ChenXin
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tutorials/tutorial_5_loss_optimizer.ipynb
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| { | |||
| "cells": [ | |||
| { | |||
| "cell_type": "markdown", | |||
| "metadata": {}, | |||
| "source": [ | |||
| "# 使用Trainer和Tester快速训练和测试" | |||
| ] | |||
| }, | |||
| { | |||
| "cell_type": "markdown", | |||
| "metadata": {}, | |||
| "source": [ | |||
| "## 数据读入和处理" | |||
| ] | |||
| }, | |||
| { | |||
| "cell_type": "code", | |||
| "execution_count": 1, | |||
| "metadata": {}, | |||
| "outputs": [ | |||
| { | |||
| "name": "stderr", | |||
| "output_type": "stream", | |||
| "text": [ | |||
| "/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" | |||
| ] | |||
| }, | |||
| { | |||
| "name": "stdout", | |||
| "output_type": "stream", | |||
| "text": [ | |||
| "In total 3 datasets:\n", | |||
| "\ttest has 1821 instances.\n", | |||
| "\ttrain has 67349 instances.\n", | |||
| "\tdev has 872 instances.\n", | |||
| "In total 2 vocabs:\n", | |||
| "\twords has 16292 entries.\n", | |||
| "\ttarget has 2 entries.\n", | |||
| "\n", | |||
| "+-----------------------------------+--------+-----------------------------------+---------+\n", | |||
| "| raw_words | target | words | seq_len |\n", | |||
| "+-----------------------------------+--------+-----------------------------------+---------+\n", | |||
| "| hide new secretions from the p... | 1 | [4110, 97, 12009, 39, 2, 6843,... | 7 |\n", | |||
| "+-----------------------------------+--------+-----------------------------------+---------+\n", | |||
| "Vocabulary(['hide', 'new', 'secretions', 'from', 'the']...)\n" | |||
| ] | |||
| } | |||
| ], | |||
| "source": [ | |||
| "from fastNLP.io import SST2Pipe\n", | |||
| "\n", | |||
| "pipe = SST2Pipe()\n", | |||
| "databundle = pipe.process_from_file()\n", | |||
| "vocab = databundle.get_vocab('words')\n", | |||
| "print(databundle)\n", | |||
| "print(databundle.get_dataset('train')[0])\n", | |||
| "print(databundle.get_vocab('words'))" | |||
| ] | |||
| }, | |||
| { | |||
| "cell_type": "code", | |||
| "execution_count": 2, | |||
| "metadata": {}, | |||
| "outputs": [ | |||
| { | |||
| "name": "stdout", | |||
| "output_type": "stream", | |||
| "text": [ | |||
| "4925 872 75\n" | |||
| ] | |||
| } | |||
| ], | |||
| "source": [ | |||
| "train_data = databundle.get_dataset('train')[:5000]\n", | |||
| "train_data, test_data = train_data.split(0.015)\n", | |||
| "dev_data = databundle.get_dataset('dev')\n", | |||
| "print(len(train_data),len(dev_data),len(test_data))" | |||
| ] | |||
| }, | |||
| { | |||
| "cell_type": "code", | |||
| "execution_count": 3, | |||
| "metadata": { | |||
| "scrolled": false | |||
| }, | |||
| "outputs": [ | |||
| { | |||
| "name": "stdout", | |||
| "output_type": "stream", | |||
| "text": [ | |||
| "+-------------+-----------+--------+-------+---------+\n", | |||
| "| field_names | raw_words | target | words | seq_len |\n", | |||
| "+-------------+-----------+--------+-------+---------+\n", | |||
| "| is_input | False | False | True | True |\n", | |||
| "| is_target | False | True | False | False |\n", | |||
| "| ignore_type | | False | False | False |\n", | |||
| "| pad_value | | 0 | 0 | 0 |\n", | |||
| "+-------------+-----------+--------+-------+---------+\n" | |||
| ] | |||
| }, | |||
| { | |||
| "data": { | |||
| "text/plain": [ | |||
| "<prettytable.PrettyTable at 0x7f49ec540160>" | |||
| ] | |||
| }, | |||
| "execution_count": 3, | |||
| "metadata": {}, | |||
| "output_type": "execute_result" | |||
| } | |||
| ], | |||
| "source": [ | |||
| "train_data.print_field_meta()" | |||
| ] | |||
| }, | |||
| { | |||
| "cell_type": "markdown", | |||
| "metadata": {}, | |||
| "source": [ | |||
| "## 使用内置模型训练" | |||
| ] | |||
| }, | |||
| { | |||
| "cell_type": "code", | |||
| "execution_count": 4, | |||
| "metadata": {}, | |||
| "outputs": [], | |||
| "source": [ | |||
