Morten Hjorth-Jensen
84 lines
2.2 KiB
Python
84 lines
2.2 KiB
Python
from IPython.display import Image as IPythonImage
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#%matplotlib inline
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import torch
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import numpy as np
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import matplotlib.pyplot as plt
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X_train = np.arange(10, dtype='float32').reshape((10, 1))
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y_train = np.array([1.0, 1.3, 3.1, 2.0, 5.0, 6.3, 6.6,
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7.4, 8.0, 9.0], dtype='float32')
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plt.plot(X_train, y_train, 'o', markersize=10)
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plt.xlabel('x')
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plt.ylabel('y')
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#plt.savefig('figures/12_07.pdf')
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plt.show()
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from torch.utils.data import TensorDataset
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from torch.utils.data import DataLoader
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X_train_norm = (X_train - np.mean(X_train)) / np.std(X_train)
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X_train_norm = torch.from_numpy(X_train_norm)
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# On some computers the explicit cast to .float() is
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# necessary
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y_train = torch.from_numpy(y_train).float()
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train_ds = TensorDataset(X_train_norm, y_train)
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batch_size = 1
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train_dl = DataLoader(train_ds, batch_size, shuffle=True)
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torch.manual_seed(1)
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weight = torch.randn(1)
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weight.requires_grad_()
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bias = torch.zeros(1, requires_grad=True)
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def loss_fn(input, target):
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return (input-target).pow(2).mean()
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def model(xb):
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return xb @ weight + bias
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learning_rate = 0.001
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num_epochs = 200
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log_epochs = 10
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for epoch in range(num_epochs):
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for x_batch, y_batch in train_dl:
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pred = model(x_batch)
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loss = loss_fn(pred, y_batch)
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loss.backward()
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with torch.no_grad():
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weight -= weight.grad * learning_rate
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bias -= bias.grad * learning_rate
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weight.grad.zero_()
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bias.grad.zero_()
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if epoch % log_epochs==0:
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print(f'Epoch {epoch} Loss {loss.item():.4f}')
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print('Final Parameters:', weight.item(), bias.item())
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X_test = np.linspace(0, 9, num=100, dtype='float32').reshape(-1, 1)
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X_test_norm = (X_test - np.mean(X_train)) / np.std(X_train)
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X_test_norm = torch.from_numpy(X_test_norm)
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y_pred = model(X_test_norm).detach().numpy()
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fig = plt.figure(figsize=(13, 5))
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ax = fig.add_subplot(1, 2, 1)
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plt.plot(X_train_norm, y_train, 'o', markersize=10)
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plt.plot(X_test_norm, y_pred, '--', lw=3)
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plt.legend(['Training examples', 'Linear Reg.'], fontsize=15)
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ax.set_xlabel('x', size=15)
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ax.set_ylabel('y', size=15)
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ax.tick_params(axis='both', which='major', labelsize=15)
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#plt.savefig('figures/12_08.pdf')
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plt.show()
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