use ignite for exp2

exp2/source/predict.py

@@ -1,3 +1,4 @@
+from pathlib import Path
 import tensorflow as tf
 from PIL import Image
 import numpy as np
@@ -33,6 +34,7 @@ class Predict(object):
 
 if __name__ == "__main__":
     app = Predict()
-    app.predict('./test_images/0.png') 
-    app.predict('./test_images/1.png')
-    app.predict('./test_images/4.png')
+    images_dir = Path(__file__).resolve().parent.parent / 'test_images'
+    app.predict(images_dir / '0.png') 
+    app.predict(images_dir / '1.png')
+    app.predict(images_dir / '4.png')

exp2/source/train.py

@@ -1,12 +1,6 @@
 from pathlib import Path
-import sys
 import tensorflow as tf
-import keras
-from keras import datasets, layers, models
-
-sys.path.append(str(Path(__file__).resolve().parent.parent.parent))
-import tensorflow_gpu_util
-
+from tensor.keras import datasets, layers, models
 
 class CNN(object):
     def __init__(self):
@@ -46,7 +40,7 @@ class Train:
     def train(self):
         check_path = Path(__file__).resolve().parent.parent / 'models' / 'cnn.ckpt'
         # period 每隔5epoch保存一次
-        save_model_cb = keras.callbacks.ModelCheckpoint(
+        save_model_cb = tf.keras.callbacks.ModelCheckpoint(
             str(check_path), save_weights_only=True, verbose=1, period=5)
         self.cnn.model.compile(optimizer='adam',
                                loss='sparse_categorical_crossentropy',
@@ -58,6 +52,5 @@ class Train:
         print("准确率: %.4f, 共测试了%d张图片 " % (test_acc, len(self.data.test_labels)))
 
 if __name__ == "__main__":
-    tensorflow_gpu_util.print_gpu_availability()
-    #app = Train()
-    #app.train()
+    app = Train()
+    app.train()