| "from fastNLP.models import CNNText\n", | |||
| "\n", | |||
| "#词嵌入的维度\n", | |||
| "EMBED_DIM = 100\n", | |||
| "\n", | |||
| "#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", | |||
| "#还可以传入 kernel_nums, kernel_sizes, padding, dropout的自定义值\n", | |||
| "model_cnn = CNNText((len(vocab),EMBED_DIM), num_classes=2, dropout=0.1)" | |||
| ] | |||
| }, | |||
| { | |||
| "cell_type": "code", | |||
| "execution_count": 5, | |||
| "metadata": {}, | |||
| "outputs": [], | |||
| "source": [ | |||
| "from fastNLP import AccuracyMetric\n", | |||
| "from fastNLP import Const\n", | |||
| "\n", | |||
| "# metrics=AccuracyMetric() 在本例中与下面这行代码等价\n", | |||
| "metrics=AccuracyMetric(pred=Const.OUTPUT, target=Const.TARGET)" | |||
| ] | |||
| }, | |||
| { | |||
| "cell_type": "code", | |||
| "execution_count": 6, | |||
| "metadata": {}, | |||
| "outputs": [], | |||
| "source": [ | |||
| "from fastNLP import CrossEntropyLoss\n", | |||
| "\n", | |||
| "# loss = CrossEntropyLoss() 在本例中与下面这行代码等价\n", | |||
| "loss = CrossEntropyLoss(pred=Const.OUTPUT, target=Const.TARGET)" | |||
| ] | |||
| }, | |||
| { | |||
| "cell_type": "code", | |||
| "execution_count": 7, | |||
| "metadata": {}, | |||
| "outputs": [], | |||
| "source": [ | |||
| "# 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", | |||
| "# 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", | |||
| "# 传入func作为一个名为`target`的参数\n", | |||
| "#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", | |||
| "import torch\n", | |||
| "from fastNLP import LossFunc\n", | |||
| "func = torch.nn.functional.cross_entropy\n", | |||
| "loss_func = LossFunc(func, input=Const.OUTPUT, target=Const.TARGET)" | |||
| ] | |||
| }, | |||
| { | |||
| "cell_type": "code", | |||
| "execution_count": 8, | |||
| "metadata": {}, | |||
| "outputs": [], | |||
| "source": [ | |||
| "import torch.optim as optim\n", | |||
| "\n", | |||
| "#使用 torch.optim 定义优化器\n", | |||
| "optimizer=optim.RMSprop(model_cnn.parameters(), lr=0.01, alpha=0.99, eps=1e-08, weight_decay=0, momentum=0, centered=False)" | |||
| ] | |||
| }, | |||
| { | |||
| "cell_type": "code", | |||
| "execution_count": 9, | |||
| "metadata": {}, | |||
| "outputs": [ | |||
| { | |||
| "name": "stdout", | |||
| "output_type": "stream", | |||
| "text": [ | |||
| "input fields after batch(if batch size is 2):\n", | |||
| "\twords: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 4]) \n", | |||
| "\tseq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) \n", | |||
| "target fields after batch(if batch size is 2):\n", | |||
| "\ttarget: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) \n", | |||
| "\n", | |||
| "training epochs started 2020-02-27-11-31-25\n" | |||
| ] | |||
| }, | |||
| { | |||
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| }, | |||
| { | |||
| "name": "stdout", | |||
| "output_type": "stream", | |||
| "text": [ | |||
| "\r", | |||
| "Evaluate data in 0.75 seconds!\n", | |||
| "\r", | |||
| "Evaluation on dev at Epoch 1/10. Step:308/3080: \n", | |||
| "\r", | |||
| "AccuracyMetric: acc=0.751147\n", | |||
| "\n" | |||
| ] | |||
| }, | |||
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| }, | |||
| { | |||
| "name": "stdout", | |||
| "output_type": "stream", | |||
| "text": [ | |||
| "\r", | |||
| "Evaluate data in 0.83 seconds!\n", | |||
| "\r", | |||
| "Evaluation on dev at Epoch 2/10. Step:616/3080: \n", | |||
| "\r", | |||
| "AccuracyMetric: acc=0.755734\n", | |||
| "\n" | |||
| ] | |||
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| "name": "stdout", | |||
| "output_type": "stream", | |||
| "text": [ | |||
| "\r", | |||
| "Evaluate data in 1.32 seconds!\n", | |||
| "\r", | |||
| "Evaluation on dev at Epoch 3/10. Step:924/3080: \n", | |||
| "\r", | |||
| "AccuracyMetric: acc=0.758028\n", | |||
| "\n" | |||
| ] | |||
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| { | |||
| "name": "stdout", | |||
| "output_type": "stream", | |||
