cnn model 用于二进制分类，86% val_accuracy 总是返回 1

Question

I created a CNN model for binary classification. I used a balanced database of 300 images. I know it's a small database but I used data augmentation. After fitting the model I got 86% val_accuracy on the validation set, but when I wanted to print the probability for each picture, I got probability 1 for most pictures from the first class and even all probabilities are >0.5, and probability 1 for all images from the second class.

This is my model

model = keras.Sequential([
layers.InputLayer(input_shape=[128, 128, 3]),

preprocessing.Rescaling(scale=1/255),
preprocessing.RandomContrast(factor=0.10),
preprocessing.RandomFlip(mode='horizontal'),
preprocessing.RandomRotation(factor=0.10),

layers.BatchNormalization(renorm=True),
layers.Conv2D(filters=64, kernel_size=3, activation='relu', padding='same'),
layers.MaxPool2D(),

layers.BatchNormalization(renorm=True),
layers.Conv2D(filters=128, kernel_size=3, activation='relu', padding='same'),
layers.MaxPool2D(),

layers.BatchNormalization(renorm=True),
layers.Conv2D(filters=256, kernel_size=3, activation='relu', padding='same'),
layers.Conv2D(filters=256, kernel_size=3, activation='relu', padding='same'),
layers.MaxPool2D(),

layers.BatchNormalization(renorm=True),
layers.Flatten(),
layers.Dense(8, activation='relu'),
layers.Dense(1, activation='sigmoid'),])

This is the accuracy plot

Thank you guys

Answer 1

I think the preprocessor function scales the pixel values between -1 and +1. However you rescaled your images for training between 0 and 1. try replacing

image = preprocess_input(image)

with

image=image/255

see if that works