def train_rnn(file, batch_size, layers, learning_rate, dropout, num_steps,
cell_size, epochs, cell, test_seed, delim, save):
""" Train neural network """
model_name = "cell-{}-size-{}-batch-{}-steps-{}-layers-{}-lr-{}-dropout-{}".format(
cell, cell_size, batch_size, num_steps, layers, learning_rate, dropout)
ds = Dataset(file,
batch_size=batch_size,
num_steps=num_steps,
with_delim=delim)
n = RNN(data=ds,
cell=cell,
num_layers=layers,
dropout=dropout,
learning_rate=learning_rate,
cell_size=cell_size,
num_epochs=epochs)
n.train(save=save,
model_name=model_name,
test_output=True,
test_seed=test_seed,
with_delim=delim)
if save:
n.save(model_name)
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import dataset
from network import RNN
dataset = dataset.Dataset()
rnn = RNN(dataset.n_letters, 128, dataset.n_letters, dataset.n_categories)
criterion = nn.NLLLoss()
learning_rate = 0.0005
rnn.train()
def timeSince(since):
now = time.time()
s = now - since
m = math.floor(s / 60)
s -= m * 60
return '%dm %ds' % (m, s)
def savepoint(iter, total_loss):
torch.save(
{
'epoch': iter,
'nn_state_dict': rnn.state_dict(),