| "text": [ | |||
| "\r", | |||
| "Evaluate data in 0.88 seconds!\n", | |||
| "\r", | |||
| "Evaluation on dev at Epoch 4/10. Step:1232/3080: \n", | |||
| "\r", | |||
| "AccuracyMetric: acc=0.741972\n", | |||
| "\n" | |||
| ] | |||
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| { | |||
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| { | |||
| "name": "stdout", | |||
| "output_type": "stream", | |||
| "text": [ | |||
| "\r", | |||
| "Evaluate data in 0.96 seconds!\n", | |||
| "\r", | |||
| "Evaluation on dev at Epoch 5/10. Step:1540/3080: \n", | |||
| "\r", | |||
| "AccuracyMetric: acc=0.728211\n", | |||
| "\n" | |||
| ] | |||
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| "text": [ | |||
| "\r", | |||
| "Evaluate data in 0.87 seconds!\n", | |||
| "\r", | |||
| "Evaluation on dev at Epoch 6/10. Step:1848/3080: \n", | |||
| "\r", | |||
| "AccuracyMetric: acc=0.755734\n", | |||
| "\n" | |||
| ] | |||
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| { | |||
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| { | |||
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| "text": [ | |||
| "\r", | |||
| "Evaluate data in 1.04 seconds!\n", | |||
| "\r", | |||
| "Evaluation on dev at Epoch 7/10. Step:2156/3080: \n", | |||
| "\r", | |||
| "AccuracyMetric: acc=0.732798\n", | |||
| "\n" | |||
| ] | |||
| }, | |||
| { | |||
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| "metadata": {}, | |||
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| }, | |||
| { | |||
| "name": "stdout", | |||
| "output_type": "stream", | |||
| "text": [ | |||
| "\r", | |||
| "Evaluate data in 0.57 seconds!\n", | |||
| "\r", | |||
| "Evaluation on dev at Epoch 8/10. Step:2464/3080: \n", | |||
| "\r", | |||
| "AccuracyMetric: acc=0.747706\n", | |||
| "\n" | |||
| ] | |||
| }, | |||
| { | |||
| "data": { | |||
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| "version_minor": 0 | |||
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| "metadata": {}, | |||
| "output_type": "display_data" | |||
| }, | |||
| { | |||
| "name": "stdout", | |||
| "output_type": "stream", | |||
| "text": [ | |||
| "\r", | |||
| "Evaluate data in 0.48 seconds!\n", | |||
| "\r", | |||
| "Evaluation on dev at Epoch 9/10. Step:2772/3080: \n", | |||
| "\r", | |||
| "AccuracyMetric: acc=0.732798\n", | |||
| "\n" | |||
| ] | |||
| }, | |||
| { | |||
| "data": { | |||
| "application/vnd.jupyter.widget-view+json": { | |||
| "model_id": "", | |||
| "version_major": 2, | |||
| "version_minor": 0 | |||
| }, | |||
| "text/plain": [ | |||
| "HBox(children=(FloatProgress(value=0.0, layout=Layout(flex='2'), max=55.0), HTML(value='')), layout=Layout(dis…" | |||
| ] | |||
| }, | |||
| "metadata": {}, | |||
| "output_type": "display_data" | |||
| }, | |||
| { | |||
| "name": "stdout", | |||
| "output_type": "stream", | |||
| "text": [ | |||
| "\r", | |||
| "Evaluate data in 0.48 seconds!\n", | |||
| "\r", | |||
| "Evaluation on dev at Epoch 10/10. Step:3080/3080: \n", | |||
| "\r", | |||
| "AccuracyMetric: acc=0.740826\n", | |||
| "\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}" | |||
| ] | |||
| }, | |||
| "execution_count": 9, | |||
| "metadata": {}, | |||
| "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, | |||
| "metadata": {}, | |||
| "outputs": [ | |||
| { | |||
| "data": { | |||
| "application/vnd.jupyter.widget-view+json": { | |||
| "model_id": "", | |||
| "version_major": 2, | |||
| "version_minor": 0 | |||
| }, | |||
| "text/plain": [ | |||
| "HBox(children=(FloatProgress(value=0.0, layout=Layout(flex='2'), max=5.0), HTML(value='')), layout=Layout(disp…" | |||
| ] | |||
| }, | |||
| "metadata": {}, | |||
| "output_type": "display_data" | |||
| }, | |||
| { | |||
| "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}}" | |||
| ] | |||
| }, | |||
| "execution_count": 10, | |||
| "metadata": {}, | |||
| "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, | |||
