285 lines
12 KiB
Python
285 lines
12 KiB
Python
import math
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import unicodedata
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from time import sleep
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from torch.utils.data import Dataset, DataLoader
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import matplotlib.pyplot as plt
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import re
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import random
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import os
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from tqdm import tqdm
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DATA_FILE = 'en_US.txt'
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EPOCHS = 4000
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TEACHER_FORCING_PROBABILITY = 0.4
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LEARNING_RATE = 0.01
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BATCH_SIZE = 1024
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plt.ion()
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if not os.path.isfile(DATA_FILE):
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import requests
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open(DATA_FILE, 'wb').write(
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requests.get('https://raw.githubusercontent.com/open-dict-data/ipa-dict/master/data/' + DATA_FILE).content)
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DEVICE = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
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OUT_LOOKUP = ['', 'b', 'a', 'ʊ', 't', 'k', 'ə', 'z', 'ɔ', 'ɹ', 's', 'j', 'u', 'm', 'f', 'ɪ', 'o', 'ɡ', 'ɛ', 'n',
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'e', 'd',
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'ɫ', 'w', 'i', 'p', 'ɑ', 'ɝ', 'θ', 'v', 'h', 'æ', 'ŋ', 'ʃ', 'ʒ', 'ð', '^', '$']
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IN_LOOKUP = ['', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't',
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'u', 'v', 'w', 'x', 'y', 'z', '$', '^']
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IN_ALPHABET = {letter: idx for idx, letter in enumerate(IN_LOOKUP)}
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OUT_ALPHABET = {letter: idx for idx, letter in enumerate(OUT_LOOKUP)}
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TOTAL_OUT_LEN = 0
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DATA: [(torch.tensor, torch.tensor)] = []
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with open(DATA_FILE) as f:
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for line in f:
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text, phonemes = line.split("\t")
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phonemes = phonemes.strip().split(",")[0]
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phonemes = '^' + re.sub(r'[/\'ˈˌ]', '', phonemes) + '$'
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text = '^' + re.sub(r'[^a-z]', '', text.strip()) + '$'
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text = torch.tensor([IN_ALPHABET[letter] for letter in text], dtype=torch.int)
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phonemes = torch.tensor([OUT_ALPHABET[letter] for letter in phonemes], dtype=torch.int)
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DATA.append((text, phonemes))
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random.shuffle(DATA)
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# DATA = DATA[:2000]
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print("Number of samples ", len(DATA))
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TRAINING_SET_SIZE = int(len(DATA) * 0.5)
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TRAINING_SET_SIZE -= TRAINING_SET_SIZE % BATCH_SIZE
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EVAL = DATA[TRAINING_SET_SIZE:]
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DATA = DATA[:TRAINING_SET_SIZE]
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assert len(DATA) % BATCH_SIZE == 0
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print("Training samples ", len(DATA))
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print("Evaluation samples ", len(EVAL))
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print("Input alphabet ", IN_LOOKUP)
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print("Output alphabet ", OUT_LOOKUP)
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TOTAL_TRAINING_OUT_LEN = 0
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TOTAL_EVALUATION_OUT_LEN = 0
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for text, phonemes in DATA:
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TOTAL_TRAINING_OUT_LEN += len(phonemes)
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for text, phonemes in EVAL:
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TOTAL_EVALUATION_OUT_LEN += len(phonemes)
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TOTAL_EVALUATION_OUT_LEN -= len(EVAL) # do not count the beginning of line ^ character
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TOTAL_TRAINING_OUT_LEN -= len(DATA)
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print("Total output length in training set", TOTAL_TRAINING_OUT_LEN)
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print("Total output length in evaluation set", TOTAL_EVALUATION_OUT_LEN)
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def shuffle_but_keep_sorted_by_output_lengths(data: [(torch.tensor, torch.tensor)]):
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random.shuffle(data)
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data.sort(reverse=True, key=lambda x: len(x[1])) # sort with respect to output lengths
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def collate(batch: [(torch.tensor, torch.tensor)]):
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batch.sort(reverse=True, key=lambda x: len(x[0])) # sort with respect to input lengths
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in_lengths = [len(entry[0]) for entry in batch]
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max_in_len = max(in_lengths)
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out_lengths = [len(entry[1]) for entry in batch]
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max_out_len = max(out_lengths)
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padded_in = torch.zeros((len(batch), max_in_len), dtype=torch.int)
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padded_out = torch.zeros((len(batch), max_out_len), dtype=torch.long)
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for i in range(0, len(batch)):
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padded_in[i, :len(batch[i][0])] = batch[i][0]
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padded_out[i, :len(batch[i][1])] = batch[i][1]
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return padded_in, in_lengths, padded_out, out_lengths
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class EncoderRNN(nn.Module):
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def __init__(self, hidden_size, layers):
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super(EncoderRNN, self).__init__()
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self.hidden_size = hidden_size
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self.hidden_layers = layers
