mirror of https://github.com/commaai/tinygrad.git
73 lines
1.7 KiB
Python
73 lines
1.7 KiB
Python
#!/usr/bin/env python
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import numpy as np
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from tinygrad.tensor import Tensor
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from tinygrad.utils import fetch_mnist
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import tinygrad.optim as optim
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from tqdm import trange
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# load the mnist dataset
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X_train, Y_train, X_test, Y_test = fetch_mnist()
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# train a model
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np.random.seed(1337)
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def layer_init(m, h):
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ret = np.random.uniform(-1., 1., size=(m,h))/np.sqrt(m*h)
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return ret.astype(np.float32)
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class TinyBobNet:
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def __init__(self):
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self.l1 = Tensor(layer_init(784, 128))
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self.l2 = Tensor(layer_init(128, 10))
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def forward(self, x):
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return x.dot(self.l1).relu().dot(self.l2).logsoftmax()
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# optimizer
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model = TinyBobNet()
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optim = optim.SGD([model.l1, model.l2], lr=0.001)
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#optim = optim.Adam([model.l1, model.l2], lr=0.001)
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BS = 128
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losses, accuracies = [], []
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for i in (t := trange(1000)):
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samp = np.random.randint(0, X_train.shape[0], size=(BS))
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x = Tensor(X_train[samp].reshape((-1, 28*28)))
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Y = Y_train[samp]
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y = np.zeros((len(samp),10), np.float32)
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# correct loss for NLL, torch NLL loss returns one per row
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y[range(y.shape[0]),Y] = -10.0
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y = Tensor(y)
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# network
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outs = model.forward(x)
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# NLL loss function
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loss = outs.mul(y).mean()
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loss.backward()
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optim.step()
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cat = np.argmax(outs.data, axis=1)
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accuracy = (cat == Y).mean()
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# printing
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loss = loss.data
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losses.append(loss)
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accuracies.append(accuracy)
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t.set_description("loss %.2f accuracy %.2f" % (loss, accuracy))
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# evaluate
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def numpy_eval():
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Y_test_preds_out = model.forward(Tensor(X_test.reshape((-1, 28*28))))
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Y_test_preds = np.argmax(Y_test_preds_out.data, axis=1)
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return (Y_test == Y_test_preds).mean()
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accuracy = numpy_eval()
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print("test set accuracy is %f" % accuracy)
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assert accuracy > 0.95
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