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ML/Pytorch/Basics/pytorch_bidirectional_lstm.py

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+"""
+Example code of a simple bidirectional LSTM on the MNIST dataset.
+
+Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
+*    2020-05-09 Initial coding
+
+"""
+
+
+# Imports
+import torch
+import torchvision
+import torch.nn as nn  # All neural network modules, nn.Linear, nn.Conv2d, BatchNorm, Loss functions
+import torch.optim as optim  # For all Optimization algorithms, SGD, Adam, etc.
+import torch.nn.functional as F  # All functions that don't have any parameters
+from torch.utils.data import (
+    DataLoader,
+)  # Gives easier dataset managment and creates mini batches
+import torchvision.datasets as datasets  # Has standard datasets we can import in a nice way
+import torchvision.transforms as transforms  # Transformations we can perform on our dataset
+
+# Set device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Hyperparameters
+input_size = 28
+sequence_length = 28
+num_layers = 2
+hidden_size = 256
+num_classes = 10
+learning_rate = 0.001
+batch_size = 64
+num_epochs = 2
+
+# Create a bidirectional LSTM
+class BRNN(nn.Module):
+    def __init__(self, input_size, hidden_size, num_layers, num_classes):
+        super(BRNN, self).__init__()
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
+        self.lstm = nn.LSTM(
+            input_size, hidden_size, num_layers, batch_first=True, bidirectional=True
+        )
+        self.fc = nn.Linear(hidden_size * 2, num_classes)
+
+    def forward(self, x):
+        h0 = torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size).to(device)
+        c0 = torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size).to(device)
+
+        out, _ = self.lstm(x, (h0, c0))
+        out = self.fc(out[:, -1, :])
+
+        return out
+
+
+# Load Data
+train_dataset = datasets.MNIST(
+    root="dataset/", train=True, transform=transforms.ToTensor(), download=True
+)
+
+test_dataset = datasets.MNIST(
+    root="dataset/", train=False, transform=transforms.ToTensor(), download=True
+)
+
+train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
+test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True)
+
+# Initialize network
+model = BRNN(input_size, hidden_size, num_layers, num_classes).to(device)
+
+# Loss and optimizer
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.Adam(model.parameters(), lr=learning_rate)
+
+# Train Network
+for epoch in range(num_epochs):
+    for batch_idx, (data, targets) in enumerate(train_loader):
+        # Get data to cuda if possible
+        data = data.to(device=device).squeeze(1)
+        targets = targets.to(device=device)
+
+        # forward
+        scores = model(data)
+        loss = criterion(scores, targets)
+
+        # backward
+        optimizer.zero_grad()
+        loss.backward()
+
+        # gradient descent or adam step
+        optimizer.step()
+
+# Check accuracy on training & test to see how good our model
+
+
+def check_accuracy(loader, model):
+    if loader.dataset.train:
+        print("Checking accuracy on training data")
+    else:
+        print("Checking accuracy on test data")
+
+    num_correct = 0
+    num_samples = 0
+    model.eval()
+
+    with torch.no_grad():
+        for x, y in loader:
+            x = x.to(device=device).squeeze(1)
+            y = y.to(device=device)
+
+            scores = model(x)
+            _, predictions = scores.max(1)
+            num_correct += (predictions == y).sum()
+            num_samples += predictions.size(0)
+
+        print(
+            f"Got {num_correct} / {num_samples} with accuracy  \
+              {float(num_correct)/float(num_samples)*100:.2f}"
+        )
+
+    model.train()
+
+
+check_accuracy(train_loader, model)
+check_accuracy(test_loader, model)