Handwritten digit recognition using convolutional neural networks

The MNIST data set is composed of handwritten digits and includes 60,000 training samples and 10,000 testing samples. Each sample is a 28x28 pixel grayscale image representing a number from 0 to 9.

Convolutional neural network (CNN) is a model used for image classification in deep learning. It extracts image features through convolutional layers and pooling layers, and uses fully connected layers for classification.

Below I will introduce how to use Python and TensorFlow to implement a simple CNN model to classify the MNIST data set.

First, we need to import the necessary libraries and MNIST dataset:

import tensorflow as tf from tensorflow.keras.datasets import mnist # 加载MNIST数据集 (x_train, y_train), (x_test, y_test) = mnist.load_data()

Next, we need to normalize the image data and convert the label data into unique Hot encoding format:

# 归一化图像数据 x_train = x_train / 255.0 x_test = x_test / 255.0 # 将标签数据转换为独热编码格式 y_train = tf.keras.utils.to_categorical(y_train, num_classes=10) y_test = tf.keras.utils.to_categorical(y_test, num_classes=10)

Then, we define the CNN model. This model includes two convolutional layers and two pooling layers, as well as a fully connected layer. We use the ReLU activation function and the Softmax activation function in the last layer for classification. The code is as follows:

model = tf.keras.models.Sequential([ # 第一个卷积层 tf.keras.layers.Conv2D(filters=32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)), tf.keras.layers.MaxPooling2D(pool_size=(2, 2)), # 第二个卷积层 tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation='relu'), tf.keras.layers.MaxPooling2D(pool_size=(2, 2)), # 将特征图展平 tf.keras.layers.Flatten(), # 全连接层 tf.keras.layers.Dense(units=128, activation='relu'), # 输出层 tf.keras.layers.Dense(units=10, activation='softmax') ])

Next, we need to compile the model and specify the loss function, optimizer and evaluation metrics:

# 编译模型 model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

Finally, we train the model and test it:

# 训练模型 model.fit(x_train.reshape(-1, 28, 28, 1), y_train, epochs=5, batch_size=32) # 测试模型 score = model.evaluate(x_test.reshape(-1, 28, 28, 1), y_test, verbose=0) print('Test loss:', score[0]) print('Test accuracy:', score[1])

After running the complete code, we can see that the model's test accuracy is approximately 99%.

To summarize, the steps to use a convolutional neural network to classify the MNIST dataset are as follows:

1. Load the MNIST dataset and proceed Preprocessing, including normalization and one-hot encoding;

2. Define the CNN model, including convolutional layer, pooling layer and fully connected layer, and specify the activation function;

3. Compile the model, specify the loss function, optimizer and evaluation indicators;

4. Train the model and test it on the test set.

The above is a simple example that can be modified and optimized according to specific circumstances.

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