[GitHub] piiswrong closed pull request #9005: update python-howto examples

[GitBox <gi...@apache.org>]

piiswrong closed pull request #9005: update python-howto examples
URL: https://github.com/apache/incubator-mxnet/pull/9005
 
 
   

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diff --git a/example/python-howto/README.md b/example/python-howto/README.md
index 2499c2ab07..29652408e0 100644
--- a/example/python-howto/README.md
+++ b/example/python-howto/README.md
@@ -1,15 +1,17 @@
 Python Howto Examples
 =====================
-* [Configuring Net to get Multiple Ouputs](multiple_outputs.py)
+
+* [Configuring Net to Get Multiple Ouputs](multiple_outputs.py)
 * [Configuring Image Record Iterator](data_iter.py)
+* [Monitor Intermediate Outputs in the Network](monitor_weights.py)
 * Set break point in C++ code of the symbol using gdb under Linux:
 
 	* 	Build mxnet with following values:
 
 		 ```
 		 	DEBUG=1 
-		 	CUDA=0 #to make sure convolution-inl.h will be used 
-		 	CUDNN=0 #to make sure convolution-inl.h will be used 
+		 	USE_CUDA=0 # to make sure convolution-inl.h will be used
+		 	USE_CUDNN=0 # to make sure convolution-inl.h will be used
 		 ```
 		 
 	*  run python under gdb:  ```gdb --args python debug_conv.py```
diff --git a/example/python-howto/monitor_weights.py b/example/python-howto/monitor_weights.py
index a8b255196d..ab77b4908b 100644
--- a/example/python-howto/monitor_weights.py
+++ b/example/python-howto/monitor_weights.py
@@ -25,6 +25,7 @@
 import numpy as np
 import logging
 
+# network
 data = mx.symbol.Variable('data')
 fc1 = mx.symbol.FullyConnected(data = data, name='fc1', num_hidden=128)
 act1 = mx.symbol.Activation(data = fc1, name='relu1', act_type="relu")
@@ -34,20 +35,16 @@
 mlp = mx.symbol.SoftmaxOutput(data = fc3, name = 'softmax')
 
 # data
-
 train, val = MNISTIterator(batch_size=100, input_shape = (784,))
 
-# train
-
-logging.basicConfig(level=logging.DEBUG)
-
-model = mx.model.FeedForward(
-    ctx = mx.cpu(), symbol = mlp, num_epoch = 20,
-    learning_rate = 0.1, momentum = 0.9, wd = 0.00001)
-
+# monitor
 def norm_stat(d):
     return mx.nd.norm(d)/np.sqrt(d.size)
 mon = mx.mon.Monitor(100, norm_stat)
-model.fit(X=train, eval_data=val, monitor=mon,
-          batch_end_callback = mx.callback.Speedometer(100, 100))
 
+# train with monitor
+logging.basicConfig(level=logging.DEBUG)
+module = mx.module.Module(context=mx.cpu(), symbol=mlp)
+module.fit(train_data=train, eval_data=val, monitor=mon, num_epoch=2,
+           batch_end_callback = mx.callback.Speedometer(100, 100),
+           optimizer_params=(('learning_rate', 0.1), ('momentum', 0.9), ('wd', 0.00001)))


 

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