# SETUP MODEL
CLASSES = 3
base_model = InceptionV3(weights='imagenet', include_top=False)
x=base_model.output
x=GlobalAveragePooling2D()(x)
preds=Dense(CLASSES,activation='softmax')(x) #final layer with softmax activation

model=Model(inputs=base_model.input,outputs=preds)

# transfer learning
for layer in base_model.layers:
    layer.trainable = False

model.compile(loss="categorical_crossentropy", optimizer='adam',metrics=["accuracy"])

# train the network
print("[INFO] training network...")
H = model.fit_generator(
    aug.flow(trainX, trainY, batch_size=BS),
    validation_data=(testX, testY),
    steps_per_epoch=len(trainX) // BS,
    epochs=EPOCHS, verbose=1, callbacks=[csv_logger])