[人工智能]从零实现深度学习框架——处理数据的加载

引言

本着“凡我不能创造的，我就不能理解”的思想，本系列文章会基于纯Python以及NumPy从零创建自己的深度学习框架，该框架类似PyTorch能实现自动求导。

要深入理解深度学习，从零开始创建的经验非常重要，从自己可以理解的角度出发，尽量不使用外部完备的框架前提下，实现我们想要的模型。本系列文章的宗旨就是通过这样的过程，让大家切实掌握深度学习底层实现，而不是仅做一个调包侠。

在处理数据集的时候，经常需要加载数据，当数据量过大时，一次加载进来是不现实的，这时需要分批处理。最好的做法是实现数据加载类，基于迭代器思想，每次只加载一部分数据。

存在的问题

之前的拆分批次代码存在一些问题

def make_batches(X, y, batch_size=32, shuffle=True):
    '''
    将数据集拆分成批大小为batch_size的批数据
    :param X: 数据集 [样本数，样本维度]
    :param y: 对应的标签
    :param batch_size:  批大小
    :param shuffle: 是否需要对数据进行洗牌
    :return:
    '''
    n = X.shape[0]  # 样本数
    if shuffle:
        indexes = np.random.permutation(n)
    else:
        indexes = np.arange(n)

    X_batches = [
        Tensor(X[indexes, :][k:k + batch_size, :]) for k in range(0, n, batch_size)
    ]
    y_batches = [
        Tensor(y[indexes][k:k + batch_size]) for k in range(0, n, batch_size)
    ]

    return X_batches, y_batches

很容易会出现MemoryError：

numpy.core._exceptions.MemoryError: Unable to allocate xx. MiB for an array with shape (xxx,xx) and data type uint8

这种内存不足的问题，主要是在实现的时候X[indexes, :]这里先读取了所有的记录，然后再去分批。正确做法是对索引分批，每次只传入批次索引。

Dataset

首先我们创建一个数据集类：

class Dataset:

    def __getitem__(self, index):
        return NotImplementedError


class TensorDataset(Dataset):
    def __init__(self, *tensors: Tensor) -> None:
        self.tensors = tensors

    def __getitem__(self, index):
        return tuple(tensor[index] for tensor in self.tensors)

    def __len__(self):
        return len(self.tensors[0])

我们先实现TensorDataset可以传入自定义数据和对应的标签。

DataLoader

import math

import numpy as np

from metagrad.dataset import Dataset


class DataLoader:
    def __init__(self, dataset: Dataset, batch_size: int = 1,
                 shuffle: bool = False):
        self.dataset = dataset
        self.shuffle = shuffle
        self.batch_size = batch_size

        self.data_size = len(dataset)

        self.max_its = math.ceil(self.data_size / batch_size)
        self.it = 0  # 迭代次数
        self.indices = None
        self.reset()

    def reset(self):
        self.it = 0
        if self.shuffle:
            self.indices = np.random.permutation(self.data_size)
        else:
            self.indices = np.arange(self.data_size)

    def __next__(self):
        if self.it >= self.max_its:
            self.reset()
            raise StopIteration

        i, batch_size = self.it, self.batch_size
        batch_indices = self.indices[i * batch_size:(i + 1) * batch_size]
        batch = self.dataset[batch_indices]
        self.it += 1
        X_batch, y_batch = batch
        return X_batch, y_batch

    def next(self):
        return self.__next__()

    def __iter__(self):
        return self

然后实现数据加载类，传入数据集dataset，每次调用__next__方法时，只会加载部分数据，利用我们实现的切片操作，可以避免循环实现。

实战

本节代码 → 点此

接下来看我们的数据加载器如何进行使用。

import numpy as np

from metagrad.dataloader import DataLoader
from metagrad.dataset import TensorDataset
from metagrad.functions import sigmoid
from metagrad.tensor import Tensor

import metagrad.module as nn
from keras.datasets import imdb
from metagrad.loss import BCELoss
from metagrad.optim import SGD
from metagrad.utils import make_batches, loss_batch, accuracy
from metagrad.tensor import no_grad

import matplotlib.pyplot as plt


class Feedforward(nn.Module):
    '''
    简单单隐藏层前馈网络，用于分类问题
    '''

    def __init__(self, input_size, hidden_size, output_size):
        '''

