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))

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))

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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DIIS

DevHeads IoT Integration Server•17mo ago•

4 replies

Boss lady

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

    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))

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))

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

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

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