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So far we've processed inputs as whole (ex. applying filters across the entire input to extract features) but we can also process our inputs sequentially. For example we can think of each token in our text as an event in time (timestep). We can process each timestep, one at a time, and predict the class after the last timestep (token) has been processed. This is very powerful because the model now has a meaningful way to account for the sequential order of tokens in our sequence and predict accordingly.
$$ \text{RNN forward pass for a single time step } X_t $$:
| \(N\) | batch size |
| \(E\) | embeddings dimension |
| \(H\) | # of hidden units |
| \(W_{hh}\) | RNN weights \(\in \mathbb{R}^{HXH}\) |
| \(h_{t-1}\) | previous timestep's hidden state \(\in in \mathbb{R}^{NXH}\) |
| \(W_{xh}\) | input weights \(\in \mathbb{R}^{EXH}\) |
| \(X_t\) | input at time step \(t \in \mathbb{R}^{NXE}\) |
| \(b_h\) | hidden units bias \(\in \mathbb{R}^{HX1}\) |
| \(h_t\) | output from RNN for timestep \(t\) |
Let's set our seed and device for our main task.
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5 | import numpy as np
import pandas as pd
import random
import torch
import torch.nn as nn
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1 | SEED = 1234
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7 | def set_seeds(seed=1234):
"""Set seeds for reproducibility."""
np.random.seed(seed)
random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed) # multi-GPU
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2 | # Set seeds for reproducibility
set_seeds(seed=SEED)
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8 | # Set device
cuda = True
device = torch.device("cuda" if (
torch.cuda.is_available() and cuda) else "cpu")
torch.set_default_tensor_type("torch.FloatTensor")
if device.type == "cuda":
torch.set_default_tensor_type("torch.cuda.FloatTensor")
print (device)
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We will download the AG News dataset, which consists of 120K text samples from 4 unique classes (Business, Sci/Tech, Sports, World)
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5 | # Load data
url = "https://raw.githubusercontent.com/GokuMohandas/Made-With-ML/main/datasets/news.csv"
df = pd.read_csv(url, header=0) # load
df = df.sample(frac=1).reset_index(drop=True) # shuffle
df.head()
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We're going to clean up our input data first by doing operations such as lower text, removing stop (filler) words, filters using regular expressions, etc.
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4 | import nltk
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
import re
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4 | nltk.download("stopwords")
STOPWORDS = stopwords.words("english")
print (STOPWORDS[:5])
porter = PorterStemmer()
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19 | def preprocess(text, stopwords=STOPWORDS):
"""Conditional preprocessing on our text unique to our task."""
# Lower
text = text.lower()
# Remove stopwords
pattern = re.compile(r"\b(" + r"|".join(stopwords) + r")\b\s*")
text = pattern.sub("", text)
# Remove words in parenthesis
text = re.sub(r"\([^)]*\)", "", text)
# Spacing and filters
text = re.sub(r"([-;;.,!?<=>])", r" \1 ", text)
text = re.sub("[^A-Za-z0-9]+", " ", text) # remove non alphanumeric chars
text = re.sub(" +", " ", text) # remove multiple spaces
text = text.strip()
return text
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3 | # Sample
text = "Great week for the NYSE!"
preprocess(text=text)
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4 | # Apply to dataframe
preprocessed_df = df.copy()
preprocessed_df.title = preprocessed_df.title.apply(preprocess)
print (f"{df.title.values[0]}\n\n{preprocessed_df.title.values[0]}")
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Warning
If you have preprocessing steps like standardization, etc. that are calculated, you need to separate the training and test set first before applying those operations. This is because we cannot apply any knowledge gained from the test set accidentally (data leak) during preprocessing/training. However for global preprocessing steps like the function above where we aren't learning anything from the data itself, we can perform before splitting the data.
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2 | import collections
from sklearn.model_selection import train_test_split
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3 | TRAIN_SIZE = 0.7
VAL_SIZE = 0.15
TEST_SIZE = 0.15
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5 | def train_val_test_split(X, y, train_size):
"""Split dataset into data splits."""
