piiswrong closed pull request #8588: reimplement cross-entropy operator in
recommender example with NDArray
URL: https://github.com/apache/incubator-mxnet/pull/8588
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diff --git a/example/recommenders/crossentropy.py
b/example/recommenders/crossentropy.py
index 51648b0eb1..d8577ed898 100644
--- a/example/recommenders/crossentropy.py
+++ b/example/recommenders/crossentropy.py
@@ -20,6 +20,7 @@
"""Cross-entropy loss layer for MXNet.
"""
import os
+import time
import numpy as np
import mxnet as mx
@@ -51,10 +52,10 @@ def forward(self, is_train, req, in_data, out_data, aux):
# d = number of dimensions
actually_calculate_loss = False
if actually_calculate_loss:
- p = in_data[0].asnumpy() # shape=(b,d)
- y = in_data[1].asnumpy()
- out = y * np.log(p+self.eps) + (1.-y) * np.log((self.eps1) - p)
- self.assign(out_data[0], req[0], mx.nd.array(out))
+ p = in_data[0] # shape=(b,d)
+ y = in_data[1]
+ out = y * mx.nd.log(p+self.eps) + (1.-y) * mx.nd.log((self.eps1) -
p)
+ self.assign(out_data[0], req[0], out)
else:
# Just copy the predictions forward
self.assign(out_data[0], req[0], in_data[0])
@@ -70,19 +71,19 @@ def approx_backward(self, req, out_grad, in_data, out_data,
in_grad, aux):
grad = 1/(p-1+y)
which is more numerically stable
"""
- p = in_data[0].asnumpy() # shape=(b,d)
- y = in_data[1].asnumpy()
+ p = in_data[0] # shape=(b,d)
+ y = in_data[1]
grad = -1. / (p - self.eps_1 + y)
- self.assign(in_grad[0], req[0], mx.nd.array(grad))
+ self.assign(in_grad[0], req[0], grad)
def exact_backward(self, req, out_grad, in_data, out_data, in_grad, aux):
"""grad = (y-p)/(p-p^2)
"""
- p = in_data[0].asnumpy() # shape=(b,d)
- y = in_data[1].asnumpy() # seems right
+ p = in_data[0] # shape=(b,d)
+ y = in_data[1] # seems right
grad = (p - y) / ((p+self.eps) * (self.eps1 - p))
- self.assign(in_grad[0], req[0], mx.nd.array(grad))
+ self.assign(in_grad[0], req[0], grad)
@mx.operator.register("CrossEntropyLoss")
@@ -126,5 +127,15 @@ def infer_shape(self, in_shape):
out = e.outputs[0].asnumpy()
if np.abs(out).max() > 1e20:
raise ValueError("output too high!")
- print("Done with test")
+ print("performance test")
+ sz = (6,4)
+ d = mx.nd.array(rand.uniform(0.01,0.99,sz))
+ l = mx.nd.array(rand.randint(0,2,sz))
+ e = net.bind(ctx=mx.cpu(), args={'data':d, 'labs':l})
+ tic = time.time()
+ for i in range(5000):
+ e.forward()
+ e.outputs[0].wait_to_read()
+ print("5000 tests costs time: %f s" % (time.time()-tic))
+ print("Done with test")
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