1255 lines
43 KiB
Plaintext
1255 lines
43 KiB
Plaintext
{
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"cells": [
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"cell_type": "markdown",
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"metadata": {
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"source": [
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"# 循环神经网络的简洁实现\n",
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":label:`sec_rnn-concise`\n",
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"\n",
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"虽然 :numref:`sec_rnn_scratch`\n",
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"对了解循环神经网络的实现方式具有指导意义，但并不方便。\n",
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"本节将展示如何使用深度学习框架的高级API提供的函数更有效地实现相同的语言模型。\n",
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"我们仍然从读取时光机器数据集开始。\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "e38d82e3",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:22:38.029441Z",
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"iopub.status.busy": "2023-08-18T07:22:38.028754Z",
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},
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"origin_pos": 2,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [],
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"source": [
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"import torch\n",
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"from torch import nn\n",
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"from torch.nn import functional as F\n",
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"from d2l import torch as d2l\n",
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"\n",
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"batch_size, num_steps = 32, 35\n",
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"train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bc8deb24",
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"metadata": {
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"origin_pos": 5
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},
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"source": [
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"## [**定义模型**]\n",
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"\n",
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"高级API提供了循环神经网络的实现。\n",
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"我们构造一个具有256个隐藏单元的单隐藏层的循环神经网络层`rnn_layer`。\n",
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"事实上，我们还没有讨论多层循环神经网络的意义（这将在 :numref:`sec_deep_rnn`中介绍）。\n",
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"现在仅需要将多层理解为一层循环神经网络的输出被用作下一层循环神经网络的输入就足够了。\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "37dae103",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:22:42.087317Z",
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"iopub.status.busy": "2023-08-18T07:22:42.086622Z",
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"iopub.status.idle": "2023-08-18T07:22:42.117225Z",
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"shell.execute_reply": "2023-08-18T07:22:42.116310Z"
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},
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"origin_pos": 7,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [],
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"source": [
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"num_hiddens = 256\n",
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"rnn_layer = nn.RNN(len(vocab), num_hiddens)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "08e36240",
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"metadata": {
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"origin_pos": 11,
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"tab": [
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"pytorch"
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]
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},
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"source": [
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"我们(**使用张量来初始化隐状态**)，它的形状是（隐藏层数，批量大小，隐藏单元数）。\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "1922fe18",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:22:42.122208Z",
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"shell.execute_reply": "2023-08-18T07:22:42.127617Z"
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"origin_pos": 13,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"torch.Size([1, 32, 256])"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"state = torch.zeros((1, batch_size, num_hiddens))\n",
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"state.shape"
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]
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},
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{
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"cell_type": "markdown",
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"id": "170be239",
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"metadata": {
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"origin_pos": 16
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},
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"source": [
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"[**通过一个隐状态和一个输入，我们就可以用更新后的隐状态计算输出。**]\n",
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"需要强调的是，`rnn_layer`的“输出”（`Y`）不涉及输出层的计算：\n",
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"它是指每个时间步的隐状态，这些隐状态可以用作后续输出层的输入。\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "cecfe4c4",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:22:42.132231Z",
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"iopub.status.busy": "2023-08-18T07:22:42.131762Z",
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"iopub.status.idle": "2023-08-18T07:22:42.149849Z",
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"shell.execute_reply": "2023-08-18T07:22:42.148795Z"
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},
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"origin_pos": 19,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(torch.Size([35, 32, 256]), torch.Size([1, 32, 256]))"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"X = torch.rand(size=(num_steps, batch_size, len(vocab)))\n",
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"Y, state_new = rnn_layer(X, state)\n",
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"Y.shape, state_new.shape"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7c919b48",
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"metadata": {
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"origin_pos": 22
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},
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"source": [
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"与 :numref:`sec_rnn_scratch`类似，\n",
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"[**我们为一个完整的循环神经网络模型定义了一个`RNNModel`类**]。\n",
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"注意，`rnn_layer`只包含隐藏的循环层，我们还需要创建一个单独的输出层。\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "300de81f",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:22:42.155516Z",
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"iopub.status.busy": "2023-08-18T07:22:42.154604Z",
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"iopub.status.idle": "2023-08-18T07:22:42.171447Z",
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"shell.execute_reply": "2023-08-18T07:22:42.170040Z"
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},
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"origin_pos": 24,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [],
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"source": [
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"#@save\n",
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"class RNNModel(nn.Module):\n",
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" \"\"\"循环神经网络模型\"\"\"\n",
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" def __init__(self, rnn_layer, vocab_size, **kwargs):\n",
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" super(RNNModel, self).__init__(**kwargs)\n",
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" self.rnn = rnn_layer\n",
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" self.vocab_size = vocab_size\n",
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" self.num_hiddens = self.rnn.hidden_size\n",