X_train, X_, y_train, y_ = train_test_split(X, y, train_size=TRAIN_SIZE, stratify=y)
X_val, X_test, y_val, y_test = train_test_split(X_, y_, train_size=0.5, stratify=y_)
return X_train, X_val, X_test, y_train, y_val, y_test
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3 | # Data
X = preprocessed_df["title"].values
y = preprocessed_df["category"].values
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7 | # Create data splits
X_train, X_val, X_test, y_train, y_val, y_test = train_val_test_split(
X=X, y=y, train_size=TRAIN_SIZE)
print (f"X_train: {X_train.shape}, y_train: {y_train.shape}")
print (f"X_val: {X_val.shape}, y_val: {y_val.shape}")
print (f"X_test: {X_test.shape}, y_test: {y_test.shape}")
print (f"Sample point: {X_train[0]} → {y_train[0]}")
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Next we'll define a LabelEncoder to encode our text labels into unique indices
1 | import itertools
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43 | class LabelEncoder(object):
"""Label encoder for tag labels."""
def __init__(self, class_to_index={}):
self.class_to_index = class_to_index or {} # mutable defaults ;)
self.index_to_class = {v: k for k, v in self.class_to_index.items()}
self.classes = list(self.class_to_index.keys())
def __len__(self):
return len(self.class_to_index)
def __str__(self):
return f"<LabelEncoder(num_classes={len(self)})>"
def fit(self, y):
classes = np.unique(y)
for i, class_ in enumerate(classes):
self.class_to_index[class_] = i
self.index_to_class = {v: k for k, v in self.class_to_index.items()}
self.classes = list(self.class_to_index.keys())
return self
def encode(self, y):
encoded = np.zeros((len(y)), dtype=int)
for i, item in enumerate(y):
encoded[i] = self.class_to_index[item]
return encoded
def decode(self, y):
classes = []
for i, item in enumerate(y):
classes.append(self.index_to_class[item])
return classes
def save(self, fp):
with open(fp, "w") as fp:
contents = {'class_to_index': self.class_to_index}
json.dump(contents, fp, indent=4, sort_keys=False)
@classmethod
def load(cls, fp):
with open(fp, "r") as fp:
kwargs = json.load(fp=fp)
return cls(**kwargs)
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5 | # Encode
label_encoder = LabelEncoder()
label_encoder.fit(y_train)
NUM_CLASSES = len(label_encoder)
label_encoder.class_to_index
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6 | # Convert labels to tokens
print (f"y_train[0]: {y_train[0]}")
y_train = label_encoder.encode(y_train)
y_val = label_encoder.encode(y_val)
y_test = label_encoder.encode(y_test)
print (f"y_train[0]: {y_train[0]}")
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4 | # Class weights
counts = np.bincount(y_train)
class_weights = {i: 1.0/count for i, count in enumerate(counts)}
print (f"counts: {counts}\nweights: {class_weights}")
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We'll define a Tokenizer to convert our text input data into token indices.
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3 | import json
from collections import Counter
from more_itertools import take
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68 | class Tokenizer(object):
def __init__(self, char_level, num_tokens=None,
pad_token="<PAD>", oov_token="<UNK>",
token_to_index=None):
self.char_level = char_level
self.separator = "" if self.char_level else " "
if num_tokens: num_tokens -= 2 # pad + unk tokens
self.num_tokens = num_tokens
self.pad_token = pad_token
self.oov_token = oov_token
if not token_to_index:
token_to_index = {pad_token: 0, oov_token: 1}
self.token_to_index = token_to_index
self.index_to_token = {v: k for k, v in self.token_to_index.items()}
def __len__(self):
return len(self.token_to_index)
def __str__(self):
return f"<Tokenizer(num_tokens={len(self)})>"
def fit_on_texts(self, texts):
if not self.char_level:
texts = [text.split(" ") for text in texts]
all_tokens = [token for text in texts for token in text]
counts = Counter(all_tokens).most_common(self.num_tokens)
self.min_token_freq = counts[-1][1]
for token, count in counts:
index = len(self)
self.token_to_index[token] = index
self.index_to_token[index] = token
return self
def texts_to_sequences(self, texts):
sequences = []
for text in texts:
if not self.char_level:
text = text.split(" ")
sequence = []
for token in text:
sequence.append(self.token_to_index.get(
token, self.token_to_index[self.oov_token]))
sequences.append(np.asarray(sequence))
return sequences
def sequences_to_texts(self, sequences):
texts = []
for sequence in sequences:
text = []
for index in sequence:
text.append(self.index_to_token.get(index, self.oov_token))
texts.append(self.separator.join([token for token in text]))
return texts
def save(self, fp):
with open(fp, "w") as fp:
contents = {
"char_level": self.char_level,
"oov_token": self.oov_token,
"token_to_index": self.token_to_index
}
json.dump(contents, fp, indent=4, sort_keys=False)
@classmethod
def load(cls, fp):
with open(fp, "r") as fp:
kwargs = json.load(fp=fp)
return cls(**kwargs)
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Warning
It's important that we only fit using our train data split because during inference, our model will not always know every token so it's important to replicate that scenario with our validation and test splits as well.