| "metadata": {}, | |||
| "outputs": [], | |||
| "source": [] | |||
| } | |||
| ], | |||
| "metadata": { | |||
| "kernelspec": { | |||
| "display_name": "Python Now", | |||
| "language": "python", | |||
| "name": "now" | |||
| }, | |||
| "language_info": { | |||
| "codemirror_mode": { | |||
| "name": "ipython", | |||
| "version": 3 | |||
| }, | |||
| "file_extension": ".py", | |||
| "mimetype": "text/x-python", | |||
| "name": "python", | |||
| "nbconvert_exporter": "python", | |||
| "pygments_lexer": "ipython3", | |||
| "version": "3.8.0" | |||
| } | |||
| }, | |||
| "nbformat": 4, | |||
| "nbformat_minor": 2 | |||
| } | |||
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| { | |||
| "cells": [ | |||
| { | |||
| "cell_type": "markdown", | |||
| "metadata": {}, | |||
| "source": [ | |||
| "# 使用Trainer和Tester快速训练和测试" | |||
| ] | |||
| }, | |||
| { | |||
| "cell_type": "markdown", | |||
| "metadata": {}, | |||
| "source": [ | |||
| "## 数据读入和处理" | |||
| ] | |||
| }, | |||
| { | |||
| "cell_type": "code", | |||
| "execution_count": 1, | |||
| "metadata": {}, | |||
| "outputs": [ | |||
| { | |||
| "name": "stderr", | |||
| "output_type": "stream", | |||
| "text": [ | |||
| "/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" | |||
| ] | |||
| }, | |||
| { | |||
| "name": "stdout", | |||
| "output_type": "stream", | |||
| "text": [ | |||
| "In total 3 datasets:\n", | |||
| "\ttest has 1821 instances.\n", | |||
| "\ttrain has 67349 instances.\n", | |||
| "\tdev has 872 instances.\n", | |||
| "In total 2 vocabs:\n", | |||
| "\twords has 16292 entries.\n", | |||
| "\ttarget has 2 entries.\n", | |||
| "\n", | |||
| "+-----------------------------------+--------+-----------------------------------+---------+\n", | |||
| "| raw_words | target | words | seq_len |\n", | |||
| "+-----------------------------------+--------+-----------------------------------+---------+\n", | |||
| "| hide new secretions from the p... | 1 | [4110, 97, 12009, 39, 2, 6843,... | 7 |\n", | |||
| "+-----------------------------------+--------+-----------------------------------+---------+\n", | |||
| "Vocabulary(['hide', 'new', 'secretions', 'from', 'the']...)\n" | |||
| ] | |||
| } | |||
| ], | |||
| "source": [ | |||
| "from fastNLP.io import SST2Pipe\n", | |||
| "\n", | |||
| "pipe = SST2Pipe()\n", | |||
| "databundle = pipe.process_from_file()\n", | |||
| "vocab = databundle.get_vocab('words')\n", | |||
| "print(databundle)\n", | |||
| "print(databundle.get_dataset('train')[0])\n", | |||
| "print(databundle.get_vocab('words'))" | |||
| ] | |||
| }, | |||
| { | |||
| "cell_type": "code", | |||
| "execution_count": 2, | |||
| "metadata": {}, | |||
| "outputs": [ | |||
| { | |||
| "name": "stdout", | |||
| "output_type": "stream", | |||
| "text": [ | |||
| "4925 872 75\n" | |||
| ] | |||
| } | |||
| ], | |||
| "source": [ | |||
| "train_data = databundle.get_dataset('train')[:5000]\n", | |||
| "train_data, test_data = train_data.split(0.015)\n", | |||
| "dev_data = databundle.get_dataset('dev')\n", | |||
| "print(len(train_data),len(dev_data),len(test_data))" | |||
| ] | |||
| }, | |||
| { | |||
| "cell_type": "code", | |||
| "execution_count": 3, | |||
| "metadata": { | |||
| "scrolled": false | |||
| }, | |||
| "outputs": [ | |||
| { | |||
| "name": "stdout", | |||
| "output_type": "stream", | |||
| "text": [ | |||
| "+-------------+-----------+--------+-------+---------+\n", | |||
| "| field_names | raw_words | target | words | seq_len |\n", | |||
| "+-------------+-----------+--------+-------+---------+\n", | |||
| "| is_input | False | False | True | True |\n", | |||
| "| is_target | False | True | False | False |\n", | |||
| "| ignore_type | | False | False | False |\n", | |||
| "| pad_value | | 0 | 0 | 0 |\n", | |||
| "+-------------+-----------+--------+-------+---------+\n" | |||
| ] | |||
| }, | |||
| { | |||
| "data": { | |||
| "text/plain": [ | |||
