Print all flags with pprint before training

Author: tobegit3hub

README.md

@@ -25,10 +25,11 @@ Following are the supported features.
   - [x] Validate auc
   - [x] Inference online
 - [x] Network Model
-  - [x] LR
-  - [x] DNN
-  - [x] Wide and deep
-  - [x] Customized
+  - [x] Logistic regression
+  - [x] Deep neural network
+  - [x] Convolution neural network
+  - [x] Wide and deep model
+  - [x] Customized models
 - [x] Others
   - [x] Checkpoint
   - [x] TensorBoard
@@ -82,6 +83,12 @@ If you use other dataset like [iris](./data/iris/), no need to modify the code.
 ./dense_classifier.py --train_tfrecords_file ./data/iris/iris_train.csv.tfrecords --validate_tfrecords_file ./data/iris/iris_test.csv.tfrecords --feature_size 4 --label_size 3
 ```
 
+If you want to use CNN model, try this command.
+
+```
+./dense_classifier.py --train_tfrecords_file ./data/lung/fa7a21165ae152b13def786e6afc3edf.dcm.csv.tfrecords --validate_tfrecords_file ./data/lung/fa7a21165ae152b13def786e6afc3edf.dcm.csv.tfrecords --feature_size 262144 --label_size 2 --batch_size 2 --validate_batch_size 2 --epoch_number -1 --model cnn
+```
+
 ### Export The Model
 
 After training, it will export the model automatically. Or you can export manually.

dense_classifier.py

@@ -5,6 +5,7 @@
 import math
 import numpy as np
 import os
+import pprint
 from sklearn import metrics
 import tensorflow as tf
 from tensorflow.contrib.session_bundle import exporter
@@ -74,6 +75,7 @@ def main():
   OUTPUT_PATH = FLAGS.output_path
   if not OUTPUT_PATH.startswith("fds://") and not os.path.exists(OUTPUT_PATH):
     os.makedirs(OUTPUT_PATH)
+  pprint.PrettyPrinter().pprint(FLAGS.__flags)
 
   # Process TFRecoreds files
   def read_and_decode(filename_queue):

sparse_classifier.py

@@ -5,6 +5,7 @@
 import math
 import numpy as np
 import os
+import pprint
 from sklearn import metrics
 import tensorflow as tf
 from tensorflow.contrib.session_bundle import exporter
@@ -75,6 +76,7 @@ def main():
   OUTPUT_PATH = FLAGS.output_path
   if not OUTPUT_PATH.startswith("fds://") and not os.path.exists(OUTPUT_PATH):
     os.makedirs(OUTPUT_PATH)
+  pprint.PrettyPrinter().pprint(FLAGS.__flags)
 
   # Read TFRecords files for training
   def read_and_decode(filename_queue):