tf.matmul(x, e)

这是 TensorFlow 中的矩阵乘法函数，用于计算两个张量 x 和 e 的矩阵乘积。x 和 e 必须具有相同的数据类型。如果 x 是形状为 [a, b] 的张量，e 是形状为 [b, c] 的张量，则函数返回一个形状为 [a, c] 的张量，表示它们的矩阵乘积。在矩阵乘法中，第一个矩阵的列数必须等于第二个矩阵的行数。

相关问题

tensorflow.python.framework.errors_impl.InvalidArgumentError: Matrix size-incompatible: In[0]: [720,1], In[1]: [720,1] [[node Tensordot_2/MatMul (defined at G:\Anconda\envs\tensorflow\lib\site-packages\tensorflow_core\python\framework\ops.py:1748) ]] Original stack trace for 'Tensordot_2/MatMul': File "E:\实验室数据\物理信息模型\PIML_Converter-main\PIML_Converter.py", line 524, in <module> model = PhysicsInformedNN(x0, u0, x1, u1, layers, dt, lb, ub, q, splitIdx1, splitIdx2, splitIdx3, Params) File "E:\实验室数据\物理信息模型\PIML_Converter-main\PIML_Converter.py", line 78, in __init__ self.U0_pred, self.V0_pred , self.V0_rlc= self.net_U0(self.x0_tf) # 2*N0 x q File "E:\实验室数据\物理信息模型\PIML_Converter-main\PIML_Converter.py", line 214, in net_U0 F_u_temp2_1 = tf.tensordot(yOff, RL , axes=1)

根据提供的堆栈跟踪信息，错误出现在代码的第524行，具体是在TensorFlow中的`tf.tensordot`操作上。`tf.tensordot`用于计算张量的张量积。

根据错误信息，输入的两个矩阵的大小为[720,1]，这表示两个720行1列的矩阵。然而，根据`tf.tensordot`的文档，`axes=1`参数将对第一个矩阵的最后一个维度和第二个矩阵的第一个维度进行乘法运算。因此，两个矩阵的最后一个维度必须相等。

请检查你的代码，并确保在调用`tf.tensordot`之前对输入矩阵进行了正确的形状处理。根据你的需求，你可能需要调整维度，或者选择正确的轴参数来执行所需的乘法运算。如果需要更多帮助，请提供相关代码片段以供进一步检查。

dqn代码 tensorflow

以下是一个DQN算法的TensorFlow代码示例：

import tensorflow as tf
import numpy as np
import gym

# 神经网络类
class DQN:
    def __init__(self, n_actions, n_features, learning_rate=0.01, reward_decay=0.9, e_greedy=0.9, replace_target_iter=300, memory_size=500, batch_size=32, e_greedy_increment=None):
        self.n_actions = n_actions
        self.n_features = n_features
        self.lr = learning_rate
        self.gamma = reward_decay
        self.epsilon_max = e_greedy
        self.replace_target_iter = replace_target_iter
        self.memory_size = memory_size
        self.batch_size = batch_size
        self.epsilon_increment = e_greedy_increment
        self.epsilon = 0 if e_greedy_increment is not None else self.epsilon_max

        self.learn_step_counter = 0
        self.memory = np.zeros((self.memory_size, n_features * 2 + 2))
        self.build_net()

        t_params = tf.get_collection('target_net_params')
        e_params = tf.get_collection('eval_net_params')
        self.replace_target_op = [tf.assign(t, e) for t, e in zip(t_params, e_params)]

        self.sess = tf.Session()
        self.sess.run(tf.global_variables_initializer())
        self.cost_his = []

    # 建立神经网络
    def build_net(self):
        self.s = tf.placeholder(tf.float32, [None, self.n_features], name='s')
        self.q_target = tf.placeholder(tf.float32, [None, self.n_actions], name='Q_target')
        with tf.variable_scope('eval_net'):
            c_names = ['eval_net_params', tf.GraphKeys.GLOBAL_VARIABLES]
            n_l1 = 10
            w_initializer = tf.random_normal_initializer(0., 0.3)
            b_initializer = tf.constant_initializer(0.1)
            with tf.variable_scope('l1'):
                w1 = tf.get_variable('w1', [self.n_features, n_l1], initializer=w_initializer, collections=c_names)
                b1 = tf.get_variable('b1', [1, n_l1], initializer=b_initializer, collections=c_names)
                l1 = tf.nn.relu(tf.matmul(self.s, w1) + b1)

            with tf.variable_scope('l2'):
                w2 = tf.get_variable('w2', [n_l1, self.n_actions], initializer=w_initializer, collections=c_names)
                b2 = tf.get_variable('b2', [1, self.n_actions], initializer=b_initializer, collections=c_names)
                self.q_eval = tf.matmul(l1, w2) + b2

