生成基于栅格地图的双向LSTM的路径规划代码输出路径，输入数据维度要正确

好的，下面是一个基于栅格地图的双向LSTM的路径规划代码，可以输出路径。需要注意的是，输入数据维度必须正确，否则会出现错误。

import numpy as np
import tensorflow as tf

# 定义双向LSTM模型
class BiLSTM:
    def __init__(self, input_size, hidden_size, output_size):
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        
        # 定义前向LSTM
        self.lstm_fw_cell = tf.nn.rnn_cell.LSTMCell(self.hidden_size)
        
        # 定义后向LSTM
        self.lstm_bw_cell = tf.nn.rnn_cell.LSTMCell(self.hidden_size)
        
        # 定义前向LSTM的初始状态
        self.initial_fw_state = self.lstm_fw_cell.zero_state(batch_size, tf.float32)
        
        # 定义后向LSTM的初始状态
        self.initial_bw_state = self.lstm_bw_cell.zero_state(batch_size, tf.float32)
        
        # 定义前向LSTM的权重和偏置
        self.W_fw = tf.Variable(tf.truncated_normal([self.input_size, self.hidden_size], stddev=0.1))
        self.b_fw = tf.Variable(tf.constant(0.1, shape=[self.hidden_size]))
        
        # 定义后向LSTM的权重和偏置
        self.W_bw = tf.Variable(tf.truncated_normal([self.input_size, self.hidden_size], stddev=0.1))
        self.b_bw = tf.Variable(tf.constant(0.1, shape=[self.hidden_size]))
        
        # 定义输出层的权重和偏置
        self.W_out = tf.Variable(tf.truncated_normal([self.hidden_size * 2, self.output_size], stddev=0.1))
        self.b_out = tf.Variable(tf.constant(0.1, shape=[self.output_size]))
    
    # 定义前向LSTM的计算图
    def forward(self, inputs):
        # 将输入数据转换为LSTM的输入格式
        inputs = tf.reshape(inputs, [batch_size, -1, self.input_size])
        
        # 将输入数据转换为LSTM需要的格式
        inputs = tf.transpose(inputs, [1, 0, 2])
        
        # 将输入数据按时间步展开，并计算前向LSTM
        outputs_fw, states_fw = tf.nn.dynamic_rnn(self.lstm_fw_cell, inputs, initial_state=self.initial_fw_state, time_major=True)
        
        # 将输入数据反向展开，并计算后向LSTM
        outputs_bw, states_bw = tf.nn.dynamic_rnn(self.lstm_bw_cell, inputs[::-1], initial_state=self.initial_bw_state, time_major=True)
        
        # 将前向和后向LSTM的输出按时间步合并
        outputs = tf.concat([outputs_fw, outputs_bw[::-1]], axis=2)
        
        # 将输出展开为二维矩阵
        outputs = tf.reshape(outputs, [-1, self.hidden_size * 2])
        
        # 计算输出层的输出
        outputs = tf.matmul(outputs, self.W_out) + self.b_out
        
        # 返回输出
        return outputs
    
    # 定义损失函数
    def loss(self, output, target):
        # 计算交叉熵损失
        loss = tf.nn.softmax_cross_entropy_with_logits(logits=output, labels=target)
        
        # 计算平均损失
        loss = tf.reduce_mean(loss)
        
        # 返回损失
        return loss
    
    # 定义训练函数
    def train(self, loss, learning_rate):
        # 定义优化器
        optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
        
        # 定义训练操作
        train_op = optimizer.minimize(loss)
        
        # 返回训练操作
        return train_op
    
    # 定义预测函数
    def predict(self, output):
        # 计算预测结果
        prediction = tf.argmax(tf.nn.softmax(output), axis=1)
        
        # 返回预测结果
        return prediction
        

# 定义路径规划函数
def path_planning(grid_map, start_pos, end_pos):
    # 将栅格地图转换为输入数据
    input_data = np.array(grid_map)
    input_data = np.expand_dims(input_data, axis=2)
    
    # 获取输入数据的形状
    input_shape = input_data.shape
    
    # 定义模型参数
    input_size = 1
    hidden_size = 128
    output_size = 2
    learning_rate = 0.001
    num_epochs = 1000
    
    # 定义模型
    model = BiLSTM(input_size, hidden_size, output_size)
    
    # 定义输入和输出
    inputs = tf.placeholder(tf.float32, [None, input_shape[1], input_shape[2]])
    targets = tf.placeholder(tf.float32, [None, output_size])
    
    # 定义前向计算
    output = model.forward(inputs)
    
    # 定义损失函数
    loss = model.loss(output, targets)
    
    # 定义训练操作
    train_op = model.train(loss, learning_rate)
    
    # 定义预测操作
    predict_op = model.predict(output)
    
    # 创建会话
    with tf.Session() as sess:
        # 初始化变量
        sess.run(tf.global_variables_initializer())
        
        # 训练模型
        for epoch in range(num_epochs):
            # 获取批次数据
            x_batch = input_data
            y_batch = np.array([[start_pos[0], start_pos[1]], [end_pos[0], end_pos[1]]])
            
            # 计算损失和预测结果
            loss_val, _ = sess.run([loss, train_op], feed_dict={inputs: x_batch, targets: y_batch})
            pred = sess.run(predict_op, feed_dict={inputs: x_batch})
            
            # 打印损失和预测结果
            print("Epoch: {}, Loss: {}, Predictions: {}".format(epoch+1, loss_val, pred))
        
        # 获取路径
        path = []
        pos = start_pos
        while pos != end_pos:
            path.append(pos)
            pred_val = sess.run(predict_op, feed_dict={inputs: np.expand_dims(input_data[pos[0], pos[1]], axis=0)})
            if pred_val == 0:
                pos = (pos[0], pos[1]-1)
            elif pred_val == 1:
                pos = (pos[0], pos[1]+1)
            elif pred_val == 2:
                pos = (pos[0]-1, pos[1])
            elif pred_val == 3:
                pos = (pos[0]+1, pos[1])
        
        # 添加终点
        path.append(end_pos)
        
        # 返回路径
        return path

其中，`grid_map` 为栅格地图，`start_pos` 为起点坐标，`end_pos` 为终点坐标。`path_planning` 函数返回路径。