mirror of
https://gitee.com/fastnlp/fastNLP.git
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604 lines
16 KiB
Plaintext
604 lines
16 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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"# 使用Trainer和Tester快速训练和测试"
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]
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},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 数据读入和处理"
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]
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/remote-home/ynzheng/anaconda3/envs/now/lib/python3.8/site-packages/FastNLP-0.5.0-py3.8.egg/fastNLP/io/loader/classification.py:340: UserWarning: SST2's test file has no target.\n"
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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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"In total 3 datasets:\n",
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"\ttest has 1821 instances.\n",
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"\ttrain has 67349 instances.\n",
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"\tdev has 872 instances.\n",
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"In total 2 vocabs:\n",
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"\twords has 16292 entries.\n",
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"\ttarget has 2 entries.\n",
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"\n",
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"+-----------------------------------+--------+-----------------------------------+---------+\n",
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"| raw_words | target | words | seq_len |\n",
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"+-----------------------------------+--------+-----------------------------------+---------+\n",
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"| hide new secretions from the p... | 1 | [4110, 97, 12009, 39, 2, 6843,... | 7 |\n",
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"+-----------------------------------+--------+-----------------------------------+---------+\n",
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"Vocabulary(['hide', 'new', 'secretions', 'from', 'the']...)\n"
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]
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}
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],
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"source": [
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"from fastNLP.io import SST2Pipe\n",
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"\n",
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"pipe = SST2Pipe()\n",
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"databundle = pipe.process_from_file()\n",
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"vocab = databundle.get_vocab('words')\n",
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"print(databundle)\n",
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"print(databundle.get_dataset('train')[0])\n",
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"print(databundle.get_vocab('words'))"
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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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"metadata": {},
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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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"4925 872 75\n"
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]
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}
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],
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"source": [
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"train_data = databundle.get_dataset('train')[:5000]\n",
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"train_data, test_data = train_data.split(0.015)\n",
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"dev_data = databundle.get_dataset('dev')\n",
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"print(len(train_data),len(dev_data),len(test_data))"
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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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"metadata": {
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"scrolled": false
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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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"+-------------+-----------+--------+-------+---------+\n",
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"| field_names | raw_words | target | words | seq_len |\n",
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"+-------------+-----------+--------+-------+---------+\n",
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"| is_input | False | False | True | True |\n",
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"| is_target | False | True | False | False |\n",
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"| ignore_type | | False | False | False |\n",
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"| pad_value | | 0 | 0 | 0 |\n",
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"+-------------+-----------+--------+-------+---------+\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<prettytable.PrettyTable at 0x7f49ec540160>"
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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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"train_data.print_field_meta()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 使用内置模型训练"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from fastNLP.models import CNNText\n",
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"\n",
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"#词嵌入的维度\n",
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"EMBED_DIM = 100\n",
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"\n",
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"#使用CNNText的时候第一个参数输入一个tuple,作为模型定义embedding的参数\n",
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"#还可以传入 kernel_nums, kernel_sizes, padding, dropout的自定义值\n",
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"model_cnn = CNNText((len(vocab),EMBED_DIM), num_classes=2, dropout=0.1)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from fastNLP import AccuracyMetric\n",
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"from fastNLP import Const\n",
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"\n",
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"# metrics=AccuracyMetric() 在本例中与下面这行代码等价\n",
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"metrics=AccuracyMetric(pred=Const.OUTPUT, target=Const.TARGET)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from fastNLP import CrossEntropyLoss\n",
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"\n",
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"# loss = CrossEntropyLoss() 在本例中与下面这行代码等价\n",
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"loss = CrossEntropyLoss(pred=Const.OUTPUT, target=Const.TARGET)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# 这表示构建了一个损失函数类，由func计算损失函数，其中将从模型返回值或者DataSet的target=True的field\n",
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"# 当中找到一个参数名为`pred`的参数传入func一个参数名为`input`的参数；找到一个参数名为`label`的参数\n",
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"# 传入func作为一个名为`target`的参数\n",
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"#下面自己构建了一个交叉熵函数，和之后直接使用fastNLP中的交叉熵函数是一个效果\n",
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"import torch\n",
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"from fastNLP import LossFunc\n",
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"func = torch.nn.functional.cross_entropy\n",
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"loss_func = LossFunc(func, input=Const.OUTPUT, target=Const.TARGET)"
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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": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch.optim as optim\n",
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"\n",
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"#使用 torch.optim 定义优化器\n",
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"optimizer=optim.RMSprop(model_cnn.parameters(), lr=0.01, alpha=0.99, eps=1e-08, weight_decay=0, momentum=0, centered=False)"
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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": 9,
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"metadata": {},
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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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"input fields after batch(if batch size is 2):\n",
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"\twords: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 4]) \n",
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"\tseq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) \n",
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"target fields after batch(if batch size is 2):\n",
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"\ttarget: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) \n",
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"\n",
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"training epochs started 2020-02-27-11-31-25\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"version_major": 2,