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5 | # Tokenize
tokenizer = Tokenizer(char_level=False, num_tokens=5000)
tokenizer.fit_on_texts(texts=X_train)
VOCAB_SIZE = len(tokenizer)
print (tokenizer)
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3 | # Sample of tokens
print (take(5, tokenizer.token_to_index.items()))
print (f"least freq token's freq: {tokenizer.min_token_freq}") # use this to adjust num_tokens
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8 | # Convert texts to sequences of indices
X_train = tokenizer.texts_to_sequences(X_train)
X_val = tokenizer.texts_to_sequences(X_val)
X_test = tokenizer.texts_to_sequences(X_test)
preprocessed_text = tokenizer.sequences_to_texts([X_train[0]])[0]
print ("Text to indices:\n"
f" (preprocessed) → {preprocessed_text}\n"
f" (tokenized) → {X_train[0]}")
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We'll need to do 2D padding to our tokenized text.
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7 | def pad_sequences(sequences, max_seq_len=0):
"""Pad sequences to max length in sequence."""
max_seq_len = max(max_seq_len, max(len(sequence) for sequence in sequences))
padded_sequences = np.zeros((len(sequences), max_seq_len))
for i, sequence in enumerate(sequences):
padded_sequences[i][:len(sequence)] = sequence
return padded_sequences
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4 | # 2D sequences
padded = pad_sequences(X_train[0:3])
print (padded.shape)
print (padded)
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We're going to create Datasets and DataLoaders to be able to efficiently create batches with our data splits.
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38 | class Dataset(torch.utils.data.Dataset):
def __init__(self, X, y):
self.X = X
self.y = y
def __len__(self):
return len(self.y)
def __str__(self):
return f"<Dataset(N={len(self)})>"
def __getitem__(self, index):
X = self.X[index]
y = self.y[index]
return [X, len(X), y]
def collate_fn(self, batch):
"""Processing on a batch."""
# Get inputs
batch = np.array(batch)
X = batch[:, 0]
seq_lens = batch[:, 1]
y = batch[:, 2]
# Pad inputs
X = pad_sequences(sequences=X)
# Cast
X = torch.LongTensor(X.astype(np.int32))
seq_lens = torch.LongTensor(seq_lens.astype(np.int32))
y = torch.LongTensor(y.astype(np.int32))
return X, seq_lens, y
def create_dataloader(self, batch_size, shuffle=False, drop_last=False):
return torch.utils.data.DataLoader(
dataset=self, batch_size=batch_size, collate_fn=self.collate_fn,
shuffle=shuffle, drop_last=drop_last, pin_memory=True)
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12 | # Create datasets
train_dataset = Dataset(X=X_train, y=y_train)
val_dataset = Dataset(X=X_val, y=y_val)
test_dataset = Dataset(X=X_test, y=y_test)
print ("Datasets:\n"
f" Train dataset:{train_dataset.__str__()}\n"
f" Val dataset: {val_dataset.__str__()}\n"
f" Test dataset: {test_dataset.__str__()}\n"
"Sample point:\n"
f" X: {train_dataset[0][0]}\n"
f" seq_len: {train_dataset[0][1]}\n"
f" y: {train_dataset[0][2]}")
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17 | # Create dataloaders
batch_size = 64
train_dataloader = train_dataset.create_dataloader(
batch_size=batch_size)
val_dataloader = val_dataset.create_dataloader(
batch_size=batch_size)
test_dataloader = test_dataset.create_dataloader(
batch_size=batch_size)
batch_X, batch_seq_lens, batch_y = next(iter(train_dataloader))
print ("Sample batch:\n"
f" X: {list(batch_X.size())}\n"
f" seq_lens: {list(batch_seq_lens.size())}\n"
f" y: {list(batch_y.size())}\n"
"Sample point:\n"
f" X: {batch_X[0]}\n"
f" seq_len: {batch_seq_lens[0]}\n"
f" y: {batch_y[0]}")
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Let's create the Trainer class that we'll use to facilitate training for our experiments.
