调用cnn模型训练mnist 数据集

以下是使用Keras框架调用CNN模型训练MNIST数据集的代码示例：

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

# Load MNIST dataset
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

# Preprocess data
x_train = x_train.astype("float32") / 255
x_test = x_test.astype("float32") / 255
x_train = tf.expand_dims(x_train, -1)
x_test = tf.expand_dims(x_test, -1)
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)

# Define model architecture
model = keras.Sequential(
    [
        keras.Input(shape=(28, 28, 1)),
        layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Flatten(),
        layers.Dropout(0.5),
        layers.Dense(10, activation="softmax"),
    ]
)

# Compile model
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])

# Train model
model.fit(x_train, y_train, batch_size=128, epochs=5, validation_split=0.1)

# Evaluate model
test_loss, test_acc = model.evaluate(x_test, y_test)
print("Test accuracy:", test_acc)

这个模型包括两个卷积层和一个全连接层，其中包括一个dropout层以避免过拟合。训练过程中使用adam优化器和分类交叉熵损失函数。在训练5个epoch之后，我们可以得到大约0.99的测试准确率。