Обновление кода для более нового Pytorch и получение RuntimeError: Тензор должен иметь одинаковое количество измерений: получил 2 и 1

Question

Я изучаю ИИ, и теперь я пытаюсь понять и обновить код, который был сделан для более старой версии Pytorch. Я пробовал это для версий 1.0 и 1.5 с похожими результатами. Переменная устарела и при создании тензора следует использовать requires_grad=True. Также мне сообщили, что вместо torch.Tensor, который является псевдонимом torch.FloatTensor, я должен использовать torch.tensor, который должен автоматически определять тип данных.

Но есть ошибки, которые я не могу для обработки:

return map(lambda x: Variable(torch.cat(x, 0)), samples) #putting the samples in to pytorch variable

, а также при удалении Переменная

return map(lambda x: torch.cat(x, 0), samples) 

получает ту же ошибку:

RuntimeError: Тензоры должны иметь одинаковое количество измерений: получили 2 и 1

Код:

# AI for Self Driving Car

# Importing the libraries

import numpy as np
import random
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.autograd as autograd
from torch.autograd import Variable

# Creating the architecture of the Neural Network

class Network(nn.Module):

    def __init__(self, input_size, nb_action):
        super(Network, self).__init__()  #to use all tools from nn.Module
        self.input_size = input_size
        self.nb_action = nb_action
        self.fc1 = nn.Linear(input_size, 30) #creating the full connection between input & hidden layer
        self.fc2 = nn.Linear(30, nb_action)

    def forward(self, state):   #forward propagation
        x = F.relu(self.fc1(state))
        q_values = self.fc2(x)
        return q_values

# Implementing Experience Replay

class ReplayMemory(object):

    def __init__(self, capacity):
        self.capacity = capacity  #maximum number of events in memory
        self.memory = []

    def push(self, event):  #append new event in to the memory up to a maximum memory size
        self.memory.append(event)
        if len(self.memory) > self.capacity:
            del self.memory[0]

    def sample(self, batch_size):   #take random samples form the memory
        # if list=((1,2,3),(4,5,6)) then zip(*list)=((1,4),(2,5),(3,6))
        # events = (state,action,reward) we need (state1,state2), (action1,action2), (reward1,reward2)
        samples = zip(*random.sample(self.memory, batch_size))
        # torch.cat aligns everything as (state, action, reward)
# OLD-> return map(lambda x: Variable(torch.cat(x, 0)), samples) #putting samples in a pytorch variable
        return map(lambda x: torch.cat(x, 0), samples) #<-NEW    #putting samples in a pytorch variable

# Implementing Deep Q Learning

class Dqn():

    def __init__(self, input_size, nb_action, gamma):
        self.gamma = gamma
        self.reward_window = []
        self.model = Network(input_size, nb_action)
        self.memory = ReplayMemory(100000)   #memory capacity
        self.optimizer = optim.Adam(self.model.parameters(), lr = 0.001)
#OLD-> self.last_state = torch.Tensor(input_size).unsqueeze(0)
        self.last_state = torch.tensor(input_size, requires_grad=True).unsqueeze(0) #<-NEW
        self.last_action = 0
        self.last_reward = 0


    def select_action(self, state):
        with torch.no_grad():
            probs = F.softmax(self.model(state), dim=1)*100 # T=100
            action = probs.multinomial(num_samples=1)
            return action.data[0,0]

# obsolete
#    def select_action(self, state):
#        probs = F.softmax(self.model(Variable(state, volatile = True))*100) # T=100
#        action = probs.multinomial()
#        return action.data[0,0]        




    def learn(self, batch_state, batch_next_state, batch_reward, batch_action):
        outputs = self.model(batch_state).gather(1, batch_action.unsqueeze(1)).squeeze(1)
        next_outputs = self.model(batch_next_state).detach().max(1)[0]
        target = self.gamma*next_outputs + batch_reward
        td_loss = F.smooth_l1_loss(outputs, target)
        self.optimizer.zero_grad()
        td_loss.backward(retain_graph = True)
        self.optimizer.step()

    def update(self, reward, new_signal):
#OLD-> new_state = torch.Tensor(new_signal).float().unsqueeze(0)
        new_state = torch.tensor(new_signal, requires_grad=True, dtype=torch.float).float().unsqueeze(0)   #<-NEW

        self.memory.push((self.last_state, new_state, torch.LongTensor([int(self.last_action)]), torch.Tensor([self.last_reward])))
        action = self.select_action(new_state)
        if len(self.memory.memory) > 100:
            batch_state, batch_next_state, batch_action, batch_reward = self.memory.sample(100)
            self.learn(batch_state, batch_next_state, batch_reward, batch_action)
        self.last_action = action
        self.last_state = new_state
        self.last_reward = reward
        self.reward_window.append(reward)
        if len(self.reward_window) > 1000:
            del self.reward_window[0]
        return action

    def score(self):
        return sum(self.reward_window)/(len(self.reward_window)+1.)

    def save(self):
        torch.save({'state_dict': self.model.state_dict(),
                    'optimizer' : self.optimizer.state_dict(),
                   }, 'last_brain.pth')

    def load(self):
        if os.path.isfile('last_brain.pth'):
            print("=> loading checkpoint... ")
            checkpoint = torch.load('last_brain.pth')
            self.model.load_state_dict(checkpoint['state_dict'])
            self.optimizer.load_state_dict(checkpoint['optimizer'])
            print("done !")
        else:
            print("no checkpoint found...")