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100 lines
3.0 KiB
Python
100 lines
3.0 KiB
Python
#!/usr/bin/env python
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import os
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import unittest
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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 layer_init_uniform, fetch_mnist
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import tinygrad.optim as tinygrad_optim
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from tqdm import trange
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np.random.seed(1337)
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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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# create a model
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class TinyBobNet:
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def __init__(self):
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self.l1 = Tensor(layer_init_uniform(784, 128))
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self.l2 = Tensor(layer_init_uniform(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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# create a model with a conv layer
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# perfect if you like slow speeds and very little accuracy gains
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class TinyConvNet:
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def __init__(self):
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self.chans = 4
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self.c1 = Tensor(layer_init_uniform(self.chans,1,3,3))
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self.l1 = Tensor(layer_init_uniform(26*26*self.chans, 128))
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self.l2 = Tensor(layer_init_uniform(128, 10))
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def forward(self, x):
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x.data = x.data.reshape((-1, 1, 28, 28)) # hacks
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x = x.conv2d(self.c1).reshape(Tensor(np.array((-1, 26*26*self.chans)))).relu()
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return x.dot(self.l1).relu().dot(self.l2).logsoftmax()
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class TestMNIST(unittest.TestCase):
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def test_mnist(self):
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def train(model, optim, steps, BS=128):
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losses, accuracies = [], []
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for i in (t := trange(steps)):
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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)).astype(np.float32))
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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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out = model.forward(x)
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# NLL loss function
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loss = out.mul(y).mean()
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loss.backward()
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optim.step()
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cat = np.argmax(out.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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def evaluate(model):
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def numpy_eval():
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Y_test_preds_out = model.forward(Tensor(X_test.reshape((-1, 28*28)).astype(np.float32)))
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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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# models
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if os.getenv("CONV") == "1":
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model = TinyConvNet()
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optim = tinygrad_optim.Adam([model.c1, model.l1, model.l2], lr=0.001)
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steps = 400
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train(model, optim, steps)
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evaluate(model)
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else:
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model = TinyBobNet()
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optim = tinygrad_optim.SGD([model.l1, model.l2], lr=0.001)
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steps = 1000
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train(model, optim, steps)
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evaluate(model)
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# RMSprop
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optim = tinygrad_optim.RMSprop([model.l1, model.l2], lr=0.001)
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train(model, optim, steps)
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evaluate(model)
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if __name__ == '__main__':
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unittest.main()
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