train_tensorflow.py 文件源码

def __init__(self,day,learning_rate = 1e-2):
        self.graph = tf.Graph()
        with self.graph.as_default():
            self.x_predict = tf.placeholder("float", [None,_feature_length])
            self.y_ = tf.placeholder("float", [None,1])
            #layer fc 1
            w_1 = tf.get_variable('all/w_1', [_feature_length,],
                                      initializer=tf.random_normal_initializer())
            #zoom layer
            w_zoom = tf.get_variable('all/w_zoom', [1,],
                                      initializer=tf.random_normal_initializer())
            #0.8~1.2
            self.zoom = tf.nn.sigmoid(w_zoom)*0.4+0.8
            self.percent = tf.nn.softmax(w_1)*self.zoom
            self.y_p = tf.reduce_sum(self.x_predict*self.percent,1)
            self.y_p = tf.reshape(self.y_p,[-1,1])
            self.error_rate = tf.reduce_mean(tf.abs(self.y_-self.y_p)/self.y_)
            self.mse = tf.reduce_mean(tf.abs(self.y_-self.y_p))
            #self.mse = self.error_rate
            self.optimizer = tf.train.AdamOptimizer(learning_rate)
            self.train_step = self.optimizer.minimize(self.mse)
            self.sess = tf.Session(graph = self.graph)
            self.sess.run(tf.global_variables_initializer())