[GitHub] piiswrong closed pull request #9174: Adding test for forward backward compatibility

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

piiswrong closed pull request #9174: Adding test for forward backward compatibility
URL: https://github.com/apache/incubator-mxnet/pull/9174
 
 
   

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diff --git a/load_inference.py b/load_inference.py
new file mode 100644
index 0000000000..fc58595f12
--- /dev/null
+++ b/load_inference.py
@@ -0,0 +1,33 @@
+import mxnet as mx
+mnist = mx.test_utils.get_mnist()
+batch_size = 100
+train_iter = mx.io.NDArrayIter(mnist['train_data'], mnist['train_label'], batch_size, shuffle=True)
+val_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
+data = mx.sym.var('data')
+# Flatten the data from 4-D shape into 2-D (batch_size, num_channel*width*height)
+data = mx.sym.flatten(data=data)
+# The first fully-connected layer and the corresponding activation function
+fc1  = mx.sym.FullyConnected(data=data, num_hidden=128)
+act1 = mx.sym.Activation(data=fc1, act_type="relu")
+
+# The second fully-connected layer and the corresponding activation function
+fc2  = mx.sym.FullyConnected(data=act1, num_hidden = 64)
+act2 = mx.sym.Activation(data=fc2, act_type="relu")
+# MNIST has 10 classes
+fc3  = mx.sym.FullyConnected(data=act2, num_hidden=10)
+# Softmax with cross entropy loss
+mlp  = mx.sym.SoftmaxOutput(data=fc3, name='softmax')
+import logging
+logging.getLogger().setLevel(logging.DEBUG)  # logging to stdout
+# create a trainable module on CPU
+mlp_model = mx.mod.Module(symbol=mlp, context=mx.cpu())
+mlp_model.bind(data_shapes=train_iter.provide_data, label_shapes=train_iter.provide_label)
+mlp_model.load_params('mymodel')
+test_iter = mx.io.NDArrayIter(mnist['test_data'], None, batch_size)
+prob = mlp_model.predict(test_iter)
+assert prob.shape == (10000, 10)
+test_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
+# predict accuracy of mlp
+acc = mx.metric.Accuracy()
+mlp_model.score(test_iter, acc)
+print(acc)
diff --git a/train_models.py b/train_models.py
new file mode 100644
index 0000000000..1fa8b58afa
--- /dev/null
+++ b/train_models.py
@@ -0,0 +1,36 @@
+# Example taken from https://mxnet.incubator.apache.org/tutorials/python/mnist.html
+import mxnet as mx
+mnist = mx.test_utils.get_mnist()
+batch_size = 100
+train_iter = mx.io.NDArrayIter(mnist['train_data'], mnist['train_label'], batch_size, shuffle=True)
+val_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
+data = mx.sym.var('data')
+# Flatten the data from 4-D shape into 2-D (batch_size, num_channel*width*height)
+data = mx.sym.flatten(data=data)
+# The first fully-connected layer and the corresponding activation function
+fc1  = mx.sym.FullyConnected(data=data, num_hidden=128)
+act1 = mx.sym.Activation(data=fc1, act_type="relu")
+
+# The second fully-connected layer and the corresponding activation function
+fc2  = mx.sym.FullyConnected(data=act1, num_hidden = 64)
+act2 = mx.sym.Activation(data=fc2, act_type="relu")
+# MNIST has 10 classes
+fc3  = mx.sym.FullyConnected(data=act2, num_hidden=10)
+# Softmax with cross entropy loss
+mlp  = mx.sym.SoftmaxOutput(data=fc3, name='softmax')
+import logging
+logging.getLogger().setLevel(logging.DEBUG)  # logging to stdout
+# create a trainable module on CPU
+mlp_model = mx.mod.Module(symbol=mlp, context=mx.cpu())
+mlp_model.fit(train_iter,  # train data
+              eval_data=val_iter,  # validation data
+              optimizer='sgd',  # use SGD to train
+              optimizer_params={'learning_rate':0.1},  # use fixed learning rate
+              eval_metric='acc',  # report accuracy during training
+              batch_end_callback = mx.callback.Speedometer(batch_size, 100), # output progress for each 100 data batches
+              num_epoch=1)  # train for at most 1 dataset passes modified
+
+
+# save a model to mymodel-symbol.json and mymodel-0100.params
+filename='mymodel'
+mlp_model.save_params(filename)


 

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