You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2017/12/30 06:47:17 UTC
[GitHub] szha closed pull request #8958: fix bayesian-methods example
szha closed pull request #8958: fix bayesian-methods example
URL: https://github.com/apache/incubator-mxnet/pull/8958
This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:
As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):
diff --git a/example/bayesian-methods/algos.py b/example/bayesian-methods/algos.py
index e47a18f398..f7b3620707 100644
--- a/example/bayesian-methods/algos.py
+++ b/example/bayesian-methods/algos.py
@@ -140,8 +140,7 @@ def SGD(sym, data_inputs, X, Y, X_test, Y_test, total_iter_num,
optimizer = mx.optimizer.create('sgd', learning_rate=lr,
rescale_grad=X.shape[0] / minibatch_size,
lr_scheduler=lr_scheduler,
- wd=prior_precision,
- arg_names=params.keys())
+ wd=prior_precision)
updater = mx.optimizer.get_updater(optimizer)
start = time.time()
for i in range(total_iter_num):
diff --git a/example/bayesian-methods/bdk.ipynb b/example/bayesian-methods/bdk.ipynb
index 8c98651063..46436c1ca8 100644
--- a/example/bayesian-methods/bdk.ipynb
+++ b/example/bayesian-methods/bdk.ipynb
@@ -25,6 +25,7 @@
},
"outputs": [],
"source": [
+ "from __future__ import print_function\n",
"import mxnet as mx\n",
"import mxnet.ndarray as nd\n",
"import numpy\n",
@@ -40,10 +41,9 @@
" origin = (\n",
" 'https://github.com/sxjscience/mxnet/raw/master/example/bayesian-methods/mnist.npz'\n",
" )\n",
- " print 'Downloading data from %s to %s' % (origin, data_path)\n",
- " context = ssl._create_unverified_context()\n",
- " urllib.request.urlretrieve(origin, data_path, context=context)\n",
- " print 'Done!'\n",
+ " print('Downloading data from %s to %s' % (origin, data_path))\n",
+ " urllib.request.urlretrieve(origin, data_path)\n",
+ " print('Done!')\n",
" dat = numpy.load(data_path)\n",
" X = (dat['X'][:training_num] / 126.0).astype('float32')\n",
" Y = dat['Y'][:training_num]\n",
@@ -136,7 +136,7 @@
" teacher_updater = mx.optimizer.get_updater(teacher_optimizer)\n",
" student_updater = mx.optimizer.get_updater(student_optimizer)\n",
" start = time.time()\n",
- " for i in xrange(total_iter_num):\n",
+ " for i in range(total_iter_num):\n",
" # 1.1 Draw random minibatch\n",
" indices = numpy.random.randint(X.shape[0], size=minibatch_size)\n",
" X_batch = X[indices]\n",
@@ -170,7 +170,7 @@
"\n",
" if (i + 1) % 2000 == 0:\n",
" end = time.time()\n",
- " print \"Current Iter Num: %d\" % (i + 1), \"Time Spent: %f\" % (end - start)\n",
+ " print(\"Current Iter Num: %d\" % (i + 1), \"Time Spent: %f\" % (end - start))\n",
" test_correct, test_total, test_acc = \\\n",
" sample_test_acc(student_exe, X=X_test, Y=Y_test, label_num=10,\n",
" minibatch_size=minibatch_size)\n",
@@ -183,11 +183,11 @@
" teacher_train_correct, teacher_train_total, teacher_train_acc = \\\n",
" sample_test_acc(teacher_exe, X=X, Y=Y, label_num=10,\n",
" minibatch_size=minibatch_size)\n",
- " print \"Student: Test %d/%d=%f, Train %d/%d=%f\" % (test_correct, test_total, test_acc,\n",
- " train_correct, train_total, train_acc)\n",
- " print \"Teacher: Test %d/%d=%f, Train %d/%d=%f\" \\\n",
+ " print(\"Student: Test %d/%d=%f, Train %d/%d=%f\" % (test_correct, test_total, test_acc,\n",
+ " train_correct, train_total, train_acc))\n",
+ " print(\"Teacher: Test %d/%d=%f, Train %d/%d=%f\" \\\n",
" % (teacher_test_correct, teacher_test_total, teacher_test_acc,\n",
- " teacher_train_correct, teacher_train_total, teacher_train_acc)\n",
+ " teacher_train_correct, teacher_train_total, teacher_train_acc))\n",
" start = time.time()\n"
]
},
diff --git a/example/bayesian-methods/data_loader.py b/example/bayesian-methods/data_loader.py
index 2649eb560b..92ca0cfb3a 100644
--- a/example/bayesian-methods/data_loader.py
+++ b/example/bayesian-methods/data_loader.py
@@ -29,8 +29,9 @@ def load_mnist(training_num=50000):
'https://github.com/sxjscience/mxnet/raw/master/example/bayesian-methods/mnist.npz'
)
print('Downloading data from %s to %s' % (origin, data_path))
- context = ssl._create_unverified_context()
- urllib.request.urlretrieve(origin, data_path, context=context)
+ ctx = ssl._create_unverified_context()
+ with urllib.request.urlopen(origin, context=ctx) as u, open(data_path, 'wb') as f:
+ f.write(u.read())
print('Done!')
dat = numpy.load(data_path)
X = (dat['X'][:training_num] / 126.0).astype('float32')
diff --git a/example/bayesian-methods/sgld.ipynb b/example/bayesian-methods/sgld.ipynb
index 197dd1ee39..3c1d008678 100644
--- a/example/bayesian-methods/sgld.ipynb
+++ b/example/bayesian-methods/sgld.ipynb
@@ -141,14 +141,14 @@
" lr_scheduler=lr_scheduler,\n",
" wd=0)\n",
"updater = mx.optimizer.get_updater(optimizer)\n",
- "theta = mx.random.normal(0, 1, (2,), mx.cpu())\n",
+ "theta = mx.random.normal(0, 1, (2,), ctx=mx.cpu())\n",
"grad = nd.empty((2,), mx.cpu())\n",
"samples = numpy.zeros((2, total_iter_num))\n",
"start = time.time()\n",
- "for i in xrange(total_iter_num):\n",
+ "for i in range(total_iter_num):\n",
" if (i+1)%100000 == 0:\n",
" end = time.time()\n",
- " print \"Iter:%d, Time spent: %f\" %(i + 1, end-start)\n",
+ " print(\"Iter:%d, Time spent: %f\" %(i + 1, end-start))\n",
" start = time.time()\n",
" ind = numpy.random.randint(0, X.shape[0])\n",
" synthetic_grad(X[ind], theta, sigma1, sigma2, sigmax, rescale_grad=\n",
@@ -244,4 +244,4 @@
},
"nbformat": 4,
"nbformat_minor": 0
-}
\ No newline at end of file
+}
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
With regards,
Apache Git Services