        :param input_size: 输入维度
        :param hidden_size: 隐藏层大小
        :param output_size: 分类个数
        '''
        self.net = nn.Sequential(
            nn.Linear(input_size, hidden_size),  # 隐藏层，将输入转换为隐藏向量
            nn.ReLU(),  # 激活函数
            nn.Linear(hidden_size, output_size)  # 输出层，将隐藏向量转换为输出
        )

    def forward(self, x: Tensor) -> Tensor:
        return self.net(x)


# 加载数据集
def load_dataset():
    (X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=10000)
    # 标签的维度很重要，否则训练不起来
    y_train, y_test = y_train[:, np.newaxis], y_test[:, np.newaxis]
    X_train = vectorize_sequences(X_train)
    X_test = vectorize_sequences(X_test)

    # 保留验证集
    # X_train有25000条数据，我们保留10000条作为验证集
    X_val = X_train[:10000]
    X_train = X_train[10000:]

    y_val = y_train[:10000]
    y_train = y_train[10000:]

    return Tensor(X_train), Tensor(X_test), Tensor(y_train), Tensor(y_test), Tensor(X_val), Tensor(y_val)


def indices_to_sentence(indices: Tensor):
    # 单词索引字典 word -> indices
    word_index = imdb.get_word_index()
    # 逆单词索引字典 indices -> word
    reverse_word_index = dict(
        [(value, key) for (key, value) in word_index.items()])
    # 将index列表转换为word列表
    #
    # 0、1、2 是为“padding”（填充）、“start of sequence”（序
    # 列开始）、“unknown”（未知词）分别保留的索引
    decoded_review = ' '.join(
        [reverse_word_index.get(i - 3, '?') for i in indices.data])
    return decoded_review


def vectorize_sequences(sequences, dimension=10000):
    # 默认生成一个[句子长度，维度数]的向量
    results = np.zeros((len(sequences), dimension), dtype='uint8')
    for i, sequence in enumerate(sequences):
        # 将第i个序列中，对应单词序号处的位置置为1
        results[i, sequence] = 1
    return results


def compute_loss_and_accury(data_loader: DataLoader, model, loss_func, total_nums, opt=None):
    losses = []
    correct = 0
    for X_batch, y_batch in data_loader:
        y_pred = model(X_batch)
        l = loss_func(y_pred, y_batch)

        if opt is not None:
            l.backward()
            opt.step()
            opt.zero_grad()

        # 当前批次的损失
        losses.append(l.item())

        correct += np.sum(sigmoid(y_pred).numpy().round() == y_batch.numpy())

    loss = sum(losses) / total_nums  # 总损失 除以 样本总数
    accuracy = 100 * correct / total_nums

    return loss, accuracy


if __name__ == '__main__':
    X_train, X_test, y_train, y_test, X_val, y_val = load_dataset()

    model = Feedforward(10000, 128, 1)  # 输入大小10000,隐藏层大小128，输出只有一个，代表判断为正例的概率

    optimizer = SGD(model.parameters(), lr=0.001)
    # 先计算sum
    loss = BCELoss(reduction="sum")

    epochs = 20
    batch_size = 512
    train_losses, val_losses = [], []
    train_accuracies, val_accuracies = [], []

    # 由于数据过多，需要拆分成批次，使用自定义数据集和加载器
    train_ds = TensorDataset(X_train, y_train)
    train_dl = DataLoader(train_ds, batch_size=batch_size)

    val_ds = TensorDataset(X_val, y_val)
    val_dl = DataLoader(val_ds, batch_size=batch_size)

    for epoch in range(epochs):
        train_loss, train_accuracy = compute_loss_and_accury(train_dl, model, loss, len(X_train), optimizer)

        train_losses.append(train_loss)
        train_accuracies.append(train_accuracy)

        with no_grad():
            val_loss, val_accuracy = compute_loss_and_accury(val_dl, model, loss, len(X_val))

            val_losses.append(val_loss)
            val_accuracies.append(val_accuracy)

        print(f"Epoch:{epoch + 1}, Training Loss: {train_loss:.4f}, Accuracy: {train_accuracy:.2f}% | "
              f" Validation Loss:{val_loss:.4f} , Accuracy:{val_accuracy:.2f}%")

