pengxin99 commented on a change in pull request #12498: Code modification for
testcases of various network models in directory example
URL: https://github.com/apache/incubator-mxnet/pull/12498#discussion_r235269821
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File path: example/bayesian-methods/bdk_demo.py
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@@ -180,18 +183,18 @@ def run_mnist_SGLD(training_num=50000):
minibatch_size = 100
net = get_mnist_sym()
data_shape = (minibatch_size,) + X.shape[1::]
- data_inputs = {'data': nd.zeros(data_shape, ctx=dev()),
- 'softmax_label': nd.zeros((minibatch_size,), ctx=dev())}
+ data_inputs = {'data': nd.zeros(data_shape, ctx=dev(xpu)),
+ 'softmax_label': nd.zeros((minibatch_size,), ctx=dev(xpu))}
initializer = mx.init.Xavier(factor_type="in", magnitude=2.34)
- exe, sample_pool = SGLD(sym=net, dev=dev(), data_inputs=data_inputs, X=X,
Y=Y,
+ exe, sample_pool = SGLD(sym=net, dev=dev(xpu), data_inputs=data_inputs,
X=X, Y=Y,
X_test=X_test, Y_test=Y_test,
total_iter_num=1000000,
initializer=initializer,
learning_rate=4E-6, prior_precision=1.0,
minibatch_size=100,
thin_interval=100, burn_in_iter_num=1000)
-def run_mnist_DistilledSGLD(training_num=50000):
+def run_mnist_DistilledSGLD(training_num=50000, xpu=0):
Review comment:
@sandeep-krishnamurthy The code has been modified according to your
requirements. Could you review it again? Thank you!
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