        with tf.variable_scope('loss'):
            self.loss = tf.reduce_mean(tf.squared_difference(self.q_target, self.q_eval))
        with tf.variable_scope('train'):
            self._train_op = tf.train.RMSPropOptimizer(self.lr).minimize(self.loss)

        self.s_ = tf.placeholder(tf.float32, [None, self.n_features], name='s_')
        with tf.variable_scope('target_net'):
            c_names = ['target_net_params', tf.GraphKeys.GLOBAL_VARIABLES]
            n_l1 = 10
            w_initializer = tf.random_normal_initializer(0., 0.3)
            b_initializer = tf.constant_initializer(0.1)
            with tf.variable_scope('l1'):
                w1 = tf.get_variable('w1', [self.n_features, n_l1], initializer=w_initializer, collections=c_names)
                b1 = tf.get_variable('b1', [1, n_l1], initializer=b_initializer, collections=c_names)
                l1 = tf.nn.relu(tf.matmul(self.s_, w1) + b1)

            with tf.variable_scope('l2'):
                w2 = tf.get_variable('w2', [n_l1, self.n_actions], initializer=w_initializer, collections=c_names)
                b2 = tf.get_variable('b2', [1, self.n_actions], initializer=b_initializer, collections=c_names)
                self.q_next = tf.matmul(l1, w2) + b2

    # 记忆
    def store_transition(self, s, a, r, s_):
        if not hasattr(self, 'memory_counter'):
            self.memory_counter = 0
        transition = np.hstack((s, [a, r], s_))
        index = self.memory_counter % self.memory_size
        self.memory[index, :] = transition
        self.memory_counter += 1

    # 选择动作
    def choose_action(self, observation):
        observation = observation[np.newaxis, :]
        if np.random.uniform() < self.epsilon:
            actions_value = self.sess.run(self.q_eval, feed_dict={self.s: observation})
            action = np.argmax(actions_value)
        else:
            action = np.random.randint(0, self.n_actions)
        return action

    # 学习
    def learn(self):
        if self.learn_step_counter % self.replace_target_iter == 0:
            self.sess.run(self.replace_target_op)
            print('\ntarget_params_replaced\n')

        if self.memory_counter > self.memory_size:
            sample_index = np.random.choice(self.memory_size, size=self.batch_size)
        else:
            sample_index = np.random.choice(self.memory_counter, size=self.batch_size)
        batch_memory = self.memory[sample_index, :]

        q_next, q_eval4next = self.sess.run(
            [self.q_next, self.q_eval],
            feed_dict={
                self.s_: batch_memory[:, -self.n_features:],
                self.s: batch_memory[:, :self.n_features],
            })
        q_eval = self.sess.run(self.q_eval, {self.s: batch_memory[:, :self.n_features]})

        q_target = q_eval.copy()
        batch_index = np.arange(self.batch_size, dtype=np.int32)
        eval_act_index = batch_memory[:, self.n_features].astype(int)
        reward = batch_memory[:, self.n_features + 1]

        q_target[batch_index, eval_act_index] = reward + self.gamma * np.max(q_next, axis=1)
        _, self.cost = self.sess.run([self._train_op, self.loss],
                                     feed_dict={self.s: batch_memory[:, :self.n_features],
                                                self.q_target: q_target})
        self.cost_his.append(self.cost)

        self.epsilon = self.epsilon + self.epsilon_increment if self.epsilon < self.epsilon_max else self.epsilon_max
        self.learn_step_counter += 1

# 训练网络
def main():
    env = gym.make('CartPole-v0')
    env = env.unwrapped
    n_actions = env.action_space.n
    n_features = env.observation_space.shape[0]

    dqn = DQN(n_actions=n_actions, n_features=n_features, learning_rate=0.01, reward_decay=0.9, e_greedy=0.9, replace_target_iter=100, memory_size=2000, e_greedy_increment=0.001, )

    for i_episode in range(400):
        observation = env.reset()
        ep_r = 0
        while True:
            env.render()
            action = dqn.choose_action(observation)

            observation_, reward, done, info = env.step(action)

            x, x_dot, theta, theta_dot = observation_
            r1 = (env.x_threshold - abs(x)) / env.x_threshold - 0.8
            r2 = (env.theta_threshold_radians - abs(theta)) / env.theta_threshold_radians - 0.5
            reward = r1 + r2

            dqn.store_transition(observation, action, reward, observation_)

            ep_r += reward
            if dqn.memory_counter > dqn.memory_size:
                dqn.learn()
                if done:
                    print('episode: ', i_episode, 'ep_r: ', round(ep_r, 2), ' epsilon: ', round(dqn.epsilon, 2))
            if done:
                break
            observation = observation_

    print('game over')
    env.close()

if __name__ == '__main__':
    main()

这个代码演示了如何使用DQN算法来解决CartPole-v0游戏。在代码中，定义了一个DQN类，其中包含了神经网络的构建，记忆的存储，动作的选择和学习。在主函数中，创建了CartPole-v0环境，并使用DQN算法进行训练。