[GitHub] indhub closed pull request #9092: Some changes to numpy_ops examples

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

indhub closed pull request #9092: Some changes to numpy_ops examples
URL: https://github.com/apache/incubator-mxnet/pull/9092
 
 
   

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diff --git a/example/numpy-ops/custom_softmax.py b/example/numpy-ops/custom_softmax.py
index 82f491e458..a2ec5d54b7 100644
--- a/example/numpy-ops/custom_softmax.py
+++ b/example/numpy-ops/custom_softmax.py
@@ -82,10 +82,12 @@ def create_operator(self, ctx, shapes, dtypes):
 logging.basicConfig(level=logging.DEBUG)
 
 # MXNET_CPU_WORKER_NTHREADS must be greater than 1 for custom op to work on CPU
-model = mx.model.FeedForward(
-    ctx = mx.cpu(0), symbol = mlp, num_epoch = 20,
-    learning_rate = 0.1, momentum = 0.9, wd = 0.00001)
+context=mx.cpu()
+# Uncomment this line to train on GPU
+# context=mx.gpu(0)
 
-model.fit(X=train, eval_data=val,
-          batch_end_callback=mx.callback.Speedometer(100,100))
+mod = mx.mod.Module(mlp, context=context)
 
+mod.fit(train_data=train, eval_data=val, optimizer='sgd',
+    optimizer_params={'learning_rate':0.1, 'momentum': 0.9, 'wd': 0.00001},
+    num_epoch=10, batch_end_callback=mx.callback.Speedometer(100, 100))
diff --git a/example/numpy-ops/numpy_softmax.py b/example/numpy-ops/numpy_softmax.py
index cbcb7787ae..c10dfe3779 100644
--- a/example/numpy-ops/numpy_softmax.py
+++ b/example/numpy-ops/numpy_softmax.py
@@ -76,9 +76,13 @@ def backward(self, out_grad, in_data, out_data, in_grad):
 
 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)
+# MXNET_CPU_WORKER_NTHREADS must be greater than 1 for custom op to work on CPU
+context=mx.cpu()
+# Uncomment this line to train on GPU instead of CPU
+# context=mx.gpu(0)
 
-model.fit(X=train, eval_data=val)
+mod = mx.mod.Module(mlp, context=context)
 
+mod.fit(train_data=train, eval_data=val, optimizer='sgd',
+    optimizer_params={'learning_rate':0.1, 'momentum': 0.9, 'wd': 0.00001},
+    num_epoch=10, batch_end_callback=mx.callback.Speedometer(100, 100))
diff --git a/example/numpy-ops/weighted_logistic_regression.py b/example/numpy-ops/weighted_logistic_regression.py
index 26b5fb2fda..4062495e86 100644
--- a/example/numpy-ops/weighted_logistic_regression.py
+++ b/example/numpy-ops/weighted_logistic_regression.py
@@ -15,7 +15,6 @@
 # specific language governing permissions and limitations
 # under the License.
 
-import os
 import numpy as np
 import mxnet as mx
 
@@ -26,7 +25,8 @@ def __init__(self, pos_grad_scale, neg_grad_scale):
     def forward(self, is_train, req, in_data, out_data, aux):
         self.assign(out_data[0], req[0], mx.nd.divide(1, (1 + mx.nd.exp(- in_data[0]))))
     def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
-        in_grad[0][:] = ((out_data[0] - 1) * in_data[1] * self.pos_grad_scale + out_data[0] * (1 - in_data[1]) * self.neg_grad_scale) / out_data[0].shape[1]
+        in_grad[0][:] = ((out_data[0] - 1) * in_data[1] * self.pos_grad_scale
+                         + out_data[0] * (1 - in_data[1]) * self.neg_grad_scale) / out_data[0].shape[1]
 
 @mx.operator.register("weighted_logistic_regression")
 class WeightedLogisticRegressionProp(mx.operator.CustomOpProp):
@@ -48,23 +48,39 @@ def create_operator(self, ctx, shapes, dtypes):
     m, n = 2, 5
     pos, neg = 1, 0.1
     data = mx.sym.Variable('data')
-    wlr = mx.sym.Custom(data, pos_grad_scale = pos, neg_grad_scale = neg, name = 'wlr', op_type = 'weighted_logistic_regression')
-    lr = mx.sym.LogisticRegressionOutput(data, name = 'lr')
-    exe1 = wlr.simple_bind(ctx = mx.gpu(1), data = (2 * m, n))
-    exe2 = lr.simple_bind(ctx = mx.gpu(1), data = (2 * m, n))
+
+    wlr = mx.sym.Custom(data, pos_grad_scale=pos, neg_grad_scale=neg, name='wlr',
+                        op_type='weighted_logistic_regression')
+    lr = mx.sym.LogisticRegressionOutput(data, name='lr')
+
+    # MXNET_CPU_WORKER_NTHREADS must be greater than 1 for custom op to work on CPU
+    context = mx.cpu()
+    # Uncomment this line to compute on GPU
+    # context=mx.gpu(0)
+
+    exe1 = wlr.simple_bind(ctx=context, data=(2 * m, n))
+    exe2 = lr.simple_bind(ctx=context, data=(2 * m, n))
+
     exe1.arg_dict['data'][:] = np.ones([2 * m, n])
     exe2.arg_dict['data'][:] = np.ones([2 * m, n])
+
     exe1.arg_dict['wlr_label'][:] = np.vstack([np.ones([m, n]), np.zeros([m, n])])
     exe2.arg_dict['lr_label'][:] = np.vstack([np.ones([m, n]), np.zeros([m, n])])
-    exe1.forward(is_train = True)
-    exe2.forward(is_train = True)
-    print('wlr output:')
+
+    exe1.forward(is_train=True)
+    exe2.forward(is_train=True)
+
+    print('Weighted Logistic Regression output:')
     print(exe1.outputs[0].asnumpy())
-    print('lr output:')
+
+    print('Logistic Regression output:')
     print(exe2.outputs[0].asnumpy())
+
     exe1.backward()
     exe2.backward()
-    print('wlr grad:')
+
+    print('Weighted Logistic Regression gradients:')
     print(exe1.grad_dict['data'].asnumpy())
-    print('lr grad:')
+
+    print('Logistic Regression gradients:')
     print(exe2.grad_dict['data'].asnumpy())


 

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