DevHeads IoT Integration ServerDIIS
DevHeads IoT Integration Server•15mo ago
Boss lady

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

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))
Solution
your code should look like
import tensorflow as tf
from tensorflow.keras import layers, models

# Normalize the images
train_images = train_images / 255.0
test_images = test_images / 255.0

# Define the model
model = models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 1)),  # Adjust input_shape if using RGB
    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'])

# Train the model
history = model.fit(train_images, train_labels, epochs=10, validation_data=(test_images, test_labels))
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