| "<prettytable.PrettyTable at 0x7f0db03d0640>" | |||
| ] | |||
| }, | |||
| "execution_count": 3, | |||
| "metadata": {}, | |||
| "output_type": "execute_result" | |||
| } | |||
| ], | |||
| "source": [ | |||
| "train_data.print_field_meta()" | |||
| ] | |||
| }, | |||
| { | |||
| "cell_type": "code", | |||
| "execution_count": 4, | |||
| "metadata": {}, | |||
| "outputs": [], | |||
| "source": [ | |||
| "from fastNLP import AccuracyMetric\n", | |||
| "from fastNLP import Const\n", | |||
| "\n", | |||
| "# metrics=AccuracyMetric() 在本例中与下面这行代码等价\n", | |||
| "metrics=AccuracyMetric(pred=Const.OUTPUT, target=Const.TARGET)" | |||
| ] | |||
| }, | |||
| { | |||
| "cell_type": "markdown", | |||
| "metadata": {}, | |||
| "source": [ | |||
| "## DataSetIter初探" | |||
| ] | |||
| }, | |||
| { | |||
| "cell_type": "code", | |||
| "execution_count": 5, | |||
| "metadata": {}, | |||
| "outputs": [ | |||
| { | |||
| "name": "stdout", | |||
| "output_type": "stream", | |||
| "text": [ | |||
| "batch_x: {'words': tensor([[ 13, 830, 7746, 174, 3, 47, 6, 83, 5752, 15,\n", | |||
| " 2177, 15, 63, 57, 406, 84, 1009, 4973, 27, 17,\n", | |||
| " 13785, 3, 533, 3687, 15623, 39, 375, 8, 15624, 8,\n", | |||
| " 1323, 4398, 7],\n", | |||
| " [ 1045, 11113, 16, 104, 5, 4, 176, 1824, 1704, 3,\n", | |||
| " 2, 18, 11, 4, 1018, 432, 143, 33, 245, 308,\n", | |||
| " 7, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", | |||
| " 0, 0, 0]]), 'seq_len': tensor([33, 21])}\n", | |||
| "batch_y: {'target': tensor([1, 0])}\n", | |||
| "batch_x: {'words': tensor([[ 14, 10, 4, 311, 5, 154, 1418, 609, 7],\n", | |||
| " [ 14, 10, 437, 32, 78, 3, 78, 437, 7]]), 'seq_len': tensor([9, 9])}\n", | |||
| "batch_y: {'target': tensor([0, 1])}\n", | |||
| "batch_x: {'words': tensor([[ 4, 277, 685, 18, 7],\n", | |||
| " [15618, 3204, 5, 1675, 0]]), 'seq_len': tensor([5, 4])}\n", | |||
| "batch_y: {'target': tensor([1, 1])}\n", | |||
| "batch_x: {'words': tensor([[ 2, 155, 3, 4426, 3, 239, 3, 739, 5, 1136,\n", | |||
| " 41, 43, 2427, 736, 2, 648, 10, 15620, 2285, 7],\n", | |||
| " [ 24, 95, 28, 46, 8, 336, 38, 239, 8, 2133,\n", | |||
| " 2, 18, 10, 15622, 1421, 6, 61, 5, 387, 7]]), 'seq_len': tensor([20, 20])}\n", | |||
| "batch_y: {'target': tensor([0, 0])}\n", | |||
| "batch_x: {'words': tensor([[ 879, 96, 8, 1026, 12, 8067, 11, 13623, 8, 15619,\n", | |||
| " 4, 673, 662, 15, 4, 1154, 240, 639, 417, 7],\n", | |||
| " [ 45, 752, 327, 180, 10, 15621, 16, 72, 8904, 9,\n", | |||
| " 1217, 7, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([20, 12])}\n", | |||
| "batch_y: {'target': tensor([0, 1])}\n" | |||
| ] | |||
| } | |||
| ], | |||
| "source": [ | |||
| "from fastNLP import BucketSampler\n", | |||
| "from fastNLP import DataSetIter\n", | |||
| "\n", | |||
| "tmp_data = dev_data[:10]\n", | |||
| "# 定义一个Batch，传入DataSet，规定batch_size和去batch的规则。\n", | |||
| "# 顺序（Sequential），随机（Random），相似长度组成一个batch（Bucket）\n", | |||
| "sampler = BucketSampler(batch_size=2, seq_len_field_name='seq_len')\n", | |||
| "batch = DataSetIter(batch_size=2, dataset=tmp_data, sampler=sampler)\n", | |||
| "for batch_x, batch_y in batch:\n", | |||
| " print(\"batch_x: \",batch_x)\n", | |||
| " print(\"batch_y: \", batch_y)" | |||
| ] | |||
| }, | |||
| { | |||
| "cell_type": "code", | |||
| "execution_count": 6, | |||
| "metadata": {}, | |||
| "outputs": [ | |||
| { | |||
| "name": "stdout", | |||
| "output_type": "stream", | |||
| "text": [ | |||
| "batch_x: {'words': tensor([[ 13, 830, 7746, 174, 3, 47, 6, 83, 5752, 15,\n", | |||
| " 2177, 15, 63, 57, 406, 84, 1009, 4973, 27, 17,\n", | |||
| " 13785, 3, 533, 3687, 15623, 39, 375, 8, 15624, 8,\n", | |||
| " 1323, 4398, 7],\n", | |||
| " [ 1045, 11113, 16, 104, 5, 4, 176, 1824, 1704, 3,\n", | |||
| " 2, 18, 11, 4, 1018, 432, 143, 33, 245, 308,\n", | |||
| " 7, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n", | |||
| " -1, -1, -1]]), 'seq_len': tensor([33, 21])}\n", | |||
