Tensorflow（二）单机多卡分布式训练

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建立分布式训练：

# 计算losses:
with tf.device('/gpu:0'):
    D_real_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_real,
                                                                         labels=tf.ones_like(D_logit_real)))
    D_fake_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_fake,
                                                                         labels=tf.zeros_like(D_logit_fake)))
    D_loss = D_fake_loss + D_real_loss
    D_solver = tf.train.AdamOptimizer().minimize(D_loss, var_list=theta_D)
with tf.device('/gpu:1'):
    G_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_fake, labels=tf.ones_like(D_logit_fake)))
    G_solver = tf.train.AdamOptimizer().minimize(G_loss, var_list=theta_G)

分布式训练结果：

未建立分布式训练时，默认使用全部四张显卡，得到的结果为：训练时间短一些（苦笑）