人工智能算法快速入门：CNN教程

import numpy as np

def convolve(image, kernel, stride=1, padding=0):
    # Add padding to the input image
    image_padded = np.pad(image, [(padding, padding), (padding, padding)], mode='constant', constant_values=0)
    
    # Calculate output dimensions
    output_height = (image.shape[0] - kernel.shape[0] + 2 * padding) // stride + 1
    output_width = (image.shape[1] - kernel.shape[1] + 2 * padding) // stride + 1
    
    # Initialize output
    output = np.zeros((output_height, output_width))
    
    # Perform convolution
    for i in range(0, output_height, stride):
        for j in range(0, output_width, stride):
            output[i, j] = np.sum(image_padded[i:i+kernel.shape[0], j:j+kernel.shape[1]] * kernel)
    
    return output

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def max_pooling(image, size=2, stride=2):
    output_height = (image.shape[0] - size) // stride + 1
    output_width = (image.shape[1] - size) // stride + 1
    output = np.zeros((output_height, output_width))
    
    for i in range(0, output_height, stride):
        for j in range(0, output_width, stride):
            output[i, j] = np.max(image[i:i+size, j:j+size])
    
    return output

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import tensorflow as tf
from tensorflow.keras import layers, models
import matplotlib.pyplot as plt

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# Load the MNIST dataset
(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()

# Normalize the images
train_images = train_images.reshape((60000, 28, 28, 1)).astype('float32') / 255
test_images = test_images.reshape((10000, 28, 28, 1)).astype('float32') / 255

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model = models.Sequential([
    layers.Conv2D(32, (3, 3), activatinotallow='relu', input_shape=(28, 28, 1)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activatinotallow='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activatinotallow='relu'),
    layers.Flatten(),
    layers.Dense(64, activatinotallow='relu'),
    layers.Dense(10, activatinotallow='softmax')
])
model.summary()

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model.compile(optimizer='adam', 
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
history = model.fit(train_images, train_labels, epochs=5, 
                    validation_data=(test_images, test_labels))

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test_loss, test_acc = model.evaluate(test_images, test_labels)
print(f"Test accuracy: {test_acc}")

plt.plot(history.history['accuracy'], label='accuracy')
plt.plot(history.history['val_accuracy'], label='val_accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend(loc='lower right')
plt.show()

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