| "batch_y: {'target': tensor([1, 0])}\n", | |||
| "batch_x: {'words': tensor([[ 14, 10, 4, 311, 5, 154, 1418, 609, 7],\n", | |||
| " [ 14, 10, 437, 32, 78, 3, 78, 437, 7]]), 'seq_len': tensor([9, 9])}\n", | |||
| "batch_y: {'target': tensor([0, 1])}\n", | |||
| "batch_x: {'words': tensor([[ 2, 155, 3, 4426, 3, 239, 3, 739, 5, 1136,\n", | |||
| " 41, 43, 2427, 736, 2, 648, 10, 15620, 2285, 7],\n", | |||
| " [ 24, 95, 28, 46, 8, 336, 38, 239, 8, 2133,\n", | |||
| " 2, 18, 10, 15622, 1421, 6, 61, 5, 387, 7]]), 'seq_len': tensor([20, 20])}\n", | |||
| "batch_y: {'target': tensor([0, 0])}\n", | |||
| "batch_x: {'words': tensor([[ 4, 277, 685, 18, 7],\n", | |||
| " [15618, 3204, 5, 1675, -1]]), 'seq_len': tensor([5, 4])}\n", | |||
| "batch_y: {'target': tensor([1, 1])}\n", | |||
| "batch_x: {'words': tensor([[ 879, 96, 8, 1026, 12, 8067, 11, 13623, 8, 15619,\n", | |||
| " 4, 673, 662, 15, 4, 1154, 240, 639, 417, 7],\n", | |||
| " [ 45, 752, 327, 180, 10, 15621, 16, 72, 8904, 9,\n", | |||
| " 1217, 7, -1, -1, -1, -1, -1, -1, -1, -1]]), 'seq_len': tensor([20, 12])}\n", | |||
| "batch_y: {'target': tensor([0, 1])}\n" | |||
| ] | |||
| } | |||
| ], | |||
| "source": [ | |||
| "tmp_data.set_pad_val('words',-1)\n", | |||
| "batch = DataSetIter(batch_size=2, dataset=tmp_data, sampler=sampler)\n", | |||
| "for batch_x, batch_y in batch:\n", | |||
| " print(\"batch_x: \",batch_x)\n", | |||
| " print(\"batch_y: \", batch_y)" | |||
| ] | |||
| }, | |||
| { | |||
| "cell_type": "code", | |||
| "execution_count": 7, | |||
| "metadata": {}, | |||
| "outputs": [ | |||
| { | |||
| "name": "stdout", | |||
| "output_type": "stream", | |||
| "text": [ | |||
| "batch_x: {'words': tensor([[ 45, 752, 327, 180, 10, 15621, 16, 72, 8904, 9,\n", | |||
| " 1217, 7, 0, 0, 0, 0, 0, 0, 0, 0,\n", | |||
| " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", | |||
| " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", | |||
| " [ 879, 96, 8, 1026, 12, 8067, 11, 13623, 8, 15619,\n", | |||
| " 4, 673, 662, 15, 4, 1154, 240, 639, 417, 7,\n", | |||
| " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", | |||
| " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([12, 20])}\n", | |||
| "batch_y: {'target': tensor([1, 0])}\n", | |||
| "batch_x: {'words': tensor([[ 13, 830, 7746, 174, 3, 47, 6, 83, 5752, 15,\n", | |||
| " 2177, 15, 63, 57, 406, 84, 1009, 4973, 27, 17,\n", | |||
| " 13785, 3, 533, 3687, 15623, 39, 375, 8, 15624, 8,\n", | |||
| " 1323, 4398, 7, 0, 0, 0, 0, 0, 0, 0],\n", | |||
| " [ 1045, 11113, 16, 104, 5, 4, 176, 1824, 1704, 3,\n", | |||
| " 2, 18, 11, 4, 1018, 432, 143, 33, 245, 308,\n", | |||
| " 7, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", | |||
| " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([33, 21])}\n", | |||
| "batch_y: {'target': tensor([1, 0])}\n", | |||
| "batch_x: {'words': tensor([[ 14, 10, 4, 311, 5, 154, 1418, 609, 7, 0, 0, 0,\n", | |||
| " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", | |||
| " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", | |||
| " 0, 0, 0, 0],\n", | |||
| " [ 14, 10, 437, 32, 78, 3, 78, 437, 7, 0, 0, 0,\n", | |||
| " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", | |||
| " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", | |||
| " 0, 0, 0, 0]]), 'seq_len': tensor([9, 9])}\n", | |||
| "batch_y: {'target': tensor([0, 1])}\n", | |||
| "batch_x: {'words': tensor([[ 2, 155, 3, 4426, 3, 239, 3, 739, 5, 1136,\n", | |||
| " 41, 43, 2427, 736, 2, 648, 10, 15620, 2285, 7,\n", | |||
| " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", | |||
| " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", | |||
| " [ 24, 95, 28, 46, 8, 336, 38, 239, 8, 2133,\n", | |||
| " 2, 18, 10, 15622, 1421, 6, 61, 5, 387, 7,\n", | |||
| " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", | |||
| " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([20, 20])}\n", | |||
