部署 CNN：训练和测试准确度高，但预测准确度低

Question

Just starting out in ML and created my first CNN to detect the orientation of an image of a face. I got the training and testing accuracy up to around 96-99% over 2 different sets of 1000 pictures (128x128 RGB). However, when I go to predict an image from the test set on its own, the model rarely predicts correctly. I think there must be a difference in the way I load data into the model during testing vs prediction. Here is how I load the data into the model to train and test:

datagen = ImageDataGenerator()
train_it = datagen.flow_from_directory('twoThousandTransformed/', class_mode='categorical', batch_size=32, color_mode="rgb", target_size=(64,64))
val_it = datagen.flow_from_directory('validation/', class_mode='categorical', batch_size=32, color_mode="rgb", target_size=(64,64))
test_it = datagen.flow_from_directory('test/', class_mode='categorical', batch_size=32, color_mode='rgb', target_size=(64,64))

And here is how I load an image to make a prediction:

image_path='inputPicture/02001.png'
image = tf.keras.preprocessing.image.load_img(image_path)
input_arr = keras.preprocessing.image.img_to_array(image)
reshaped_image = np.resize(input_arr, (64,64,3))
input_arr = np.array([reshaped_image]) 
predictions = model.predict(input_arr)
print(predictions)
classes = np.argmax(predictions, axis = 1)
print(classes)

There must be some difference in the way the ImageDataGenerator handles the images vs. how I am doing it in the prediction. Can y'all help a noobie out? Thanks!

Edit: Below is my model

imageInput = Input(shape=(64,64,3))
conv1 = Conv2D(128, kernel_size=16, activation='relu')(imageInput)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(64, kernel_size=12, activation='relu')(pool1)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(64, kernel_size=4, activation='relu')(pool2)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
flat = Flatten()(pool3)
hidden1 = Dense(16, activation='relu')(flat)
hidden2 = Dense(16, activation='relu')(hidden1)
hidden3 = Dense(10, activation='relu')(hidden2)
output = Dense(4, activation='softmax')(hidden3)
model = Model(inputs=imageInput, outputs=output)
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
history = model.fit(train_it, steps_per_epoch=16, validation_data=val_it, validation_steps=8, epochs=25)
print('here we go!')
_, accuracy = model.evaluate(test_it)
print('Accuracy: %.2f' % (accuracy*100))

Answer 1

One thing you can try is to replicate the chosen image to resemble the batch size with which you trained the model. Also, because of such high training accuracy, it seems your model must be overfitting. So, try adding dropout or reducing the number of layers in your network, if the first thing doesn't work out.