jupyter notebook 识别minst数据集 完整代码

好的，以下是使用Jupyter Notebook识别MNIST数据集的完整Python代码。请确保已经安装了Jupyter Notebook和所需的Python库，例如numpy、matplotlib和tensorflow。

# 导入所需的库
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf

# 下载并导入MNIST数据集
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# 将图像数据规范化到0到1之间
x_train, x_test = x_train / 255.0, x_test / 255.0

# 构建模型
model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10)
])

# 编译模型
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

# 训练模型
history = model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))

# 评估模型
model.evaluate(x_test,  y_test, verbose=2)

# 可视化训练结果
plt.plot(history.history['accuracy'], label='accuracy')
plt.plot(history.history['val_accuracy'], label = 'val_accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.ylim([0.5, 1])
plt.legend(loc='lower right')
plt.show()

# 使用模型进行预测
probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])
predictions = probability_model.predict(x_test)

# 显示预测结果
for i in range(10):
    plt.imshow(x_test[i], cmap=plt.cm.binary)
    plt.title("Predicted: " + str(np.argmax(predictions[i])))
    plt.show()

希望这能够帮助到你！