linear_svm.py 文件源码

def loss_fn(W,b,x_data,y_target):
    logits = tf.subtract(tf.matmul(x_data, W),b)
    norm_term = tf.divide(tf.reduce_sum(tf.multiply(tf.transpose(W),W)),2)
    classification_loss = tf.reduce_mean(tf.maximum(0., tf.subtract(FLAGS.delta, tf.multiply(logits, y_target))))
    total_loss = tf.add(tf.multiply(FLAGS.C_param,classification_loss), tf.multiply(FLAGS.Reg_param,norm_term))
    return total_loss