minor typo
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@ -60,7 +60,7 @@ v = value_heads(h, is_training)
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p = policy_heads(h, is_training)
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# loss = tf.reduce_mean(tf.square(z-v)) - tf.multiply(pi, tf.log(tf.clip_by_value(tf.nn.softmax(p), 1e-8, tf.reduce_max(tf.nn.softmax(p)))))
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value_loss = tf.reduce_mean(tf.square(z - v))
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policy_loss = - tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=pi, logits=p))
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policy_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=pi, logits=p))
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reg = tf.add_n(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))
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total_loss = value_loss + policy_loss + reg
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