Fix some bugs in doc and log (#561)

* fix some bugs in doc and log

* The learning rate focus more on validation sets accuracy than training sets accuracy.

examples/trials/mnist-batch-tune-keras/mnist-keras.py

@@ -84,7 +84,7 @@ class SendMetrics(keras.callbacks.Callback):
         Run on end of each epoch
         '''
         LOG.debug(logs)
-        nni.report_intermediate_result(logs['acc'])
+        nni.report_intermediate_result(logs["val_acc"])
 
 def train(args, params):
     '''

examples/trials/mnist-keras/mnist-keras.py

@@ -84,7 +84,7 @@ class SendMetrics(keras.callbacks.Callback):
         Run on end of each epoch
         '''
         LOG.debug(logs)
-        nni.report_intermediate_result(logs['acc'])
+        nni.report_intermediate_result(logs["val_acc"])
 
 def train(args, params):
     '''

examples/trials/network_morphism/FashionMNIST/FashionMNIST_keras.py

@@ -40,7 +40,7 @@ logging.basicConfig(
     datefmt="%m/%d %I:%M:%S %p",
 )
 # set the logger format
-logger = logging.getLogger("fashion_mnist-network-morphism-keras")
+logger = logging.getLogger("FashionMNIST-network-morphism-keras")
 
 
 # restrict gpu usage background
@@ -152,7 +152,7 @@ class SendMetrics(keras.callbacks.Callback):
         if logs is None:
             logs = dict()
         logger.debug(logs)
-        nni.report_intermediate_result(logs["acc"])
+        nni.report_intermediate_result(logs["val_acc"])
 
 
 # Training
@@ -184,7 +184,7 @@ def train_eval():
 
     # trial report final acc to tuner
     _, acc = net.evaluate(x_test, y_test)
-    logger.debug("Final result is: %d", acc)
+    logger.debug("Final result is: %.3f", acc)
     nni.report_final_result(acc)
 
 

examples/trials/network_morphism/FashionMNIST/FashionMNIST_pytorch.py

@@ -42,7 +42,7 @@ logging.basicConfig(
 )
 # pylint: disable=W0603
 # set the logger format
-logger = logging.getLogger("FashionMNIST-network-morphism")
+logger = logging.getLogger("FashionMNIST-network-morphism-pytorch")
 
 
 def get_args():

examples/trials/network_morphism/cifar10/cifar10_keras.py

@@ -152,7 +152,7 @@ class SendMetrics(keras.callbacks.Callback):
         if logs is None:
             logs = dict()
         logger.debug(logs)
-        nni.report_intermediate_result(logs["acc"])
+        nni.report_intermediate_result(logs["val_acc"])
 
 
 # Training
@@ -184,7 +184,7 @@ def train_eval():
 
     # trial report final acc to tuner
     _, acc = net.evaluate(x_test, y_test)
-    logger.debug("Final result is: %d", acc)
+    logger.debug("Final result is: %.3f", acc)
     nni.report_final_result(acc)
 
 

examples/trials/network_morphism/requirements.txt

@@ -2,5 +2,5 @@ numpy==1.14.2
 tensorflow==1.12.0
 torchvision==0.2.1
 Keras==2.2.2
-nni==0.3.0
+nni==0.4.1
 torch==0.4.1

examples/trials/network_morphism/utils.py

-"""Some helper functions for PyTorch, including:
-    - get_mean_and_std: calculate the mean and std value of dataset.
-    - msr_init: net parameter initialization.
-    - progress_bar: progress bar mimic xlua.progress.
-"""
+# Copyright (c) Microsoft Corporation
+# All rights reserved.
+#
+# MIT License
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation
+# the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and
+# to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING
+# BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
+# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
 import numpy as np
 import torch
 import torch.nn as nn