tflearn_lstm.py 文件源码

def __init__(self, s_date):
        prev_bd = int(s_date[:6])-1
        prev_ed = int(s_date[9:15])-1
        if prev_bd%100 == 0: prev_bd -= 98
        if prev_ed%100 == 0: prev_ed -= 98
        pred_s_date = "%d01_%d01" % (prev_bd, prev_ed)
        prev_model = '../model/tflearn/lstm/%s' % pred_s_date
        self.model_dir = '../model/tflearn/lstm/%s' % s_date

        tf.reset_default_graph()
        tflearn.init_graph(gpu_memory_fraction=0.1)
        input_layer = tflearn.input_data(shape=[None, 30, 23], name='input')
        lstm1 = tflearn.lstm(input_layer, 23, dynamic=True, name='lstm1')
        dense1 = tflearn.fully_connected(lstm1, 1, name='dense1')
        output = tflearn.single_unit(dense1)
        regression = tflearn.regression(output, optimizer='adam', loss='mean_square',
                                metric='R2', learning_rate=0.001)
        self.estimators = tflearn.DNN(regression)
        if os.path.exists('%s/model.tfl' % prev_model):
            self.estimators.load('%s/model.tfl' % prev_model)