fix style
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@ -210,7 +210,8 @@ class NeuralNet(object):
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feed_dict={self.vars['w1_']: w1, self.vars['w2_']: w2,
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self.vars['w3_']: w3, self.vars['b1_']: b1,
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self.vars['b2_']: b2, self.vars['b3_']: b3,
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self.vars['X_max_']: X_max, self.vars['X_min_']: X_min})
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self.vars['X_max_']: X_max,
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self.vars['X_min_']: X_min})
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if self.debug:
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LOG.info("Recommend phase, y before gradient descent: min %f, max %f, mean %f",
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np.min(y_before), np.max(y_before), np.mean(y_before))
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@ -227,7 +228,8 @@ class NeuralNet(object):
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feed_dict={self.vars['w1_']: w1, self.vars['w2_']: w2,
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self.vars['w3_']: w3, self.vars['b1_']: b1,
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self.vars['b2_']: b2, self.vars['b3_']: b3,
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self.vars['X_max_']: X_max, self.vars['X_min_']: X_min})
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self.vars['X_max_']: X_max,
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self.vars['X_min_']: X_min})
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LOG.info("Recommend phase, epoch %d, y: min %f, max %f, mean %f",
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i, np.min(y_train), np.max(y_train), np.mean(y_train))
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@ -235,8 +237,10 @@ class NeuralNet(object):
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feed_dict={self.vars['w1_']: w1, self.vars['w2_']: w2,
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self.vars['w3_']: w3, self.vars['b1_']: b1,
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self.vars['b2_']: b2, self.vars['b3_']: b3,
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self.vars['X_max_']: X_max, self.vars['X_min_']: X_min})
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X_recommend = sess.run(self.vars['x_bounded_'], feed_dict={self.vars['X_max_']: X_max, self.vars['X_min_']: X_min})
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self.vars['X_max_']: X_max,
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self.vars['X_min_']: X_min})
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X_recommend = sess.run(self.vars['x_bounded_'], feed_dict={self.vars['X_max_']: X_max,
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self.vars['X_min_']: X_min})
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res = NeuralNetResult(minl=y_recommend, minl_conf=X_recommend)
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if self.debug:
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