神经网络多分类代码怎么写

为您提供一个基本的神经网络多分类代码：

import tensorflow as tf

# 准备数据集
train_dataset = tf.data.Dataset.from_tensor_slices((train_images, train_labels)).batch(batch_size)
test_dataset = tf.data.Dataset.from_tensor_slices((test_images, test_labels)).batch(batch_size)

# 定义模型
model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),   # 将输入的图片矩阵48x48改成1D向量输入
    tf.keras.layers.Dense(128, activation='relu'),   # 全连接层1，128个节点，ReLU激活函数
    tf.keras.layers.Dropout(0.2),   # Dropout正则化层
    tf.keras.layers.Dense(10)   # 全连接层2，10个节点，输出分类评分
])

# 定义损失函数
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)

# 设置优化器
optimizer = tf.keras.optimizers.Adam()

# 定义评估指标
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')
test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')

# 定义训练函数
@tf.function
def train_step(images, labels):
    with tf.GradientTape() as tape:
        predictions = model(images, training=True)   # 在训练模式下进行预测
        loss = loss_fn(labels, predictions)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    train_loss(loss)
    train_accuracy(labels, predictions)

# 定义测试函数
@tf.function
def test_step(images, labels):
    predictions = model(images, training=False)   # 在测试模式下进行预测
    t_loss = loss_fn(labels, predictions)
    test_loss(t_loss)
    test_accuracy(labels, predictions)

# 开始训练
for epoch in range(num_epochs):
    train_loss.reset_states()
    train_accuracy.reset_states()
    test_loss.reset_states()
    test_accuracy.reset_states()
    for images, labels in train_dataset:
        train_step(images, labels)
    for test_images, test_labels in test_dataset:
        test_step(test_images, test_labels)
    print('Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'.format(epoch+1,
                 train_loss.result(),
                 train_accuracy.result()*100,
                 test_loss.result(),
                 test_accuracy.result()*100))

希望这个代码对您有所帮助！