focalloss的tensorflow的多分类代码

以下是使用focal loss进行多分类的TensorFlow代码示例：

import tensorflow as tf

# 定义Focal Loss函数
def focal_loss(y_true, y_pred, gamma=2.0, alpha=0.25):
    # 计算交叉熵
    ce = tf.keras.losses.categorical_crossentropy(y_true, y_pred, from_logits=True)
    # 计算权重系数
    p_t = tf.where(tf.equal(y_true, 1), y_pred, 1 - y_pred)
    alpha_factor = tf.ones_like(y_true) * alpha
    alpha_t = tf.where(tf.equal(y_true, 1), alpha_factor, 1 - alpha_factor)
    weight = alpha_t * tf.pow(1 - p_t, gamma)
    # 计算Focal Loss
    focal_loss = weight * ce
    # 返回平均损失
    return tf.reduce_mean(focal_loss)

# 定义模型
model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(64, activation='relu', input_shape=(784,)),
    tf.keras.layers.Dense(10)
])

# 编译模型，使用Focal Loss作为损失函数
model.compile(optimizer='adam', loss=focal_loss, metrics=['accuracy'])

# 加载数据集，训练模型
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train = x_train.reshape(-1, 784) / 255.0
x_test = x_test.reshape(-1, 784) / 255.0
y_train = tf.keras.utils.to_categorical(y_train, num_classes=10)
y_test = tf.keras.utils.to_categorical(y_test, num_classes=10)
model.fit(x_train, y_train, epochs=10, batch_size=32, validation_data=(x_test, y_test))