diff --git a/paddle2.0_docs/image_classification/mnist_lenet_classification.ipynb b/paddle2.0_docs/image_classification/mnist_lenet_classification.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..2e5ba0a50754fc1c2a46afec281d2761039d82e9 --- /dev/null +++ b/paddle2.0_docs/image_classification/mnist_lenet_classification.ipynb @@ -0,0 +1,640 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MNIST数据集使用LeNet进行图像分类\n", + "本示例教程演示如何在MNIST数据集上用LeNet进行图像分类。\n", + "手写数字的MNIST数据集，包含60,000个用于训练的示例和10,000个用于测试的示例。这些数字已经过尺寸标准化并位于图像中心，图像是固定大小(28x28像素)，其值为0到1。该数据集的官方地址为：http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 环境\n", + "本教程基于paddle-develop编写，如果您的环境不是本版本，请先安装paddle-develop版本。" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0.0\n" + ] + } + ], + "source": [ + "import paddle\n", + "print(paddle.__version__)\n", + "paddle.disable_static()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 加载数据集\n", + "我们使用飞桨自带的paddle.dataset完成mnist数据集的加载。" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "download training data and load training data\n", + "load finished\n" + ] + } + ], + "source": [ + "print('download training data and load training data')\n", + "train_dataset = paddle.vision.datasets.MNIST(mode='train')\n", + "test_dataset = paddle.vision.datasets.MNIST(mode='test')\n", + "print('load finished')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "取训练集中的一条数据看一下。" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train_data0 label is: [5]\n" + ] + }, + { + "data": { + 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "train_data0, train_label_0 = train_dataset[0][0],train_dataset[0][1]\n", + "train_data0 = train_data0.reshape([28,28])\n", + "plt.figure(figsize=(2,2))\n", + "plt.imshow(train_data0, cmap=plt.cm.binary)\n", + "print('train_data0 label is: ' + str(train_label_0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2.组网\n", + "用paddle.nn下的API，如`Conv2d`、`Pool2D`、`Linead`完成LeNet的构建。" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "import paddle\n", + "import paddle.nn.functional as F\n", + "class LeNet(paddle.nn.Layer):\n", + " def __init__(self):\n", + " super(LeNet, self).__init__()\n", + " self.conv1 = paddle.nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, stride=1, padding=2)\n", + " self.max_pool1 = paddle.nn.Pool2D(pool_size=2, pool_type='max', pool_stride=2)\n", + " self.conv2 = paddle.nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, stride=1)\n", + " self.max_pool2 = paddle.nn.Pool2D(pool_size=2, pool_type='max', pool_stride=2)\n", + " self.linear1 = paddle.nn.Linear(in_features=16*5*5, out_features=120)\n", + " self.linear2 = paddle.nn.Linear(in_features=120, out_features=84)\n", + " self.linear3 = paddle.nn.Linear(in_features=84, out_features=10)\n", + "\n", + " def forward(self, x):\n", + " x = self.conv1(x)\n", + " x = F.relu(x)\n", + " x = self.max_pool1(x)\n", + " x = F.relu(x)\n", + " x = self.conv2(x)\n", + " x = self.max_pool2(x)\n", + " x = paddle.reshape(x, shape=[-1, 16*5*5])\n", + " x = self.linear1(x)\n", + " x = F.relu(x)\n", + " x = self.linear2(x)\n", + " x = F.relu(x)\n", + " x = self.linear3(x)\n", + " x = F.softmax(x)\n", + " return x" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 训练方式一\n", + "通过`Model` 构建实例，快速完成模型训练" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "import paddle\n", + "from paddle.static import InputSpec\n", + "from paddle.metric import Accuracy\n", + "inputs = InputSpec([None, 784], 'float32', 'x')\n", + "labels = InputSpec([None, 10], 'float32', 'x')\n", + "model = paddle.hapi.Model(LeNet(), inputs, labels)\n", + "optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())\n", + "\n", + "\n", + "model.prepare(\n", + " optim,\n", + " paddle.nn.loss.CrossEntropyLoss(),\n", + " Accuracy(topk=(1, 2))\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 使用model.fit来训练模型" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/2\n", + "step 10/938 - loss: 2.2369 - acc_top1: 0.3281 - acc_top2: 0.4172 - 18ms/step\n" + ] + }, + { + "name": "stderr", + 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"Eval begin...