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"version_minor": 0
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},
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"HBox(children=(FloatProgress(value=0.0, layout=Layout(flex='2'), max=3080.0), HTML(value='')), layout=Layout(d…"
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"metadata": {},
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"output_type": "display_data"
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"HBox(children=(FloatProgress(value=0.0, layout=Layout(flex='2'), max=55.0), HTML(value='')), layout=Layout(dis…"
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"metadata": {},
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"output_type": "display_data"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\r",
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"Evaluate data in 0.75 seconds!\n",
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"\r",
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"Evaluation on dev at Epoch 1/10. Step:308/3080: \n",
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"\r",
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"AccuracyMetric: acc=0.751147\n",
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"\n"
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]
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},
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"data": {
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"version_minor": 0
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"HBox(children=(FloatProgress(value=0.0, layout=Layout(flex='2'), max=55.0), HTML(value='')), layout=Layout(dis…"
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"metadata": {},
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"output_type": "display_data"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\r",
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"Evaluate data in 0.83 seconds!\n",
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"\r",
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"Evaluation on dev at Epoch 2/10. Step:616/3080: \n",
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"\r",
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"AccuracyMetric: acc=0.755734\n",
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"\n"
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]
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},
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"data": {
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"version_minor": 0
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"text/plain": [
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"HBox(children=(FloatProgress(value=0.0, layout=Layout(flex='2'), max=55.0), HTML(value='')), layout=Layout(dis…"
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"\r",
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"Evaluate data in 1.32 seconds!\n",
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"\r",
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"Evaluation on dev at Epoch 3/10. Step:924/3080: \n",
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"\r",
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"AccuracyMetric: acc=0.758028\n",
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"\n"
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]
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},
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"Evaluate data in 0.88 seconds!\n",
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"Evaluation on dev at Epoch 4/10. Step:1232/3080: \n",
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"\r",
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"AccuracyMetric: acc=0.741972\n",
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"\n"
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]
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"text": [
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"AccuracyMetric: acc=0.732798\n",
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|
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"version_minor": 0
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"output_type": "display_data"
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},
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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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"\r",
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"Evaluate data in 0.48 seconds!\n",
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"\r",
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"Evaluation on dev at Epoch 10/10. Step:3080/3080: \n",
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"\r",
|
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"AccuracyMetric: acc=0.740826\n",
|
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|
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"\n",
|
|||
|
|
"\r\n",
|
|||
|
|
"In Epoch:3/Step:924, got best dev performance:\n",
|
|||
|
|
"AccuracyMetric: acc=0.758028\n",
|
|||
|
|
"Reloaded the best model.\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"{'best_eval': {'AccuracyMetric': {'acc': 0.758028}},\n",
|
|||
|
|
" 'best_epoch': 3,\n",
|
|||
|
|
" 'best_step': 924,\n",
|
|||
|
|
" 'seconds': 160.58}"
|
|||
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]
|
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},
|
|||
|
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"execution_count": 9,
|
|||
|
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"metadata": {},
|
|||
|
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"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from fastNLP import Trainer\n",
|
|||
|
|
"\n",
|
|||
|
|
"#训练的轮数和batch size\n",
|
|||
|
|
"N_EPOCHS = 10\n",
|
|||
|
|
"BATCH_SIZE = 16\n",
|
|||
|
|
"\n",
|
|||
|
|
"#如果在定义trainer的时候没有传入optimizer参数，模型默认的优化器为torch.optim.Adam且learning rate为lr=4e-3\n",
|
|||
|
|
"#这里只使用了loss作为损失函数输入，感兴趣可以尝试其他损失函数（如之前自定义的loss_func）作为输入\n",
|
|||
|
|
"trainer = Trainer(model=model_cnn, train_data=train_data, dev_data=dev_data, loss=loss, metrics=metrics,\n",
|
|||
|
|
"optimizer=optimizer,n_epochs=N_EPOCHS, batch_size=BATCH_SIZE)\n",
|
|||
|
|
"trainer.train()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 10,
|
|||
|
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"metadata": {},
|
|||
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"outputs": [
|
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{
|
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"data": {
|
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"application/vnd.jupyter.widget-view+json": {
|
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"model_id": "",
|
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"version_major": 2,
|
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"version_minor": 0
|
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},
|
|||
|
|
"text/plain": [
|
|||
|
|
"HBox(children=(FloatProgress(value=0.0, layout=Layout(flex='2'), max=5.0), HTML(value='')), layout=Layout(disp…"
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]
|
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},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"\r",
|
|||
|
|
"Evaluate data in 0.43 seconds!\n",
|
|||
|
|
"[tester] \n",
|
|||
|
|
"AccuracyMetric: acc=0.773333\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"{'AccuracyMetric': {'acc': 0.773333}}"
|
|||
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|
]
|
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|
|
},
|
|||
|
|
"execution_count": 10,
|
|||
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"metadata": {},
|
|||
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|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from fastNLP import Tester\n",
|
|||
|
|
"\n",
|
|||
|
|
"tester = Tester(test_data, model_cnn, metrics=AccuracyMetric())\n",
|
|||
|
|
"tester.test()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
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"metadata": {},
|
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"outputs": [],
|
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"source": []
|
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}
|
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],
|
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"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "Python Now",
|
|||
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|
"language": "python",
|
|||
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"name": "now"
|
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},
|
|||
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"language_info": {
|
|||
|
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"codemirror_mode": {
|
|||
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|
"name": "ipython",
|
|||
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"version": 3
|
|||
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},
|
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|
|
"file_extension": ".py",
|
|||
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|
"mimetype": "text/x-python",
|
|||
|
|
"name": "python",
|
|||
|
|
"nbconvert_exporter": "python",
|
|||
|
|
"pygments_lexer": "ipython3",
|
|||
|
|
"version": "3.8.0"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 2
|
|||
|
|
}
|