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108 | class Trainer(object):
def __init__(self, model, device, loss_fn=None, optimizer=None, scheduler=None):
# Set params
self.model = model
self.device = device
self.loss_fn = loss_fn
self.optimizer = optimizer
self.scheduler = scheduler
def train_step(self, dataloader):
"""Train step."""
# Set model to train mode
self.model.train()
loss = 0.0
# Iterate over train batches
for i, batch in enumerate(dataloader):
# Step
batch = [item.to(self.device) for item in batch] # Set device
inputs, targets = batch[:-1], batch[-1]
self.optimizer.zero_grad() # Reset gradients
z = self.model(inputs) # Forward pass
J = self.loss_fn(z, targets) # Define loss
J.backward() # Backward pass
self.optimizer.step() # Update weights
# Cumulative Metrics
loss += (J.detach().item() - loss) / (i + 1)
return loss
def eval_step(self, dataloader):
"""Validation or test step."""
# Set model to eval mode
self.model.eval()
loss = 0.0
y_trues, y_probs = [], []
# Iterate over val batches
with torch.inference_mode():
for i, batch in enumerate(dataloader):
# Step
batch = [item.to(self.device) for item in batch] # Set device
inputs, y_true = batch[:-1], batch[-1]
z = self.model(inputs) # Forward pass
J = self.loss_fn(z, y_true).item()
# Cumulative Metrics
loss += (J - loss) / (i + 1)
# Store outputs
y_prob = F.softmax(z).cpu().numpy()
y_probs.extend(y_prob)
y_trues.extend(y_true.cpu().numpy())
return loss, np.vstack(y_trues), np.vstack(y_probs)
def predict_step(self, dataloader):
"""Prediction step."""
# Set model to eval mode
self.model.eval()
y_probs = []
# Iterate over val batches
with torch.inference_mode():
for i, batch in enumerate(dataloader):
# Forward pass w/ inputs
inputs, targets = batch[:-1], batch[-1]
z = self.model(inputs)
# Store outputs
y_prob = F.softmax(z).cpu().numpy()
y_probs.extend(y_prob)
return np.vstack(y_probs)
def train(self, num_epochs, patience, train_dataloader, val_dataloader):
best_val_loss = np.inf
for epoch in range(num_epochs):
# Steps
train_loss = self.train_step(dataloader=train_dataloader)
val_loss, _, _ = self.eval_step(dataloader=val_dataloader)
self.scheduler.step(val_loss)
# Early stopping
if val_loss < best_val_loss:
best_val_loss = val_loss
best_model = self.model
_patience = patience # reset _patience
else:
_patience -= 1
if not _patience: # 0
print("Stopping early!")
break
# Logging
print(
f"Epoch: {epoch+1} | "
f"train_loss: {train_loss:.5f}, "
f"val_loss: {val_loss:.5f}, "
f"lr: {self.optimizer.param_groups[0]['lr']:.2E}, "
f"_patience: {_patience}"
)
return best_model
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Inputs to RNNs are sequential like text or time-series.
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2 | BATCH_SIZE = 64
EMBEDDING_DIM = 100
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6 | # Input
sequence_size = 8 # words per input
x = torch.rand((BATCH_SIZE, sequence_size, EMBEDDING_DIM))
seq_lens = torch.randint(high=sequence_size, size=(BATCH_SIZE, ))
print (x.shape)
print (seq_lens.shape)
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$$ \text{RNN forward pass for a single time step } X_t $$:
| \(N\) | batch size |
| \(E\) | embeddings dimension |
| \(H\) | # of hidden units |
| \(W_{hh}\) | RNN weights \(\in \mathbb{R}^{HXH}\) |
| \(h_{t-1}\) | previous timestep's hidden state \(\in in \mathbb{R}^{NXH}\) |
| \(W_{xh}\) | input weights \(\in \mathbb{R}^{EXH}\) |
| \(X_t\) | input at time step \(t \in \mathbb{R}^{NXE}\) |
| \(b_h\) | hidden units bias \(\in \mathbb{R}^{HX1}\) |
| \(h_t\) | output from RNN for timestep \(t\) |
At the first time step, the previous hidden state \(h_{t-1}\) can either be a zero vector (unconditioned) or initialized (conditioned). If we are conditioning the RNN, the first hidden state \(h_0\) can belong to a specific condition or we can concat the specific condition to the randomly initialized hidden vectors at each time step. More on this in the subsequent notebooks on RNNs.
