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