Train the model

training_loop.py

import time
import numpy as np
import math
import matplotlib.pyplot as plt

# Set up the number of iterations, printing and plotting options
n_iters = 1100000
print_every = 1000
plot_every = 1000

# Keep track of losses for plotting
current_loss = 0
all_losses = []

rnn = rnn.to(device)

def timeSince(since):
    now = time.time()
    s = now - since
    m = math.floor(s / 60)
    s -= m * 60
    return '%dm %ds' % (m, s)

# Shuffle indices
indices = np.random.permutation(len(sequences))

start = time.time()

# Run training procedure
for iter in range(1, n_iters + 1):
    
    # Pick index
    index = indices[iter % len(sequences)]
    
    # Run one training step
    output, loss = train(sequences[index], targets[index][0].long(), device)
    current_loss += loss

    # Print iter number and loss
    if iter % print_every == 0:
        guess, guess_i = wordFromOutput(output)
        print('%d %d%% (%s) Loss: %.4f' % (iter, iter / n_iters * 100, timeSince(start), loss))

    # Add current loss avg to list of losses
    if iter % plot_every == 0:
        all_losses.append(current_loss / plot_every)
        current_loss = 0
        
# Plot training loss
plt.figure()
plt.plot(all_losses)