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2 | RNN_HIDDEN_DIM = 128
DROPOUT_P = 0.1
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3 | # Initialize hidden state
hidden_t = torch.zeros((BATCH_SIZE, RNN_HIDDEN_DIM))
print (hidden_t.size())
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We'll show how to create an RNN cell using PyTorch's RNNCell and the more abstracted RNN.
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3 | # Initialize RNN cell
rnn_cell = nn.RNNCell(EMBEDDING_DIM, RNN_HIDDEN_DIM)
print (rnn_cell)
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11 | # Forward pass through RNN
x = x.permute(1, 0, 2) # RNN needs batch_size to be at dim 1
# Loop through the inputs time steps
hiddens = []
for t in range(sequence_size):
hidden_t = rnn_cell(x[t], hidden_t)
hiddens.append(hidden_t)
hiddens = torch.stack(hiddens)
hiddens = hiddens.permute(1, 0, 2) # bring batch_size back to dim 0
print (hiddens.size())
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6 | # We also could've used a more abstracted layer
x = torch.rand((BATCH_SIZE, sequence_size, EMBEDDING_DIM))
rnn = nn.RNN(EMBEDDING_DIM, RNN_HIDDEN_DIM, batch_first=True)
out, h_n = rnn(x) # h_n is the last hidden state
print ("out: ", out.shape)
print ("h_n: ", h_n.shape)
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3 | # The same tensors
print (out[:,-1,:])
print (h_n.squeeze(0))
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In our model, we want to use the RNN's output after the last relevant token in the sentence is processed. The last relevant token doesn't refer the <PAD> tokens but to the last actual word in the sentence and its index is different for each input in the batch. This is why we included a seq_lens tensor in our batches.
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8 | def gather_last_relevant_hidden(hiddens, seq_lens):
"""Extract and collect the last relevant
hidden state based on the sequence length."""
seq_lens = seq_lens.long().detach().cpu().numpy() - 1
out = []
for batch_index, column_index in enumerate(seq_lens):
out.append(hiddens[batch_index, column_index])
return torch.stack(out)
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1
2 | # Get the last relevant hidden state
gather_last_relevant_hidden(hiddens=out, seq_lens=seq_lens).squeeze(0).shape
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There are many different ways to use RNNs. So far we've processed our inputs one timestep at a time and we could either use the RNN's output at each time step or just use the final input timestep's RNN output. Let's look at a few other possibilities.
1 | import torch.nn.functional as F
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1 | HIDDEN_DIM = 100
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32 | class RNN(nn.Module):
def __init__(self, embedding_dim, vocab_size, rnn_hidden_dim,
hidden_dim, dropout_p, num_classes, padding_idx=0):
super(RNN, self).__init__()
# Initialize embeddings
self.embeddings = nn.Embedding(
embedding_dim=embedding_dim, num_embeddings=vocab_size,
padding_idx=padding_idx)
# RNN
self.rnn = nn.RNN(embedding_dim, rnn_hidden_dim, batch_first=True)
# FC weights
self.dropout = nn.Dropout(dropout_p)
self.fc1 = nn.Linear(rnn_hidden_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, num_classes)
def forward(self, inputs):
# Embed
x_in, seq_lens = inputs
x_in = self.embeddings(x_in)
# Rnn outputs
out, h_n = self.rnn(x_in)
z = gather_last_relevant_hidden(hiddens=out, seq_lens=seq_lens)
# FC layers
z = self.fc1(z)
z = self.dropout(z)
z = self.fc2(z)
return z
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7 | # Simple RNN cell
model = RNN(
embedding_dim=EMBEDDING_DIM, vocab_size=VOCAB_SIZE,
rnn_hidden_dim=RNN_HIDDEN_DIM, hidden_dim=HIDDEN_DIM,
dropout_p=DROPOUT_P, num_classes=NUM_CLASSES)
model = model.to(device) # set device
print (model.named_parameters)
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1 | from torch.optim import Adam
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4 | NUM_LAYERS = 1
LEARNING_RATE = 1e-4
PATIENCE = 10
NUM_EPOCHS = 50
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1
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3 | # Define Loss
class_weights_tensor = torch.Tensor(list(class_weights.values())).to(device)
loss_fn = nn.CrossEntropyLoss(weight=class_weights_tensor)
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4 | # Define optimizer & scheduler
optimizer = Adam(model.parameters(), lr=LEARNING_RATE)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, mode="min", factor=0.1, patience=3)
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4 | # Trainer module
trainer = Trainer(
model=model, device=device, loss_fn=loss_fn,
optimizer=optimizer, scheduler=scheduler)
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1
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3 | # Train
best_model = trainer.train(
NUM_EPOCHS, PATIENCE, train_dataloader, val_dataloader)
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2 | import json
from sklearn.metrics import precision_recall_fscore_support
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23 | def get_metrics(y_true, y_pred, classes):
"""Per-class performance metrics."""
