forked from tinygrad/tinygrad
liej6799andGitHub
c06a4fcc80
* Fix temp location for windows * Make sure get tempfile for other os also. * Update utils.py
33 lines
1.2 KiB
Python
33 lines
1.2 KiB
Python
import numpy as np
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def mask_like(like, mask_inx, mask_value = 1.0):
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mask = np.zeros_like(like).reshape(-1)
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mask[mask_inx] = mask_value
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return mask.reshape(like.shape)
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def layer_init_uniform(*x):
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ret = np.random.uniform(-1., 1., size=x)/np.sqrt(np.prod(x))
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return ret.astype(np.float32)
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def fetch(url):
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import requests, os, hashlib, tempfile
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fp = os.path.join(tempfile.gettempdir(), hashlib.md5(url.encode('utf-8')).hexdigest())
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if os.path.isfile(fp):
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with open(fp, "rb") as f:
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dat = f.read()
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else:
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with open(fp, "wb") as f:
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dat = requests.get(url).content
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f.write(dat)
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return dat
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def fetch_mnist():
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import gzip
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parse = lambda dat: np.frombuffer(gzip.decompress(dat), dtype=np.uint8).copy()
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X_train = parse(fetch("http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz"))[0x10:].reshape((-1, 28, 28))
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Y_train = parse(fetch("http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz"))[8:]
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X_test = parse(fetch("http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz"))[0x10:].reshape((-1, 28, 28))
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Y_test = parse(fetch("http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz"))[8:]
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return X_train, Y_train, X_test, Y_test
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