脳は世界をどうみているのか

import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import torch.nn.functional as F

# Define the Variational Autoencoder
class VAE(nn.Module):
    def __init__(self):
        super(VAE, self).__init__()
        self.fc1 = nn.Linear(784, 400)
        self.fc21 = nn.Linear(400, 20)  # mean
        self.fc22 = nn.Linear(400, 20)  # log variance
        self.fc3 = nn.Linear(20, 400)
        self.fc4 = nn.Linear(400, 784)

    def encode(self, x):
        h1 = F.relu(self.fc1(x))
        return self.fc21(h1), self.fc22(h1)

    def reparameterize(self, mu, logvar):
        std = torch.exp(0.5*logvar)
        eps = torch.randn_like(std)
        return mu + eps*std

    def decode(self, z):
        h3 = F.relu(self.fc3(z))
        return torch.sigmoid(self.fc4(h3))

    def forward(self, x):
        mu, logvar = self.encode(x.view(-1, 784))
        z = self.reparameterize(mu, logvar)
        return self.decode(z), mu, logvar

# Training loop for VAE
model = VAE()
optimizer = optim.Adam(model.parameters(), lr=1e-3)

# Example data loading (MNIST dataset)
from torchvision import datasets, transforms
train_loader = torch.utils.data.DataLoader(
    datasets.MNIST('./data', train=True, download=True, transform=transforms.ToTensor()),
    batch_size=128, shuffle=True)

def train(epoch):
    model.train()
    train_loss = 0
    for batch_idx, (data, _) in enumerate(train_loader):
        data = Variable(data)
        optimizer.zero_grad()
        recon_batch, mu, logvar = model(data)
        loss = loss_function(recon_batch, data, mu, logvar)
        loss.backward()
        train_loss += loss.item()
        optimizer.step()
    print('Train Epoch: {} \tAverage loss: {:.6f}'.format(
          epoch, train_loss / len(train_loader.dataset)))

# Loss function for VAE (Reconstruction + KL Divergence)
def loss_function(recon_x, x, mu, logvar):
    BCE = F.binary_cross_entropy(recon_x, x.view(-1, 784), reduction='sum')
    # KL divergence
    # D_KL(Q || P)
    # Where Q is the learned posterior distribution and P is the prior distribution.
    # We assume P = N(0, I)
    # The formula for KL is:
    # D_KL(Q || P) = 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
    # Where mu and sigma are the parameters of the learned distribution.
    # In this case we are approximating the true posterior Q using a Gaussian distribution.
    # The prior distribution P is a standard Gaussian, N(0, 1)
    # So the KL term is regularizing the latent space to follow a standard Gaussian.
    # Therefore, the final loss is a combination of the reconstruction loss and the KL loss.
    # More info: https://arxiv.org/abs/1312.6114

    # KL divergence
    M = mu.pow(2).sum(1)
    S = logvar.exp().sum(1)
    return BCE + 0.5 * (M + S - 20 - logvar.sum())

# Example training
for epoch in range(1, 11):
    train(epoch)

import gym
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from collections import deque
import random

# Define the DQN Model
class DQN(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(DQN, self).__init__()
        self.fc1 = nn.Linear(input_dim, 128)
        self.fc2 = nn.Linear(128, 128)
        self.fc3 = nn.Linear(128, output_dim)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        return self.fc3(x)

# Set up the environment (e.g., CartPole)
env = gym.make('CartPole-v1')

# Hyperparameters
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.n
learning_rate = 0.001
gamma = 0.99
epsilon = 0.1
batch_size = 64
buffer_size = 10000
target_update_freq = 10

# Initialize the Q-network and target network
q_network = DQN(state_dim, action_dim)
target_network = DQN(state_dim, action_dim)
target_network.load_state_dict(q_network.state_dict())

optimizer = optim.Adam(q_network.parameters(), lr=learning_rate)
memory = deque(maxlen=buffer_size)

# Epsilon-Greedy policy for exploration
def epsilon_greedy_policy(state):
    if random.random() < epsilon: return env.action_space.sample() # Random action else: with torch.no_grad(): return torch.argmax(q_network(torch.tensor(state, dtype=torch.float32))).item() # Training loop for episode in range(500): state = env.reset() done = False total_reward = 0 while not done: action = epsilon_greedy_policy(state) next_state, reward, done, _ = env.step(action) memory.append((state, action, reward, next_state, done)) if len(memory) > batch_size:
            batch = random.sample(memory, batch_size)
            states, actions, rewards, next_states, dones = zip(*batch)

            # Convert data to tensor
            states = torch.tensor(states, dtype=torch.float32)
            actions = torch.tensor(actions, dtype=torch.int64)
            rewards = torch.tensor(rewards, dtype=torch.float32)
            next_states = torch.tensor(next_states, dtype=torch.float32)
            dones = torch.tensor(dones, dtype=torch.uint8)

            # Compute the Q values
            q_values = q_network(states).gather(1, actions.unsqueeze(1)).squeeze(1)
            next_q_values = target_network(next_states).max(1)[0]
            target = rewards + gamma * next_q_values * (1 - dones)

            # Compute loss
            loss = torch.mean((q_values - target.detach()) ** 2)

            # Optimize the Q-network
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

        state = next_state
        total_reward += reward

    # Update the target network
    if episode % target_update_freq == 0:
        target_network.load_state_dict(q_network.state_dict())

    print(f"Episode {episode}, Total Reward: {total_reward}")

import torch
import torch.nn as nn
import torchvision.models as models
import torchaudio
from torchaudio.transforms import MelSpectrogram

# Vision model (ResNet18)
vision_model = models.resnet18(pretrained=True)
vision_model.fc = nn.Linear(vision_model.fc.in_features, 100)  # Output layer for our case

# Audio model
class AudioModel(nn.Module):
    def __init__(self):
        super(AudioModel, self).__init__()
        self.mel_spec = MelSpectrogram()
        self.lstm = nn.LSTM(input_size=64, hidden_size=128, num_layers=2, batch_first=True)
        self.fc = nn.Linear(128, 100)  # Output layer

    def forward(self, x):
        x = self.mel_spec(x)
        _, (hn, _) = self.lstm(x)
        return self.fc(hn[-1])

# Example inputs (image and audio)
image = torch.randn(1, 3, 224, 224)  # Random image (batch_size, channels, height, width)
audio = torch.randn(1, 16000)  # Random audio signal (batch_size, samples)

# Forward pass for vision and audio
vision_output = vision_model(image)
audio_model = AudioModel()
audio_output = audio_model(audio)

# Combine both outputs (e.g., for joint classification task)
combined_output = vision_output + audio_output