tensorflow2.0训练mnist

首先，需要导入tensorflow2.0和mnist数据集：

import tensorflow as tf
from tensorflow.keras.datasets import mnist

然后，加载mnist数据集并进行预处理：

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

接下来，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, activation='softmax')
])

然后，编译模型并进行训练：

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=5)

最后，评估模型并进行预测：

model.evaluate(x_test, y_test, verbose=2)

predictions = model.predict(x_test[:5])
print(np.argmax(predictions, axis=1))
print(y_test[:5])