| "batch_y: {'target': tensor([0, 0])}\n", | |||
| "batch_x: {'words': tensor([[ 4, 277, 685, 18, 7, 0, 0, 0, 0, 0,\n", | |||
| " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", | |||
| " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", | |||
| " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", | |||
| " [15618, 3204, 5, 1675, 0, 0, 0, 0, 0, 0,\n", | |||
| " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", | |||
| " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", | |||
| " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([5, 4])}\n", | |||
| "batch_y: {'target': tensor([1, 1])}\n" | |||
| ] | |||
| } | |||
| ], | |||
| "source": [ | |||
| "from fastNLP.core.field import Padder\n", | |||
| "import numpy as np\n", | |||
| "class FixLengthPadder(Padder):\n", | |||
| " def __init__(self, pad_val=0, length=None):\n", | |||
| " super().__init__(pad_val=pad_val)\n", | |||
| " self.length = length\n", | |||
| " assert self.length is not None, \"Creating FixLengthPadder with no specific length!\"\n", | |||
| "\n", | |||
| " def __call__(self, contents, field_name, field_ele_dtype, dim):\n", | |||
| " #计算当前contents中的最大长度\n", | |||
| " max_len = max(map(len, contents))\n", | |||
| " #如果当前contents中的最大长度大于指定的padder length的话就报错\n", | |||
| " assert max_len <= self.length, \"Fixed padder length smaller than actual length! with length {}\".format(max_len)\n", | |||
| " array = np.full((len(contents), self.length), self.pad_val, dtype=field_ele_dtype)\n", | |||
| " for i, content_i in enumerate(contents):\n", | |||
| " array[i, :len(content_i)] = content_i\n", | |||
| " return array\n", | |||
| "\n", | |||
| "#设定FixLengthPadder的固定长度为40\n", | |||
| "tmp_padder = FixLengthPadder(pad_val=0,length=40)\n", | |||
| "#利用dataset的set_padder函数设定words field的padder\n", | |||
| "tmp_data.set_padder('words',tmp_padder)\n", | |||
| "batch = DataSetIter(batch_size=2, dataset=tmp_data, sampler=sampler)\n", | |||
| "for batch_x, batch_y in batch:\n", | |||
| " print(\"batch_x: \",batch_x)\n", | |||
| " print(\"batch_y: \", batch_y)" | |||
| ] | |||
| }, | |||
| { | |||
| "cell_type": "markdown", | |||
| "metadata": {}, | |||
| "source": [ | |||
| "## 使用DataSetIter自己编写训练过程\n" | |||
| ] | |||
| }, | |||
| { | |||
| "cell_type": "code", | |||
| "execution_count": 8, | |||
| "metadata": {}, | |||
| "outputs": [ | |||
| { | |||
| "name": "stdout", | |||
| "output_type": "stream", | |||
| "text": [ | |||
| "-----start training-----\n" | |||
| ] | |||
| }, | |||
| { | |||
| "data": { | |||
| "application/vnd.jupyter.widget-view+json": { | |||
| "model_id": "", | |||
| "version_major": 2, | |||
| "version_minor": 0 | |||
| }, | |||
| "text/plain": [ | |||
| "HBox(children=(FloatProgress(value=0.0, layout=Layout(flex='2'), max=55.0), HTML(value='')), layout=Layout(dis…" | |||
| ] | |||
| }, | |||
| "metadata": {}, | |||
| "output_type": "display_data" | |||
| }, | |||
| { | |||
| "name": "stdout", | |||
| "output_type": "stream", | |||
| "text": [ | |||
| "\r", | |||
| "Evaluate data in 2.68 seconds!\n", | |||
| "Epoch 0 Avg Loss: 0.66 AccuracyMetric: acc=0.708716 29307ms\n" | |||
| ] | |||
| }, | |||
| { | |||
| "data": { | |||
| "application/vnd.jupyter.widget-view+json": { | |||
| "model_id": "", | |||
| "version_major": 2, | |||
| "version_minor": 0 | |||
| }, | |||
| "text/plain": [ | |||
| "HBox(children=(FloatProgress(value=0.0, layout=Layout(flex='2'), max=55.0), HTML(value='')), layout=Layout(dis…" | |||
| ] | |||
| }, | |||
| "metadata": {}, | |||
| "output_type": "display_data" | |||
| }, | |||
| { | |||
| "name": "stdout", | |||
| "output_type": "stream", | |||
| "text": [ | |||
| "\r", | |||
| "Evaluate data in 0.38 seconds!\n", | |||
| "Epoch 1 Avg Loss: 0.41 AccuracyMetric: acc=0.770642 52200ms\n" | |||
| ] | |||
| }, | |||
| { | |||
| "data": { | |||
| "application/vnd.jupyter.widget-view+json": { | |||
| "model_id": "", | |||
| "version_major": 2, | |||
