forked from tinygrad/tinygrad
55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
import numpy as np
|
|
|
|
def mask_like(like, mask_inx, mask_value = 1.0):
|
|
mask = np.zeros_like(like).reshape(-1)
|
|
mask[mask_inx] = mask_value
|
|
return mask.reshape(like.shape)
|
|
|
|
def layer_init_uniform(*x):
|
|
ret = np.random.uniform(-1., 1., size=x)/np.sqrt(np.prod(x))
|
|
return ret.astype(np.float32)
|
|
|
|
def fetch_mnist():
|
|
def fetch(url):
|
|
import requests, gzip, os, hashlib, numpy
|
|
fp = os.path.join("/tmp", hashlib.md5(url.encode('utf-8')).hexdigest())
|
|
if os.path.isfile(fp):
|
|
with open(fp, "rb") as f:
|
|
dat = f.read()
|
|
else:
|
|
with open(fp, "wb") as f:
|
|
dat = requests.get(url).content
|
|
f.write(dat)
|
|
return numpy.frombuffer(gzip.decompress(dat), dtype=numpy.uint8).copy()
|
|
X_train = fetch("http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz")[0x10:].reshape((-1, 28, 28))
|
|
Y_train = fetch("http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz")[8:]
|
|
X_test = fetch("http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz")[0x10:].reshape((-1, 28, 28))
|
|
Y_test = fetch("http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz")[8:]
|
|
return X_train, Y_train, X_test, Y_test
|
|
|
|
|
|
# these are matlab functions used to speed up convs
|
|
# write them fast and the convs will be fast?
|
|
|
|
def im2col(x, H, W):
|
|
bs,cin,oy,ox = x.shape[0], x.shape[1], x.shape[2]-(H-1), x.shape[3]-(W-1)
|
|
|
|
tx = np.empty((bs, oy, ox, cin*W*H), dtype=x.dtype)
|
|
for Y in range(oy):
|
|
for X in range(ox):
|
|
tx[:, Y, X] = x[:, :, Y:Y+H, X:X+W].reshape(bs, -1)
|
|
|
|
return tx.reshape(-1, cin*W*H)
|
|
|
|
def col2im(tx, H, W, OY, OX):
|
|
oy, ox = OY-(H-1), OX-(W-1)
|
|
bs = tx.shape[0] // (oy * ox)
|
|
cin = tx.shape[1] // (H * W)
|
|
tx = tx.reshape(bs, oy, ox, cin, H, W)
|
|
|
|
x = np.zeros((bs, cin, OY, OX), dtype=tx.dtype)
|
|
for Y in range(oy):
|
|
for X in range(ox):
|
|
x[:, :, Y:Y+H, X:X+W] += tx[:, Y, X]
|
|
return x
|