Updated training for new KERAS API

TrainingModelKeras.py

@@ -7,7 +7,6 @@ import matplotlib.pyplot as plt
 from tensorflow import keras
 from time import time
 from PIL import ImageFile
-
 ImageFile.LOAD_TRUNCATED_IMAGES = True
 
 input_shape = (224, 224, 3)
@@ -18,7 +17,7 @@ from keras.preprocessing.image import ImageDataGenerator
 from keras.applications.inception_v3 import preprocess_input
 
 train_idg = ImageDataGenerator(
-    horizontal_flip=True,
+    # horizontal_flip=True,
     preprocessing_function=preprocess_input
 )
 train_gen = train_idg.flow_from_directory(
@@ -28,7 +27,7 @@ train_gen = train_idg.flow_from_directory(
 )
 
 val_idg = ImageDataGenerator(
-    horizontal_flip=True,
+    # horizontal_flip=True,
     preprocessing_function=preprocess_input
 )
 
@@ -88,7 +87,7 @@ checkpoint = ModelCheckpoint(file_path, monitor='val_acc', verbose=1, save_best_
 early = EarlyStopping(monitor="val_acc", mode="max", patience=15)
 
 tensorboard = TensorBoard(
-    log_dir="logs/" + model_name + "{}".format(time()), histogram_freq=0, batch_size=32,
+    log_dir="logs/" + model_name + "{}".format(time()), histogram_freq=0, batch_size=batch_size,
     write_graph=True,
     write_grads=True,
     write_images=True,
@@ -100,6 +99,8 @@ callbacks_list = [checkpoint, early, tensorboard]  # early
 history = model.fit_generator(
     train_gen,
     validation_data=val_gen,
+    steps_per_epoch=len(train_gen),
+    validation_steps=len(val_gen),
     epochs=2,
     shuffle=True,
     verbose=True,
@@ -119,10 +120,11 @@ test_gen = test_idg.flow_from_directory(
 )
 len(test_gen.filenames)
 
-score = model.evaluate_generator(test_gen, workers=1)
+score = model.evaluate_generator(test_gen, workers=1, steps=len(test_gen))
 
 # predicts
-predicts = model.predict_generator(test_gen, verbose=True, workers=1)
+predicts = model.predict_generator(test_gen, verbose=True, workers=1, steps=len(test_gen))
+
 
 keras_file = 'finished.h5'
 keras.models.save_model(model, keras_file)

TransferLearningKeras.py

@@ -122,10 +122,11 @@ test_gen = test_idg.flow_from_directory(
 
 len(test_gen.filenames)
 
-score = model.evaluate_generator(test_gen, workers=1)
+score = model.evaluate_generator(test_gen, workers=1, steps=len(test_gen))
 
 # predicts
-predicts = model.predict_generator(test_gen, verbose=True, workers=1)
+predicts = model.predict_generator(test_gen, verbose=True, workers=1, steps=len(test_gen))
+
 
 print("Loss: ", score[0], "Accuracy: ", score[1])
 print(score)