    # 绘制训练损失和验证损失
    epoch_list = range(1, epochs + 1)
    plt.plot(epoch_list, train_losses, 'r', label='Training loss')
    plt.plot(epoch_list, val_losses, 'b', label='Validation loss')
    plt.title('Training and validation loss')
    plt.xlabel('Epochs')
    plt.ylabel('Loss')
    plt.legend()
    plt.show()

    # 绘制训练准确率和验证准确率
    # 清空图像
    plt.clf()
    plt.plot(epoch_list, train_accuracies, 'r', label='Training acc')
    plt.plot(epoch_list, val_accuracies, 'b', label='Validation acc')
    plt.title('Training and validation accuracy')
    plt.xlabel('Epochs')
    plt.ylabel('Accuracy')
    plt.legend()
    plt.show()

    # 最后在测试集上测试
    with no_grad():
        X_test, y_test = Tensor(X_test), Tensor(y_test)
        outputs = model(X_test)
        correct = np.sum(sigmoid(outputs).numpy().round() == y_test.numpy())
        accuracy = 100 * correct / len(y_test)
        print(f"Test Accuracy:{accuracy}")

Epoch:1, Training Loss: 0.6283, Accuracy: 62.46% |  Validation Loss:0.5137 , Accuracy:80.53%
Epoch:2, Training Loss: 0.6466, Accuracy: 68.52% |  Validation Loss:0.5245 , Accuracy:81.06%
Epoch:3, Training Loss: 0.5974, Accuracy: 68.98% |  Validation Loss:0.4521 , Accuracy:82.33%
Epoch:4, Training Loss: 0.5042, Accuracy: 75.94% |  Validation Loss:0.3807 , Accuracy:83.38%
Epoch:5, Training Loss: 0.4556, Accuracy: 79.24% |  Validation Loss:0.3690 , Accuracy:85.02%
Epoch:6, Training Loss: 0.3801, Accuracy: 83.11% |  Validation Loss:0.3610 , Accuracy:84.43%
Epoch:7, Training Loss: 0.3606, Accuracy: 83.74% |  Validation Loss:0.3267 , Accuracy:86.23%
Epoch:8, Training Loss: 0.3090, Accuracy: 86.72% |  Validation Loss:0.3120 , Accuracy:86.53%
Epoch:9, Training Loss: 0.3088, Accuracy: 86.45% |  Validation Loss:0.3056 , Accuracy:86.94%
Epoch:10, Training Loss: 0.3781, Accuracy: 83.32% |  Validation Loss:0.3326 , Accuracy:86.67%
Epoch:11, Training Loss: 0.3872, Accuracy: 82.99% |  Validation Loss:0.3053 , Accuracy:87.00%
Epoch:12, Training Loss: 0.2890, Accuracy: 88.03% |  Validation Loss:0.2994 , Accuracy:87.33%
Epoch:13, Training Loss: 0.2775, Accuracy: 87.62% |  Validation Loss:0.2955 , Accuracy:87.32%
Epoch:14, Training Loss: 0.3027, Accuracy: 86.41% |  Validation Loss:0.3102 , Accuracy:87.04%
Epoch:15, Training Loss: 0.2360, Accuracy: 90.01% |  Validation Loss:0.3045 , Accuracy:87.47%
Epoch:16, Training Loss: 0.2313, Accuracy: 90.17% |  Validation Loss:0.3071 , Accuracy:87.43%
Epoch:17, Training Loss: 0.1887, Accuracy: 92.70% |  Validation Loss:0.2868 , Accuracy:88.18%
Epoch:18, Training Loss: 0.5147, Accuracy: 77.11% |  Validation Loss:0.3775 , Accuracy:86.02%
Epoch:19, Training Loss: 0.4108, Accuracy: 80.92% |  Validation Loss:0.3340 , Accuracy:85.39%
Epoch:20, Training Loss: 0.2783, Accuracy: 87.69% |  Validation Loss:0.3466 , Accuracy:86.15%