42 lines
1.4 KiB
Python
42 lines
1.4 KiB
Python
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import torch
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import numpy as np
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from datetime import datetime
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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, TensorDataset, DataLoader
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class LayerLinearRegression(nn.Module):
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def __init__(self):
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super().__init__()
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# Instead of our custom parameters, we use a Linear layer with single input and single output
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self.linear = nn.Linear(1, 1)
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def forward(self, x):
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# Now it only takes a call to the layer to make predictions
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return self.linear(x)
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checkpoint = torch.load('model.pt')
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model = LayerLinearRegression()
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optimizer = optim.SGD(model.parameters(), lr=checkpoint['loss'])
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model.load_state_dict(checkpoint['model_state_dict'])
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optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
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model.eval()
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now = datetime.now()
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print("\n-----------{}-----------".format(now.strftime("%d/%m/%Y, %H:%M:%S")))
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# Checks model's parameters
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print("Model's state_dict:")
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for param_tensor in model.state_dict():
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print(param_tensor, "\t", model.state_dict()[param_tensor])
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# Print optimizer's state_dict
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print("Optimizer's state_dict:")
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for var_name in optimizer.state_dict():
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print(var_name, "\t", optimizer.state_dict()[var_name])
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#print("Mean squared error for training: ", np.mean(losses))
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#print("Mean squared error for validating: ", np.mean(val_losses))
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print("----------------------\n")
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