNN cell step.
|
Inputs:
|
lstm_input (dy.Expression): Some input to the step.
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layer_states (list of dy.RNNState): The states of each layer in the cell.
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dropout_amount (float, optional): The amount of dropout to apply, in
|
between the layers.
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Returns:
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(list of dy.Expression, list of dy.Expression), dy.Expression, (list of dy.RNNSTate),
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representing (each layer's cell memory, each layer's cell hidden state),
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the final hidden state, and (each layer's updated RNNState).
|
"""
|
num_layers = len(layer_states)
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new_states = []
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cell_states = []
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hidden_states = []
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state = lstm_input
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for i in range(num_layers):
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new_states.append(layer_states[i].add_input(state))
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layer_c, layer_h = new_states[i].s()
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state = layer_h
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if i < num_layers - 1:
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state = dy.dropout(state, dropout_amount)
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cell_states.append(layer_c)
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hidden_states.append(layer_h)
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return (cell_states, hidden_states), state, new_states
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def encode_sequence(sequence, rnns, embedder, dropout_amount=0.):
|
""" Encodes a sequence given RNN cells and an embedding function.
|
Inputs:
|
seq (list of str): The sequence to encode.
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rnns (list of dy._RNNBuilder): The RNNs to use.
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emb_fn (dict str->dy.Expression): Function that embeds strings to
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word vectors.
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size (int): The size of the RNN.
|
dropout_amount (float, optional): The amount of dropout to apply.
|
Returns:
|
(list of dy.Expression, list of dy.Expression), list of dy.Expression,
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where the first pair is the (final cell memories, final cell states) of
|
all layers, and the second list is a list of the final layer's cell
|
state for all tokens in the sequence.
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"""
|
layer_states = []
|
for rnn in rnns:
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hidden_size = rnn.spec[2]
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layer_states.append(rnn.initial_state([dy.zeroes((hidden_size, 1)),
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dy.zeroes((hidden_size, 1))]))
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outputs = []
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for token in sequence:
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rnn_input = embedder(token)
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(cell_states, hidden_states), output, layer_states = \
|
forward_one_multilayer(rnn_input,
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layer_states,
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dropout_amount)
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outputs.append(output)
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return (cell_states, hidden_states), outputs
|
def create_multilayer_lstm_params(num_layers,
|
in_size,
|
state_size,
|
model,
|
name=""):
|
""" Adds a multilayer LSTM to the model parameters.
|
Inputs:
|
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