自定义解码预测 keras model

Question

some days ago I started with ML as I wanted to do a hcaptcha solver. I have everything ready, I just need to train a model that will classify the captcha images so I can send a request with the good answer and get the captcha token.

I've looked into some tutorials on how to train my own model with several classes. I have it the following way:

1 trainer folder, 1 validation folder and 1 testing folder. On the trainer and validation folder there is more subfolders named airplane, truck, boat, train,... each one containing aprox 20 images. On the testing folder, some random images related with the classes I have.

I have trained the model and it seems like I'm getting a 1 accuracy. Then I get some of the random testing images and try to predict them using this saved model. It does it's job and predicts them, returning an array of numbers. The thing is I don't know how to decode those predictions nor how to see the classes list with his representative integer before predicting.

I'm super new on this so I'm sure anything will help:)

My code below:

import os

from keras.preprocessing import image
from keras.models import Sequential
from keras import layers
from keras.models import load_model

import numpy as np

trainer_path = "./img/trainer"
validator_path = "./img/validator"
testing_path = "./img/tester"

WIDTH = 128
HEIGHT = 128
BATCH = 30
EPOCHS = 15

train_dataset = image.image_dataset_from_directory(
    trainer_path,
    label_mode="int",
    batch_size=BATCH,
    image_size=(WIDTH, HEIGHT)
)

validator_dataset = image.image_dataset_from_directory(
    validator_path,
    label_mode="int",
    batch_size=BATCH,
    image_size=(WIDTH, HEIGHT)
)


model = Sequential([
    layers.Input((WIDTH, HEIGHT, 3)),
    layers.Conv2D(16, 3, padding="same"),
    layers.Conv2D(32, 3, padding="same"),
    layers.MaxPooling2D(),
    layers.Flatten(),
    layers.Dense(10)
])

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

model_fit = model.fit(
    train_dataset,
    epochs=EPOCHS,
    validation_data=validator_dataset,
    verbose=2
)

#loading the saved model
model = load_model("./model")
 
for i in os.listdir(testing_path):
    
    img = image.load_img(testing_path + "/" + i, target_size=(WIDTH, HEIGHT, 3))
    
    img_array = image.img_to_array(img)
    
    img_batch = np.expand_dims(img_array, axis=0)
            
    prediction = model.predict(img_batch)
    
    print(prediction)
    print()

Output example:

[[  875.5614    3123.8257    1521.7046      90.056526   335.5274
   -785.3671    1075.9199    1105.3068     -14.917503 -3745.6494  ]]

Answer 1

You have to apply activation function on last Dense layer, if you want to classify the image it should be softmax (you will get probabilities for all classes), here is the link:

https://keras.io/api/layers/activations/

When it comes to class names it should be sorted by alphanumerical values, you can also pass class_names argument, here is the link to arguments of this function:

https://www.tensorflow.org/api_docs/python/tf/keras/utils/image_dataset_from_directory