\n", + "step 10/157 - loss: 1.5196 - acc_top1: 0.9719 - acc_top2: 0.9969 - 5ms/step\n", + "step 20/157 - loss: 1.5393 - acc_top1: 0.9672 - acc_top2: 0.9945 - 6ms/step\n", + "step 30/157 - loss: 1.4928 - acc_top1: 0.9630 - acc_top2: 0.9906 - 5ms/step\n", + "step 40/157 - loss: 1.4765 - acc_top1: 0.9617 - acc_top2: 0.9902 - 5ms/step\n", + "step 50/157 - loss: 1.4646 - acc_top1: 0.9631 - acc_top2: 0.9903 - 5ms/step\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step 60/157 - loss: 1.5646 - acc_top1: 0.9641 - acc_top2: 0.9906 - 5ms/step\n", + "step 70/157 - loss: 1.5167 - acc_top1: 0.9618 - acc_top2: 0.9900 - 5ms/step\n", + "step 80/157 - loss: 1.4728 - acc_top1: 0.9635 - acc_top2: 0.9906 - 5ms/step\n", + "step 90/157 - loss: 1.5030 - acc_top1: 0.9668 - acc_top2: 0.9917 - 5ms/step\n", + "step 100/157 - loss: 1.4612 - acc_top1: 0.9677 - acc_top2: 0.9914 - 5ms/step\n", + "step 110/157 - loss: 1.4612 - acc_top1: 0.9689 - acc_top2: 0.9913 - 5ms/step\n", + "step 120/157 - loss: 1.4612 - acc_top1: 0.9707 - acc_top2: 0.9919 - 5ms/step\n", + "step 130/157 - loss: 1.4621 - acc_top1: 0.9719 - acc_top2: 0.9923 - 5ms/step\n", + "step 140/157 - loss: 1.4612 - acc_top1: 0.9734 - acc_top2: 0.9929 - 5ms/step\n", + "step 150/157 - loss: 1.4660 - acc_top1: 0.9748 - acc_top2: 0.9933 - 5ms/step\n", + "step 157/157 - loss: 1.5215 - acc_top1: 0.9731 - acc_top2: 0.9930 - 5ms/step\n", + "Eval samples: 10000\n", + "save checkpoint at /Users/chenlong/online_repo/book/paddle2.0_docs/image_classification/mnist_checkpoint/final\n" + ] + } + ], + "source": [ + "model.fit(train_dataset,\n", + " test_dataset,\n", + " epochs=2,\n", + " batch_size=64,\n", + " save_dir='mnist_checkpoint')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 训练方式1结束\n", + "以上就是训练方式1，可以非常快速的完成网络模型训练。此外，paddle还可以用下面的方式来完成模型的训练。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3.训练方式2\n", + "方式1可以快速便捷的完成训练，隐藏了训练时的细节。而方式2则可以用最基本的方式，完成模型的训练。具体如下。" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch: 0, batch_id: 0, loss is: [2.300888], acc is: [0.28125]\n", + "epoch: 0, batch_id: 100, loss is: [1.6948285], acc is: [0.8125]\n", + "epoch: 0, batch_id: 200, loss is: [1.5282547], acc is: [0.96875]\n", + "epoch: 0, batch_id: 300, loss is: [1.509404], acc is: [0.96875]\n", + "epoch: 0, batch_id: 400, loss is: [1.4973292], acc is: [1.]\n", + "epoch: 0, batch_id: 500, loss is: [1.5063374], acc is: [0.984375]\n", + "epoch: 0, batch_id: 600, loss is: [1.490077], acc is: [0.984375]\n", + "epoch: 0, batch_id: 700, loss is: [1.5206413], acc is: [0.984375]\n", + "epoch: 0, batch_id: 800, loss is: [1.5104291], acc is: [1.]\n", + "epoch: 0, batch_id: 900, loss is: [1.5216607], acc is: [0.96875]\n", + "epoch: 1, batch_id: 0, loss is: [1.4949667], acc is: [0.984375]\n", + "epoch: 1, batch_id: 100, loss is: [1.4923338], acc is: [0.96875]\n", + "epoch: 1, batch_id: 200, loss is: [1.5026703], acc is: [1.]\n", + "epoch: 1, batch_id: 300, loss is: [1.4965419], acc is: [0.984375]\n", + "epoch: 1, batch_id: 400, loss is: [1.5270758], acc is: [1.]\n", + "epoch: 1, batch_id: 500, loss is: [1.4774603], acc is: [1.]\n", + "epoch: 1, batch_id: 600, loss is: [1.4762554], acc is: [0.984375]\n", + "epoch: 1, batch_id: 700, loss is: [1.4773959], acc is: [0.984375]\n", + "epoch: 1, batch_id: 800, loss is: [1.5044193], acc is: [1.]\n", + "epoch: 1, batch_id: 900, loss is: [1.4986757], acc is: [0.96875]\n" + ] + } + ], + "source": [ + "import paddle\n", + "train_loader = paddle.io.DataLoader(train_dataset, places=paddle.CPUPlace(), batch_size=64)\n", + "def train(model):\n", + " model.train()\n", + " epochs = 2\n", + " batch_size = 64\n", + " optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())\n", + " for epoch in range(epochs):\n", + " for batch_id, data in enumerate(train_loader()):\n", + " x_data = data[0]\n", + " y_data = data[1]\n", + " predicts = model(x_data)\n", + " loss = paddle.nn.functional.cross_entropy(predicts, y_data)\n", + " acc = paddle.metric.accuracy(predicts, y_data, k=2)\n", + " avg_loss = paddle.mean(loss)\n", + " avg_acc = paddle.mean(acc)\n", + " avg_loss.backward()\n", + " if batch_id % 100 == 0:\n", + " print(\"epoch: {}, batch_id: {}, loss is: {}, acc is: {}\".format(epoch, batch_id, avg_loss.numpy(), avg_acc.numpy()))\n", + " optim.minimize(avg_loss)\n", + " model.clear_gradients()\n", + "model = LeNet()\n", + "train(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 对模型进行验证" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "batch_id: 0, loss is: [1.5017498], acc is: [1.]