# Performance
performance = {"overall": {}, "class": {}}
# Overall performance
metrics = precision_recall_fscore_support(y_true, y_pred, average="weighted")
performance["overall"]["precision"] = metrics[0]
performance["overall"]["recall"] = metrics[1]
performance["overall"]["f1"] = metrics[2]
performance["overall"]["num_samples"] = np.float64(len(y_true))
# Per-class performance
metrics = precision_recall_fscore_support(y_true, y_pred, average=None)
for i in range(len(classes)):
performance["class"][classes[i]] = {
"precision": metrics[0][i],
"recall": metrics[1][i],
"f1": metrics[2][i],
"num_samples": np.float64(metrics[3][i]),
}
return performance
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1
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3 | # Get predictions
test_loss, y_true, y_prob = trainer.eval_step(dataloader=test_dataloader)
y_pred = np.argmax(y_prob, axis=1)
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4 | # Determine performance
performance = get_metrics(
y_true=y_test, y_pred=y_pred, classes=label_encoder.classes)
print (json.dumps(performance["overall"], indent=2))
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While our simple RNNs so far are great for sequentially processing our inputs, they have quite a few disadvantages. They commonly suffer from exploding or vanishing gradients as a result using the same set of weights (\(W_{xh}\) and \(W_{hh}\)) with each timestep's input. During backpropagation, this can cause gradients to explode (>1) or vanish (<1). If you multiply any number greater than 1 with itself over and over, it moves towards infinity (exploding gradients) and similarly, If you multiply any number less than 1 with itself over and over, it moves towards zero (vanishing gradients). To mitigate this issue, gated RNNs were devised to selectively retain information. If you're interested in learning more of the specifics, this post is a must-read.
There are two popular types of gated RNNs: Long Short-term Memory (LSTMs) units and Gated Recurrent Units (GRUs).
When deciding between LSTMs and GRUs, empirical performance is the best factor but in general GRUs offer similar performance with less complexity (less weights).
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4 | # Input
sequence_size = 8 # words per input
x = torch.rand((BATCH_SIZE, sequence_size, EMBEDDING_DIM))
print (x.shape)
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2 | # GRU
gru = nn.GRU(input_size=EMBEDDING_DIM, hidden_size=RNN_HIDDEN_DIM, batch_first=True)
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4 | # Forward pass
out, h_n = gru(x)
print (f"out: {out.shape}")
print (f"h_n: {h_n.shape}")
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We can also have RNNs that process inputs from both directions (first token to last token and vice versa) and combine their outputs. This architecture is known as a bidirectional RNN.
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3 | # GRU
gru = nn.GRU(input_size=EMBEDDING_DIM, hidden_size=RNN_HIDDEN_DIM,
batch_first=True, bidirectional=True)
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4 | # Forward pass
out, h_n = gru(x)
print (f"out: {out.shape}")
print (f"h_n: {h_n.shape}")
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Notice that the output for each sample at each timestamp has size 256 (double the RNN_HIDDEN_DIM). This is because this includes both the forward and backward directions from the BiRNN.