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self.embedding = nn.Embedding(num_embeddings=len(IN_ALPHABET),
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embedding_dim=hidden_size,
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padding_idx=IN_ALPHABET[''])
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self.gru = nn.LSTM(input_size=hidden_size,
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hidden_size=hidden_size,
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num_layers=self.hidden_layers,
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batch_first=True)
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# self.lin = nn.Linear(hidden_size, hidden_size)
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def forward(self, padded_in, in_lengths):
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batch_size = len(in_lengths)
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# hidden_state, cell_state = hidden
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# assert hidden_state.size() == (self.hidden_layers, batch_size, self.hidden_size)
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# assert cell_state.size() == (self.hidden_layers, batch_size, self.hidden_size)
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embedded = self.embedding(padded_in)
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assert embedded.size() == (batch_size, max(in_lengths), self.hidden_size)
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packed = torch.nn.utils.rnn.pack_padded_sequence(embedded, in_lengths, batch_first=True)
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gru_out, hidden = self.gru(packed)
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unpacked, _ = torch.nn.utils.rnn.pad_packed_sequence(gru_out, batch_first=True)
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assert unpacked.size() == (batch_size, max(in_lengths), self.hidden_size)
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# assert hidden.size() == (self.hidden_layers, batch_size, self.hidden_size)
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# h, cell_state = hidden
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# final_hidden = self.lin(h)
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return unpacked, hidden
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def init_hidden(self, batch_size, device):
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hidden_state = torch.zeros(self.hidden_layers, batch_size, self.hidden_size, device=device)
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cell_state = torch.zeros(self.hidden_layers, batch_size, self.hidden_size, device=device)
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return hidden_state, cell_state
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class DecoderRNN(nn.Module):
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def __init__(self, hidden_size, layers):
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super(DecoderRNN, self).__init__()
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self.hidden_size = hidden_size
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self.hidden_layers = layers
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self.embedding = nn.Embedding(num_embeddings=len(OUT_ALPHABET),
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embedding_dim=hidden_size,
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padding_idx=OUT_ALPHABET[''])
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self.gru = nn.LSTM(input_size=hidden_size,
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hidden_size=hidden_size,
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num_layers=self.hidden_layers,
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batch_first=True)
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self.out = nn.Linear(hidden_size, len(OUT_ALPHABET))
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self.softmax = nn.LogSoftmax(dim=2)
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def forward(self, padded_out, hidden):
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batch_size = len(padded_out)
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padded_out = padded_out.unsqueeze(1)
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seq_length = 1
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hidden_state, cell_state = hidden
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assert hidden_state.size() == (self.hidden_layers, batch_size, self.hidden_size)
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assert cell_state.size() == (self.hidden_layers, batch_size, self.hidden_size)
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embedded = self.embedding(padded_out)
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assert embedded.size() == (batch_size, seq_length, self.hidden_size)
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gru_out, hidden = self.gru(embedded, hidden)
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# assert hidden.size() == (self.hidden_layers, batch_size, self.hidden_size)
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assert gru_out.size() == (batch_size, seq_length, self.hidden_size)
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lin = self.out(gru_out)
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assert lin.size() == (batch_size, seq_length, len(OUT_ALPHABET))
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probabilities = self.softmax(lin)
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assert probabilities.size() == (batch_size, seq_length, len(OUT_ALPHABET))
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return probabilities, hidden
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def run(encoder, decoder, batch_in, i_lengths, batch_out, o_lengths, teacher_forcing_prob, criterion):
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batch_in = batch_in.to(DEVICE)
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batch_out = batch_out.to(DEVICE)
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out_seq_len = batch_out.size()[1]
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in_seq_len = batch_in.size()[1]
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assert batch_in.size() == (BATCH_SIZE, in_seq_len)
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assert batch_out.size() == (BATCH_SIZE, out_seq_len)
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loss = 0
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total_correct_predictions = 0
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encoder_output, hidden = encoder(batch_in, i_lengths)
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output = batch_out[:, 0]
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for i in range(out_seq_len - 1):
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if random.random() < teacher_forcing_prob:
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out = batch_out[:, i]
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else:
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out = output
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output, hidden = decoder(out, hidden)
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output = output.squeeze(1)
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expected_output = batch_out[:, i + 1]
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if criterion is not None:
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loss += criterion(output, expected_output)
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argmax_output = torch.argmax(output, 1)
