def test_rnn(
rnn_type="LSTM",
input_size=500,
hidden_size=50,
sequence_length=10,
batch_size=8,
num_layers=2,
bidirectional=True,
lr=0.001,
):
rnn_model = getattr(nn, rnn_type)(input_size,
hidden_size,
num_layers,
bidirectional=bidirectional)
num_directions = 2 if bidirectional else 1
model = PyTorchWrapperRNN(rnn_model)
sgd = SGD(model.ops, lr)
# Inputs and expected ouput
X, Y = generate_data(sequence_length, batch_size, input_size, hidden_size,
num_directions)
# Test output shape
check_Y_shape(model, X, Y, sequence_length, batch_size, hidden_size,
num_directions)
# Test initializing hidden
initial_hidden = init_hidden(rnn_model, rnn_type, batch_size,
num_layers * num_directions, hidden_size)
# Test if sum of rnn output converges to with initial hidden
check_learns_zero_output_rnn(model, sgd, X, Y, initial_hidden)
def PyTorchBiLSTM(nO, nI, depth, dropout=0.2):
import torch.nn
from thinc.api import with_square_sequences
from thinc.extra.wrappers import PyTorchWrapperRNN
if depth == 0:
return layerize(noop())
model = torch.nn.LSTM(nI, nO // 2, depth, bidirectional=True, dropout=dropout)
return with_square_sequences(PyTorchWrapperRNN(model))
def PyTorchBiLSTM(nO, nI, depth, dropout=0.2):
if depth == 0:
return layerize(noop())
model = torch.nn.LSTM(nI,
nO // 2,
depth,
bidirectional=True,
dropout=dropout)
return with_square_sequences(PyTorchWrapperRNN(model))
def TorchBiLSTMEncoder(config):
import torch.nn
from thinc.extra.wrappers import PyTorchWrapperRNN
width = config["width"]
depth = config["depth"]
if depth == 0:
return layerize(noop())
return with_square_sequences(
PyTorchWrapperRNN(torch.nn.LSTM(width, width // 2, depth, bidirectional=True))
)
def PyTorchBiLSTM(nO, nI, depth):
model = torch.nn.LSTM(nI, nO // 2, depth, bidirectional=True)
return with_square_sequences(PyTorchWrapperRNN(model))
