fix typos
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@ -45,12 +45,12 @@ class NeuralNet(object):
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self.n_input = n_input
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self.debug = debug
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self.debug_interval = debug_interval
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self.learning_rate = 0.01
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self.learning_rate = learning_rate
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self.batch_size = batch_size
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self.explore_iters = explore_iters
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self.noise_scale_begin = noise_scale_begin
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self.noise_scale_end = noise_scale_end
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self.optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
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self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate)
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# input X is placeholder, weights are variables.
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self.model = keras.Sequential([
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layers.Dense(64, activation=tf.nn.relu, input_shape=[n_input]),
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@ -93,7 +93,7 @@ class NeuralNet(object):
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l1_ = tf.nn.relu(tf.add(tf.matmul(x_, w1_), b1_))
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l2_ = tf.nn.relu(tf.add(tf.matmul(l1_, w2_), b2_))
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y_ = tf.add(tf.matmul(l2_, w3_), b3_)
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optimizer_ = tf.train.AdamOptimizer(learning_rate=0.01)
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optimizer_ = tf.train.AdamOptimizer(learning_rate=self.learning_rate)
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train_ = optimizer_.minimize(y_)
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self.vars['x_'] = x_
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@ -155,8 +155,8 @@ class NeuralNet(object):
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w3 = self.add_noise(w3)
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b3 = self.add_noise(b3)
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if self.debug:
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y_predict = self.predict(X_start)
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if self.debug:
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LOG.info("Recommend phase, y prediction: min %f, max %f, mean %f",
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np.min(y_predict), np.max(y_predict), np.mean(y_predict))
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@ -204,7 +204,7 @@ class NeuralNet(object):
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if self.debug:
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LOG.info("Recommend phase, epoch %d, y after gradient descent: \
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min %f, max %f, mean %f", recommend_epochs, np.mean(y_recommend),
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min %f, max %f, mean %f", recommend_epochs, np.min(y_recommend),
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np.max(y_recommend), np.mean(y_recommend))
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self.recommend_iters += 1
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