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with torch.no_grad():
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total_correct_predictions += (argmax_output == expected_output).sum().item()
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output = argmax_output
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return loss, total_correct_predictions
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eval_bar = tqdm(total=len(EVAL), position=2)
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def eval_model(encoder, decoder):
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eval_bar.reset()
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eval_bar.set_description("Evaluation")
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with torch.no_grad():
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total_correct_predictions = 0
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for batch_in, i_lengths, batch_out, o_lengths in DataLoader(dataset=EVAL, drop_last=True,
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batch_size=BATCH_SIZE,
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collate_fn=collate):
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loss, correct_predictions = run(encoder=encoder,
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decoder=decoder,
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criterion=None,
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i_lengths=i_lengths,
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o_lengths=o_lengths,
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batch_in=batch_in,
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batch_out=batch_out,
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teacher_forcing_prob=0)
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total_correct_predictions += correct_predictions
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eval_bar.update(BATCH_SIZE)
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return total_correct_predictions
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outer_bar = tqdm(total=EPOCHS, position=0)
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inner_bar = tqdm(total=len(DATA), position=1)
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def train_model(encoder, decoder):
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encoder_optimizer = optim.Adam(filter(lambda x: x.requires_grad, encoder.parameters()),
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lr=LEARNING_RATE)
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decoder_optimizer = optim.Adam(filter(lambda x: x.requires_grad, decoder.parameters()),
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lr=LEARNING_RATE)
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criterion = nn.NLLLoss(ignore_index=OUT_ALPHABET[''])
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train_snapshots_percentage = [0]
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train_snapshots = [0]
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eval_snapshots = [eval_model(encoder, decoder)]
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eval_snapshots_percentage = [eval_snapshots[0] / TOTAL_EVALUATION_OUT_LEN]
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outer_bar.reset()
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outer_bar.set_description("Epochs")
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for epoch in range(EPOCHS):
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shuffle_but_keep_sorted_by_output_lengths(DATA)
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total_loss = 0
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total_correct_predictions = 0
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inner_bar.reset()
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for batch_in, i_lengths, batch_out, o_lengths in DataLoader(dataset=DATA, drop_last=True,
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batch_size=BATCH_SIZE,
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collate_fn=collate):
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encoder_optimizer.zero_grad()
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decoder_optimizer.zero_grad()
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loss, correct_predictions = run(encoder=encoder,
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decoder=decoder,
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criterion=criterion,
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i_lengths=i_lengths,
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o_lengths=o_lengths,
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batch_in=batch_in,
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batch_out=batch_out,
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teacher_forcing_prob=TEACHER_FORCING_PROBABILITY)
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total_correct_predictions += correct_predictions
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loss.backward()
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encoder_optimizer.step()
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decoder_optimizer.step()
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inner_bar.update(BATCH_SIZE)
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loss_scalar = loss.item()
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total_loss += loss_scalar
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inner_bar.set_description("Avg loss %.2f" % (loss_scalar / batch_out.size()[1]))
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train_snapshots.append(total_correct_predictions)
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train_snapshots_percentage.append(total_correct_predictions / TOTAL_TRAINING_OUT_LEN)
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eval_snapshots.append(eval_model(encoder, decoder))
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eval_snapshots_percentage.append(eval_snapshots[-1] / TOTAL_EVALUATION_OUT_LEN)
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plt.clf()
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plt.plot(train_snapshots_percentage, label="Training %")
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plt.plot(eval_snapshots_percentage, label="Evaluation %")
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plt.legend(loc="upper left")
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plt.pause(interval=0.01)
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# print()
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# print("Total epoch loss:", total_loss)
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# print("Total epoch avg loss:", total_loss / TOTAL_TRAINING_OUT_LEN)
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# print("Training snapshots:", train_snapshots)
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# print("Training snapshots(%):", train_snapshots_percentage)
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# print("Evaluation snapshots:", eval_snapshots)
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# print("Evaluation snapshots(%):", eval_snapshots_percentage)
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outer_bar.set_description("Epochs")
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outer_bar.update(1)
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train_model(EncoderRNN(32, 4).to(DEVICE), DecoderRNN(32, 4).to(DEVICE))
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