Boss lady Posts - Answer Overflow

Boss lady

•Created by Boss lady on 9/26/2024 in #firmware-and-baremetal

Normalizing Input Data for CNN Model in Image Recognition System on ESP32

still based on my project image recognition system that can analyze images of tissue samples, identify malignancies, and predict possible symptoms and causes. How do i train a CNN to accurately identify malignant tissues? My aim is to train a convolutional neural network (CNN) model for image recognition. But I keep encountering the error

    ValueError: Input data not properly normalized

    ValueError: Input data not properly normalized

Here's my code

    import tensorflow as tf
    from tensorflow.keras import layers, models
    
    model = models.Sequential([
        layers.Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 1)),
        layers.MaxPooling2D((2, 2)),
        layers.Conv2D(64, (3, 3), activation='relu'),
        layers.MaxPooling2D((2, 2)),
        layers.Flatten(),
        layers.Dense(64, activation='relu'),
        layers.Dense(1, activation='sigmoid')
    ])
    
    model.compile(optimizer='adam',
                  loss='binary_crossentropy',
                  metrics=['accuracy'])
    
    history = model.fit(train_images, train_labels, epochs=10, validation_data=(test_images, test_labels))

    import tensorflow as tf
    from tensorflow.keras import layers, models
    
    model = models.Sequential([
        layers.Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 1)),
        layers.MaxPooling2D((2, 2)),
        layers.Conv2D(64, (3, 3), activation='relu'),
        layers.MaxPooling2D((2, 2)),
        layers.Flatten(),
        layers.Dense(64, activation='relu'),
        layers.Dense(1, activation='sigmoid')
    ])
    
    model.compile(optimizer='adam',
                  loss='binary_crossentropy',
                  metrics=['accuracy'])
    
    history = model.fit(train_images, train_labels, epochs=10, validation_data=(test_images, test_labels))

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