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" # 如果RNN是双向的（之后将介绍），num_directions应该是2，否则应该是1\n",
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" if not self.rnn.bidirectional:\n",
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" self.num_directions = 1\n",
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" self.linear = nn.Linear(self.num_hiddens, self.vocab_size)\n",
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" else:\n",
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" self.num_directions = 2\n",
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" self.linear = nn.Linear(self.num_hiddens * 2, self.vocab_size)\n",
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"\n",
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" def forward(self, inputs, state):\n",
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" X = F.one_hot(inputs.T.long(), self.vocab_size)\n",
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" X = X.to(torch.float32)\n",
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" Y, state = self.rnn(X, state)\n",
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" # 全连接层首先将Y的形状改为(时间步数*批量大小,隐藏单元数)\n",
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" # 它的输出形状是(时间步数*批量大小,词表大小)。\n",
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" output = self.linear(Y.reshape((-1, Y.shape[-1])))\n",
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" return output, state\n",
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"\n",
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" def begin_state(self, device, batch_size=1):\n",
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" if not isinstance(self.rnn, nn.LSTM):\n",
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" # nn.GRU以张量作为隐状态\n",
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" return torch.zeros((self.num_directions * self.rnn.num_layers,\n",
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" batch_size, self.num_hiddens),\n",
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" device=device)\n",
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" else:\n",
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" # nn.LSTM以元组作为隐状态\n",
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" return (torch.zeros((\n",
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" self.num_directions * self.rnn.num_layers,\n",
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" batch_size, self.num_hiddens), device=device),\n",
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" torch.zeros((\n",
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" self.num_directions * self.rnn.num_layers,\n",
|
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" batch_size, self.num_hiddens), device=device))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "573178db",
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"metadata": {
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"origin_pos": 27
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},
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"source": [
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"## 训练与预测\n",
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"\n",
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"在训练模型之前，让我们[**基于一个具有随机权重的模型进行预测**]。\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "93c01655",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:22:42.176888Z",
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"iopub.status.busy": "2023-08-18T07:22:42.175834Z",
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"iopub.status.idle": "2023-08-18T07:22:45.380419Z",
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"shell.execute_reply": "2023-08-18T07:22:45.379583Z"
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},
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"origin_pos": 29,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'time travellerbbabbkabyg'"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"device = d2l.try_gpu()\n",
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"net = RNNModel(rnn_layer, vocab_size=len(vocab))\n",
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"net = net.to(device)\n",
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"d2l.predict_ch8('time traveller', 10, net, vocab, device)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "240f0d45",
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"metadata": {
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"origin_pos": 32
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},
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"source": [
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"很明显，这种模型根本不能输出好的结果。\n",
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"接下来，我们使用 :numref:`sec_rnn_scratch`中\n",
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"定义的超参数调用`train_ch8`，并且[**使用高级API训练模型**]。\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "97fc8534",
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"execution": {
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"origin_pos": 34,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"perplexity 1.3, 404413.8 tokens/sec on cuda:0\n",
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"time travellerit would be remarkably convenient for the historia\n",
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"travellery of il the hise fupt might and st was it loflers \n"
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]
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},
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" <use xlink:href=\"#DejaVuSans-69\" x=\"141.601562\"/>\n",
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" <use xlink:href=\"#DejaVuSans-6e\" x=\"169.384766\"/>\n",
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" </g>\n",
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" </g>\n",
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" </g>\n",
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" </g>\n",
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" </g>\n",
|
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" <defs>\n",
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" <clipPath id=\"pe049e26444\">\n",
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" <rect x=\"40.603125\" y=\"7.2\" width=\"195.3\" height=\"135.9\"/>\n",
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" </clipPath>\n",
|
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" </defs>\n",
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"</svg>\n"
|
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],
|
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"text/plain": [
|
|
"<Figure size 252x180 with 1 Axes>"
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|
]
|
|
},
|
|
"metadata": {
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|
"needs_background": "light"
|
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},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
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"source": [
|
|
"num_epochs, lr = 500, 1\n",
|
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"d2l.train_ch8(net, train_iter, vocab, lr, num_epochs, device)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "409494cb",
|
|
"metadata": {
|
|
"origin_pos": 37
|
|
},
|
|
"source": [
|
|
"与上一节相比，由于深度学习框架的高级API对代码进行了更多的优化，\n",
|
|
"该模型在较短的时间内达到了较低的困惑度。\n",
|
|
"\n",
|
|
"## 小结\n",
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|
"\n",
|
|
"* 深度学习框架的高级API提供了循环神经网络层的实现。\n",
|
|
"* 高级API的循环神经网络层返回一个输出和一个更新后的隐状态，我们还需要计算整个模型的输出层。\n",
|
|
"* 相比从零开始实现的循环神经网络，使用高级API实现可以加速训练。\n",
|
|
"\n",
|
|
"## 练习\n",
|
|
"\n",
|
|
"1. 尝试使用高级API，能使循环神经网络模型过拟合吗？\n",
|
|
"1. 如果在循环神经网络模型中增加隐藏层的数量会发生什么？能使模型正常工作吗？\n",
|
|
"1. 尝试使用循环神经网络实现 :numref:`sec_sequence`的自回归模型。\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d84e3e86",
|
|
"metadata": {
|
|
"origin_pos": 39,
|
|
"tab": [
|
|
"pytorch"
|
|
]
|
|
},
|
|
"source": [
|
|
"[Discussions](https://discuss.d2l.ai/t/2106)\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
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"language_info": {
|
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"name": "python"
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},
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"required_libs": []
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},
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"nbformat": 4,
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"nbformat_minor": 5
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} |