基于深度学习的mnist手写数字识别python源码

我很乐意回答你的问题！以下是基于深度学习的 mnist 手写数字识别的 Python 源码：

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

# 加载mnist数据集
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

# 数据预处理
x_train = x_train.astype("float32") / 255.0
x_test = x_test.astype("float32") / 255.0
x_train = np.expand_dims(x_train, -1)
x_test = np.expand_dims(x_test, -1)

# 建立模型
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"),
    ]
)

# 编译与训练模型
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
model.fit(x_train, y_train, batch_size=128, epochs=15, validation_split=0.1)

# 评估模型
score = model.evaluate(x_test, y_test, verbose=0)
print("Test loss:", score[0])
print("Test accuracy:", score[1])

希望这个答案对你有帮助！如果你还有其它问题，请继续提出。