| import os |
| import mxnet as mx |
| import numpy as np |
| |
| def same(a, b): |
| return np.sum(a != b) == 0 |
| |
| def check_with_device(device): |
| a, b = -10, 10 |
| mu, sigma = 10, 2 |
| shape = (100, 100) |
| mx.random.seed(128) |
| ret1 = mx.random.normal(mu, sigma, shape, ctx=device) |
| un1 = mx.random.uniform(a, b, shape, ctx=device) |
| mx.random.seed(128) |
| ret2 = mx.random.normal(mu, sigma, shape, ctx=device) |
| un2 = mx.random.uniform(a, b, shape, ctx=device) |
| assert same(ret1.asnumpy(), ret2.asnumpy()) |
| assert same(un1.asnumpy(), un2.asnumpy()) |
| assert abs(np.mean(ret1.asnumpy()) - mu) < 0.1 |
| assert abs(np.std(ret1.asnumpy()) - sigma) < 0.1 |
| assert abs(np.mean(un1.asnumpy()) - (a+b)/2) < 0.1 |
| |
| |
| def check_symbolic_random(dev): |
| a, b = -10, 10 |
| mu, sigma = 10, 2 |
| shape = (100, 100) |
| X = mx.sym.Variable("X") |
| Y = mx.sym.uniform(low=a, high=b, shape=shape) + X |
| x = mx.nd.zeros(shape, ctx=dev) |
| xgrad = mx.nd.zeros(shape, ctx=dev) |
| yexec = Y.bind(dev, {'X' : x}, {'X': xgrad}) |
| mx.random.seed(128) |
| yexec.forward(is_train=True) |
| yexec.backward(yexec.outputs[0]) |
| un1 = (yexec.outputs[0] - x).copyto(dev) |
| assert same(xgrad.asnumpy(), un1.asnumpy()) |
| mx.random.seed(128) |
| yexec.forward() |
| un2 = (yexec.outputs[0] - x).copyto(dev) |
| assert same(un1.asnumpy(), un2.asnumpy()) |
| assert abs(np.mean(un1.asnumpy()) - (a+b)/2) < 0.1 |
| |
| Y = mx.sym.normal(loc=mu, scale=sigma, shape=shape) |
| yexec = Y.simple_bind(dev) |
| mx.random.seed(128) |
| yexec.forward() |
| ret1 = yexec.outputs[0].copyto(dev) |
| mx.random.seed(128) |
| ret2 = mx.random.normal(mu, sigma, shape) |
| assert same(ret1.asnumpy(), ret2.asnumpy()) |
| assert abs(np.mean(ret1.asnumpy()) - mu) < 0.1 |
| assert abs(np.std(ret1.asnumpy()) - sigma) < 0.1 |
| |
| |
| def test_random(): |
| check_with_device(mx.cpu()) |
| check_symbolic_random(mx.cpu()) |
| |
| |
| if __name__ == '__main__': |
| test_random() |