| "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": {}, | |||
| "output_type": "display_data" | |||
| }, | |||
| { | |||
| "name": "stdout", | |||
| "output_type": "stream", | |||
| "text": [ | |||
| "\r", | |||
| "Evaluate data in 0.51 seconds!\n", | |||
| "Epoch 2 Avg Loss: 0.16 AccuracyMetric: acc=0.747706 70268ms\n" | |||
| ] | |||
| }, | |||
| { | |||
| "data": { | |||
| "application/vnd.jupyter.widget-view+json": { | |||
| "model_id": "", | |||
| "version_major": 2, | |||
| "version_minor": 0 | |||
| }, | |||
| "text/plain": [ | |||
| "HBox(children=(FloatProgress(value=0.0, layout=Layout(flex='2'), max=55.0), HTML(value='')), layout=Layout(dis…" | |||
| ] | |||
| }, | |||
| "metadata": {}, | |||
| "output_type": "display_data" | |||
| }, | |||
| { | |||
| "name": "stdout", | |||
| "output_type": "stream", | |||
| "text": [ | |||
| "\r", | |||
| "Evaluate data in 0.96 seconds!\n", | |||
| "Epoch 3 Avg Loss: 0.06 AccuracyMetric: acc=0.741972 90349ms\n" | |||
| ] | |||
| }, | |||
| { | |||
| "data": { | |||
| "application/vnd.jupyter.widget-view+json": { | |||
| "model_id": "", | |||
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| "version_minor": 0 | |||
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| "text": [ | |||
| "\r", | |||
| "Evaluate data in 0.04 seconds!\n", | |||
| "[tester] \n", | |||
| "AccuracyMetric: acc=0.786667\n" | |||
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| "metadata": {}, | |||
| "output_type": "execute_result" | |||
| } | |||
| ], | |||
| "source": [ | |||
| "from fastNLP import BucketSampler\n", | |||
| "from fastNLP import DataSetIter\n", | |||
| "from fastNLP.models import CNNText\n", | |||
| "from fastNLP import Tester\n", | |||
| "import torch\n", | |||
| "import time\n", | |||
| "\n", | |||
| "embed_dim = 100\n", | |||
| "model = CNNText((len(vocab),embed_dim), num_classes=2, dropout=0.1)\n", | |||
| "\n", | |||
| "def train(epoch, data, devdata):\n", | |||
| " optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n", | |||
| " lossfunc = torch.nn.CrossEntropyLoss()\n", | |||
| " batch_size = 32\n", | |||
| "\n", | |||
| " # 定义一个Batch，传入DataSet，规定batch_size和去batch的规则。\n", | |||
| " # 顺序（Sequential），随机（Random），相似长度组成一个batch（Bucket）\n", | |||
| " train_sampler = BucketSampler(batch_size=batch_size, seq_len_field_name='seq_len')\n", | |||
| " train_batch = DataSetIter(batch_size=batch_size, dataset=data, sampler=train_sampler)\n", | |||
| "\n", | |||
| " start_time = time.time()\n", | |||
| " print(\"-\"*5+\"start training\"+\"-\"*5)\n", | |||
| " for i in range(epoch):\n", | |||
| " loss_list = []\n", | |||
| " for batch_x, batch_y in train_batch:\n", | |||
| " optimizer.zero_grad()\n", | |||
| " output = model(batch_x['words'])\n", | |||
| " loss = lossfunc(output['pred'], batch_y['target'])\n", | |||
| " loss.backward()\n", | |||
| " optimizer.step()\n", | |||
| " loss_list.append(loss.item())\n", | |||
| "\n", | |||
| " #这里verbose如果为0，在调用Tester对象的test()函数时不输出任何信息，返回评估信息; 如果为1，打印出验证结果，返回评估信息\n", | |||
| " #在调用过Tester对象的test()函数后，调用其_format_eval_results(res)函数，结构化输出验证结果\n", | |||
| " tester_tmp = Tester(devdata, model, metrics=AccuracyMetric(), verbose=0)\n", | |||
| " res=tester_tmp.test()\n", | |||
| "\n", | |||
| " print('Epoch {:d} Avg Loss: {:.2f}'.format(i, sum(loss_list) / len(loss_list)),end=\" \")\n", | |||
| " print(tester_tmp._format_eval_results(res),end=\" \")\n", | |||
| " print('{:d}ms'.format(round((time.time()-start_time)*1000)))\n", | |||
| " loss_list.clear()\n", | |||
| "\n", | |||
| "train(10, train_data, dev_data)\n", | |||
| "#使用tester进行快速测试\n", | |||
| "tester = Tester(test_data, model, metrics=AccuracyMetric())\n", | |||
| "tester.test()" | |||
| ] | |||
| }, | |||
| { | |||
| "cell_type": "code", | |||
| "execution_count": null, | |||
| "metadata": {}, | |||
| "outputs": [], | |||
| "source": [] | |||
| } | |||
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