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33 | class GRU(nn.Module):
def __init__(self, embedding_dim, vocab_size, rnn_hidden_dim,
hidden_dim, dropout_p, num_classes, padding_idx=0):
super(GRU, self).__init__()
# Initialize embeddings
self.embeddings = nn.Embedding(embedding_dim=embedding_dim,
num_embeddings=vocab_size,
padding_idx=padding_idx)
# RNN
self.rnn = nn.GRU(embedding_dim, rnn_hidden_dim,
batch_first=True, bidirectional=True)
# FC weights
self.dropout = nn.Dropout(dropout_p)
self.fc1 = nn.Linear(rnn_hidden_dim*2, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, num_classes)
def forward(self, inputs:
# Embed
x_in, seq_lens = inputs
x_in = self.embeddings(x_in)
# Rnn outputs
out, h_n = self.rnn(x_in)
z = gather_last_relevant_hidden(hiddens=out, seq_lens=seq_lens)
# FC layers
z = self.fc1(z)
z = self.dropout(z)
z = self.fc2(z)
return z
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7 | # Simple RNN cell
model = GRU(
embedding_dim=EMBEDDING_DIM, vocab_size=VOCAB_SIZE,
rnn_hidden_dim=RNN_HIDDEN_DIM, hidden_dim=HIDDEN_DIM,
dropout_p=DROPOUT_P, num_classes=NUM_CLASSES)
model = model.to(device) # set device
print (model.named_parameters)
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1
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3 | # Define Loss
class_weights_tensor = torch.Tensor(list(class_weights.values())).to(device)
loss_fn = nn.CrossEntropyLoss(weight=class_weights_tensor)
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2
3
4 | # Define optimizer & scheduler
optimizer = Adam(model.parameters(), lr=LEARNING_RATE)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, mode="min", factor=0.1, patience=3)
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3
4 | # Trainer module
trainer = Trainer(
model=model, device=device, loss_fn=loss_fn,
optimizer=optimizer, scheduler=scheduler)
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1
2
3 | # Train
best_model = trainer.train(
NUM_EPOCHS, PATIENCE, train_dataloader, val_dataloader)
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1 | from pathlib import Path
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2
3 | # Get predictions
test_loss, y_true, y_prob = trainer.eval_step(dataloader=test_dataloader)
y_pred = np.argmax(y_prob, axis=1)
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4 | # Determine performance
performance = get_metrics(
y_true=y_test, y_pred=y_pred, classes=label_encoder.classes)
print (json.dumps(performance["overall"], indent=2))
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8 | # Save artifacts
dir = Path("gru")
dir.mkdir(parents=True, exist_ok=True)
label_encoder.save(fp=Path(dir, "label_encoder.json"))
tokenizer.save(fp=Path(dir, 'tokenizer.json'))
torch.save(best_model.state_dict(), Path(dir, "model.pt"))
with open(Path(dir, 'performance.json'), "w") as fp:
json.dump(performance, indent=2, sort_keys=False, fp=fp)
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8 | def get_probability_distribution(y_prob, classes):
"""Create a dict of class probabilities from an array."""
results = {}
for i, class_ in enumerate(classes):
results[class_] = np.float64(y_prob[i])
sorted_results = {k: v for k, v in sorted(
results.items(), key=lambda item: item[1], reverse=True)}
return sorted_results
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10 | # Load artifacts
device = torch.device("cpu")
label_encoder = LabelEncoder.load(fp=Path(dir, "label_encoder.json"))
tokenizer = Tokenizer.load(fp=Path(dir, 'tokenizer.json'))
model = GRU(
embedding_dim=EMBEDDING_DIM, vocab_size=VOCAB_SIZE,
rnn_hidden_dim=RNN_HIDDEN_DIM, hidden_dim=HIDDEN_DIM,
dropout_p=DROPOUT_P, num_classes=NUM_CLASSES)
model.load_state_dict(torch.load(Path(dir, "model.pt"), map_location=device))
model.to(device)
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1
2 | # Initialize trainer
trainer = Trainer(model=model, device=device)
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7 | # Dataloader
text = "The final tennis tournament starts next week."
X = tokenizer.texts_to_sequences([preprocess(text)])
print (tokenizer.sequences_to_texts(X))
y_filler = label_encoder.encode([label_encoder.classes[0]]*len(X))
dataset = Dataset(X=X, y=y_filler)
dataloader = dataset.create_dataloader(batch_size=batch_size)
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4 | # Inference
y_prob = trainer.predict_step(dataloader)
y_pred = np.argmax(y_prob, axis=1)
label_encoder.decode(y_pred)
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3 | # Class distributions
prob_dist = get_probability_distribution(y_prob=y_prob[0], classes=label_encoder.classes)
print (json.dumps(prob_dist, indent=2))
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We will learn how to create more context-aware representations and a little bit of interpretability with RNNs in the next lesson on attention.
To cite this content, please use:
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6 | @article{madewithml,
author = {Goku Mohandas},
title = { RNNs - Made With ML },
howpublished = {\url{https://madewithml.com/}},
year = {2023}
}
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