56 lines
1.8 KiB
Python
56 lines
1.8 KiB
Python
#!/usr/bin/env python3
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# -*- coding:utf-8 -*-
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#############################################################
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# File: m1_test.py
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# Created Date: Saturday July 9th 2022
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# Author: Smiril
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# Email: sonar@gmx.com
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# Original: https://towardsdatascience.com/installing-pytorch-on-apple-m1-chip-with-gpu-acceleration-3351dc44d67c
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#############################################################
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import torch
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import math
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# this ensures that the current MacOS version is at least 12.3+
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print(torch.backends.mps.is_available())
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# this ensures that the current current PyTorch installation was built with MPS activated.
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print(torch.backends.mps.is_built())
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dtype = torch.float
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device = torch.device("mps")
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# Create random input and output data
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x = torch.linspace(-math.pi, math.pi, 2000, device=device, dtype=dtype)
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y = torch.sin(x)
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# Randomly initialize weights
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a = torch.randn((), device=device, dtype=dtype)
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b = torch.randn((), device=device, dtype=dtype)
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c = torch.randn((), device=device, dtype=dtype)
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d = torch.randn((), device=device, dtype=dtype)
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learning_rate = 1e-6
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for t in range(2000):
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# Forward pass: compute predicted y
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y_pred = a + b * x + c * x ** 2 + d * x ** 3
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# Compute and print loss
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loss = (y_pred - y).pow(2).sum().item()
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if t % 100 == 99:
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print(t, loss)
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# Backprop to compute gradients of a, b, c, d with respect to loss
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grad_y_pred = 2.0 * (y_pred - y)
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grad_a = grad_y_pred.sum()
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grad_b = (grad_y_pred * x).sum()
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grad_c = (grad_y_pred * x ** 2).sum()
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grad_d = (grad_y_pred * x ** 3).sum()
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# Update weights using gradient descent
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a -= learning_rate * grad_a
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b -= learning_rate * grad_b
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c -= learning_rate * grad_c
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d -= learning_rate * grad_d
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print(f'Result: y = {a.item()} + {b.item()} x + {c.item()} x^2 + {d.item()} x^3')
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