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firestar5683
22a8575199
Keep official openpilot ancestry and dependency licenses. Store resources directly in Git, remove LFS setup requirements, and leave the downloadable big model outside the source tree.
43 lines
1.6 KiB
Python
Executable File
43 lines
1.6 KiB
Python
Executable File
#!/usr/bin/env python3
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import numpy as np
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import random
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from tinygrad.nn.state import get_parameters
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from tinygrad.nn.optim import Adam
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from extra.training import train, evaluate
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from extra.models.transformer import Transformer
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# dataset idea from https://github.com/karpathy/minGPT/blob/master/projects/adder/adder.py
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def make_dataset():
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ds = []
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for i in range(100):
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for j in range(100):
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s = i+j
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ds.append([i//10, i%10, j//10, j%10, s//100, (s//10)%10, s%10])
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random.shuffle(ds)
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ds = np.array(ds).astype(np.float32)
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ds_X = ds[:, 0:6]
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ds_Y = np.copy(ds[:, 1:]).astype(np.int32)
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ds_X_train, ds_X_test = ds_X[0:8000], ds_X[8000:]
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ds_Y_train, ds_Y_test = ds_Y[0:8000], ds_Y[8000:]
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return ds_X_train, ds_Y_train, ds_X_test, ds_Y_test
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if __name__ == "__main__":
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model = Transformer(10, 6, 2, 128, 4, 32)
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X_train, Y_train, X_test, Y_test = make_dataset()
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lr = 0.003
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for i in range(10):
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optim = Adam(get_parameters(model), lr=lr)
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train(model, X_train, Y_train, optim, 50, BS=64, allow_jit=True)
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acc, Y_test_preds = evaluate(model, X_test, Y_test, num_classes=10, return_predict=True)
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lr /= 1.2
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print(f'reducing lr to {lr:.4f}')
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if acc > 0.998:
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wrong=0
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for k in range(len(Y_test_preds)):
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if (Y_test_preds[k] != Y_test[k]).any():
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wrong+=1
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a,b,c,x = X_test[k,:2].astype(np.int32), X_test[k,2:4].astype(np.int32), Y_test[k,-3:].astype(np.int32), Y_test_preds[k,-3:].astype(np.int32)
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print(f'{a[0]}{a[1]} + {b[0]}{b[1]} = {x[0]}{x[1]}{x[2]} (correct: {c[0]}{c[1]}{c[2]})')
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print(f'Wrong predictions: {wrong}, acc = {acc:.4f}')
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