rnn - Tensorflow를 사용한 예측을위한 다중 계층 LSTM

시계 시리즈 작업을 위해 이 레포 를 따르고있었습니다. RNN을 사용한 예측. 그러나이 리포지토리에서 테스트 오류는 약 24 %였습니다. 그래서 예측 정확도를 향상시키는 방법으로 다층 LSTM 모델을 시도했습니다. 코드는 다음과 같습니다.

import numpy as np
import tensorflow as tf
from tensorflow.contrib import rnn
import data_loader
import matplotlib.pyplot as plt

class SeriesPredictor(object):
    def __init__(self, input_dim, seq_size, lstm_size, num_layers=2):
        # Hyperparameters
        self.input_dim = input_dim
        self.seq_size = seq_size
        self.lstm_size = lstm_size
        self.num_layers = num_layers
        # Weight variables and input placeholders
        self.W_out = tf.Variable(tf.random_normal([lstm_size, 1]), name='W_out')
        self.b_out = tf.Variable(tf.random_normal([1]), name='b_out')
        self.x = tf.placeholder(tf.float32, [None, seq_size, input_dim])
        self.y = tf.placeholder(tf.float32, [None, seq_size])
        # Cost optimizer
        self.cost = tf.reduce_mean(tf.square(self.model(2) - self.y))
        self.train_op = tf.train.AdamOptimizer(learning_rate=0.01).minimize(self.cost)
        # Auxiliary ops
        self.saver = tf.train.Saver()
    def model(self, num_layers):
        """
        :param x: inputs of size [T, batch_size, input_size]
        :param W: matrix of fully-connected output layer weights
        :param b: vector of fully-connected output layer biases
        """
        cell = rnn.BasicLSTMCell(self.lstm_size)
        stacked_lstm_cell = tf.contrib.rnn.MultiRNNCell(
            [tf.contrib.rnn.DropoutWrapper(cell,
                                           output_keep_prob=0.8)
             for _ in range(num_layers)]
        )
        outputs, states = tf.nn.dynamic_rnn(stacked_lstm_cell, self.x, dtype=tf.float32)
        num_examples = tf.shape(self.x)[0]
        W_repeated = tf.tile(tf.expand_dims(self.W_out, 0), [num_examples, 1, 1])
        out = tf.matmul(outputs, W_repeated) + self.b_out
        out = tf.squeeze(out)
        return out
    def train(self, train_x, train_y, test_x, test_y):
        with tf.Session() as sess:
            tf.get_variable_scope().reuse_variables()
            sess.run(tf.global_variables_initializer())
            max_patience = 3
            patience = max_patience
            min_test_err = float('inf')
            step = 0
            while patience > 0:
                _, train_err = sess.run([self.train_op, self.cost], feed_dict={
                    self.x: train_x, self.y: train_y})
                if step % 100 == 0:
                    test_err = sess.run(self.cost, feed_dict={self.x: test_x, self.y: test_y})
                    print('step: {}\t\ttrain err: {}\t\ttest err: {}'.format(step, train_err, test_err))
                    if test_err < min_test_err:
                        min_test_err = test_err
                        patience = max_patience
                    else:
                        patience -= 1
                step += 1
            save_path = self.saver.save(
                sess, 'model.ckpt')
            print('Model saved to {}'.format(save_path))
    def test(self, sess, test_x):
        tf.get_variable_scope().reuse_variables()
        self.saver.restore(sess, './model.ckpt')
        output = sess.run(self.model(2), feed_dict={self.x: test_x})
        return output
    def plot_results(train_x, predictions, actual, filename):
        plt.figure()
        num_train = len(train_x)
        plt.plot(list(range(num_train)), train_x, color='b', label='training data')
        plt.plot(list(range(num_train, num_train + len(predictions))),
                 predictions, color='r', label='predicted')
        plt.plot(list(range(num_train, num_train + len(actual))),
                 actual, color='g', label='test data')
        plt.legend()
        if filename is not None:
            plt.savefig(filename)
        else:
            plt.show()

if __name__ == '__main__':
    seq_size = 5
    predictor = SeriesPredictor(input_dim=1, seq_size=seq_size, lstm_size=100)
    data = data_loader.load_series('international-airline-passengers.csv')
    train_data, actual_vals = data_loader.split_data(data)
    train_x, train_y = [], []
    for i in range(len(train_data) - seq_size - 1):
        train_x.append(np.expand_dims(train_data[i:i + seq_size], axis=1).tolist())
        train_y.append(train_data[i + 1:i + seq_size + 1])
    test_x, test_y = [], []
    for i in range(len(actual_vals) - seq_size - 1):
        test_x.append(np.expand_dims(actual_vals[i:i + seq_size], axis=1).tolist())
        test_y.append(actual_vals[i + 1:i + seq_size + 1])
    predictor.train(train_x, train_y, test_x, test_y)
    with tf.Session() as sess:
        predicted_vals = predictor.test(sess, test_x)[:, 0]
        print('predicted_vals', np.shape(predicted_vals))
        plot_results(train_data, predicted_vals, actual_vals, 'predictions.png')
        prev_seq = train_x[-1]
        predicted_vals = []
        for i in range(20):
            next_seq = predictor.test(sess, [prev_seq])
            predicted_vals.append(next_seq[-1])
            prev_seq = np.vstack((prev_seq[1:], next_seq[-1]))
        plot_results(train_data, predicted_vals, actual_vals, 'hallucinations.png')

그러나 다음과 같은 오류가 발생합니다 :

ValueError: Trying to share variable rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel, but specified shape (200, 400) and found shape (101, 400).

문제를 해결하기 위해 오랫동안 노력하고 있습니다. 그러나 이유를 얻지 못했습니다. 누구든지이 오류가 발생하는 이유를 알려주시겠습니까?

감사합니다!