Improved output for readability

feranick · Oct 21, 2017 · fce18b8 · fce18b8
1 parent 13be59e
commit fce18b8
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Showing 4 changed files with 21 additions and 13 deletions.
diff --git a/SpectraLearnPredict/SpectraLearnPredict/slp/__init__.py b/SpectraLearnPredict/SpectraLearnPredict/slp/__init__.py
@@ -5,7 +5,7 @@
 *
 * SpectraLearnPredict
 * Perform Machine Learning on Spectroscopy Data.
-* version: 20171020a
+* version: 20171021a
 *
 * Uses: Deep Neural Networks, TensorFlow, SVM, PCA, K-Means
 *

diff --git a/SpectraLearnPredict/SpectraLearnPredict/slp/slp_config.py b/SpectraLearnPredict/SpectraLearnPredict/slp/slp_config.py
@@ -368,8 +368,8 @@ class dnntfDef:
 
     plotMap = config.plotMapDNNTF
 
-    shuffleTrainDNNTF = config.shuffleTrainDNNTF
-    shuffleTestDNNTF = config.shuffleTestDNNTF
+    shuffleTrain = config.shuffleTrainDNNTF
+    shuffleTest = config.shuffleTestDNNTF
 
     #*************************************************
     # Setup variables and definitions- do not change.

diff --git a/SpectraLearnPredict/SpectraLearnPredict/slp/slp_tf.py b/SpectraLearnPredict/SpectraLearnPredict/slp/slp_tf.py
@@ -158,13 +158,7 @@ def input_fn(A, Cl2):
 ''' Train DNNClassifier model training via TensorFlow-skflow '''
 #********************************************************************************
 def trainDNNTF2(A, Cl, A_test, Cl_test, Root):
-    print('==========================================================================\n')
-    print('\033[1m Running Deep Neural Networks: tf.DNNClassifier - TensorFlow...\033[0m')
-    print('  Hidden layers:', dnntfDef.hidden_layers)
-    print('  Optimizer:',dnntfDef.optimizer,
-                '\n  Activation function:',dnntfDef.activation_function,
-                '\n  L2:',dnntfDef.l2_reg_strength,
-                '\n  Dropout:', dnntfDef.dropout_perc)
+    printInfo()
     import tensorflow as tf
     import tensorflow.contrib.learn as skflow
     from sklearn import preprocessing
@@ -200,13 +194,13 @@ def trainDNNTF2(A, Cl, A_test, Cl_test, Root):
             x={"x": np.array(A)},
             y=np.array(Cl2),
             num_epochs=None,
-            shuffle=dnntfDef.shuffleTrainDNNTF)
+            shuffle=dnntfDef.shuffleTrain)
 
     test_input_fn = tf.estimator.inputs.numpy_input_fn(
             x={"x": np.array(A_test)},
             y=np.array(Cl2_test),
             num_epochs=None,
-            shuffle=dnntfDef.shuffleTestDNNTF)
+            shuffle=dnntfDef.shuffleTest)
 
     validation_monitor = [skflow.monitors.ValidationMonitor(input_fn=test_input_fn,
                                                            eval_steps=1,
@@ -236,6 +230,8 @@ def trainDNNTF2(A, Cl, A_test, Cl_test, Root):
         print("  Retreaving training model from: ", model_directory,"\n")
 
     accuracy_score = clf.evaluate(input_fn=test_input_fn, steps=1)
+    printInfo()
+
     print('\n  ==================================')
     print('  \033[1mtf.DNNCl\033[0m - Accuracy')
     print('  ==================================')
@@ -246,6 +242,18 @@ def trainDNNTF2(A, Cl, A_test, Cl_test, Root):
 
     return clf, le
 
+def printInfo():
+    print('==========================================================================\n')
+    print('\033[1m Running Deep Neural Networks: tf.DNNClassifier - TensorFlow...\033[0m')
+    print('  Optimizer:',dnntfDef.optimizer,
+                '\n  Hidden layers:', dnntfDef.hidden_layers,
+                '\n  Activation function:',dnntfDef.activation_function,
+                '\n  L2:',dnntfDef.l2_reg_strength,
+                '\n  Dropout:', dnntfDef.dropout_perc,
+                '\n  Learning rate:', dnntfDef.learning_rate,
+                '\n  Shuffle Train:', dnntfDef.shuffleTrain,
+                '\n  Shuffle Test:', dnntfDef.shuffleTest,)
+
 #********************************************************************************
 ''' Predict using tf.estimator.DNNClassifier model via TensorFlow '''
 #********************************************************************************

diff --git a/SpectraLearnPredict/setup.py b/SpectraLearnPredict/setup.py
@@ -5,7 +5,7 @@
     packages=find_packages(),
     install_requires=['numpy', 'matplotlib', 'pandas', 'tensorflow', 'scikit-learn'],
     entry_points={'gui_scripts' : ['SpectraLearnPredict=SpectraLearnPredict.__main__:main']},
-    version='20171020a',
+    version='20171021a',
     description='Machine learning for spectral data',
     long_description= """ Machine learning for spectral data """,
     author='Nicola Ferralis',