\n", + "batch_id: 100, loss is: [1.4783669], acc is: [0.984375]\n", + "batch_id: 200, loss is: [1.4958509], acc is: [1.]\n", + "batch_id: 300, loss is: [1.4924574], acc is: [1.]\n", + "batch_id: 400, loss is: [1.4762049], acc is: [1.]\n", + "batch_id: 500, loss is: [1.4817208], acc is: [0.984375]\n", + "batch_id: 600, loss is: [1.4763825], acc is: [0.984375]\n", + "batch_id: 700, loss is: [1.4954926], acc is: [1.]\n", + "batch_id: 800, loss is: [1.5220823], acc is: [0.984375]\n", + "batch_id: 900, loss is: [1.4945463], acc is: [0.984375]\n" + ] + } + ], + "source": [ + "import paddle\n", + "test_loader = paddle.io.DataLoader(test_dataset, places=paddle.CPUPlace(), batch_size=64)\n", + "def test(model):\n", + " model.eval()\n", + " batch_size = 64\n", + " for batch_id, data in enumerate(train_loader()):\n", + " x_data = data[0]\n", + " y_data = data[1]\n", + " predicts = model(x_data)\n", + " loss = paddle.nn.functional.cross_entropy(predicts, y_data)\n", + " acc = paddle.metric.accuracy(predicts, y_data, k=2)\n", + " avg_loss = paddle.mean(loss)\n", + " avg_acc = paddle.mean(acc)\n", + " avg_loss.backward()\n", + " if batch_id % 100 == 0:\n", + " print(\"batch_id: {}, loss is: {}, acc is: {}\".format(batch_id, avg_loss.numpy(), avg_acc.numpy()))\n", + "test(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 训练方式2结束\n", + "以上就是训练方式2，通过这种方式，可以清楚的看到训练和测试中的每一步过程。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 总结\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "以上就是用LeNet对手写数字数据及MNIST进行分类。本示例提供了两种训练模型的方式，一种可以快速完成模型的组建与预测，非常适合新手用户上手。另一种则需要多个步骤来完成模型的训练，适合进阶用户使用。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/paddle2.0_docs/n_gram_model/n_gram_model.ipynb b/paddle2.0_docs/n_gram_model/n_gram_model.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..23a6bb8a31bb0e8fb9e97fedfc1d8917ead56766 --- /dev/null +++ b/paddle2.0_docs/n_gram_model/n_gram_model.ipynb @@ -0,0 +1,425 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 用N-Gram模型在莎士比亚文集中训练word embedding\n", + "N-gram 是计算机语言学和概率论范畴内的概念，是指给定的一段文本中N个项目的序列。\n", + "N=1 时 N-gram 又称为 unigram，N=2 称为 bigram，N=3 称为 trigram，以此类推。实际应用通常采用 bigram 和 trigram 进行计算。\n", + "本示例在莎士比亚文集上实现了trigram。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 环境\n", + "本教程基于paddle-develop编写，如果您的环境不是本版本，请先安装paddle-develop。" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'0.0.0'" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import paddle\n", + "paddle.__version__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 数据集&&相关参数\n", + "训练数据集采用了莎士比亚文集，[下载](https://storage.googleapis.com/download.tensorflow.org/data/shakespeare.txt)，保存为txt格式即可。
\n", + "context_size设为2，意味着是trigram。embedding_dim设为256。" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "embedding_dim = 256\n", + "context_size = 2" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Length of text: 1115394 characters\n" + ] + } + ], + "source": [ + "# 文件路径\n", + "path_to_file = './shakespeare.txt'\n", + "test_sentence = open(path_to_file, 'rb').read().decode(encoding='utf-8')\n", + "\n", + "# 文本长度是指文本中的字符个数\n", + "print ('Length of text: {} characters'.format(len(test_sentence)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 去除标点符号\n", + "用`string`库中的punctuation，完成英文符号的替换。" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'!': '', '\"': '', '#': '', '$': '', '%': '', '&': '', \"'\": '', '(': '', ')': '', '*': '', '+': '', ',': '', '-': '', '.': '', '/': '', ':': '', ';': '', '<': '', '=': '', '>': '', '?': '', '@': '', '[': '', '\\\\': '', ']': '', '^': '', '_': '', '`': '', '{': '', '|': '', '}': '', '~': ''}\n" + ] + } + ], + "source": [ + "from string import punctuation\n", + "process_dicts={i:'' for i in punctuation}\n", + "print(process_dicts)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12848\n" + ] + } + ], + "source": [ + "punc_table = str.maketrans(process_dicts)\n", + "test_sentence = test_sentence.translate(punc_table)\n", + "test_sentence = test_sentence.lower().split()\n", + "vocab = set(test_sentence)\n", + "print(len(vocab))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 数据预处理\n", + "将文本被拆成了元组的形式，格式为(('第一个词', '第二个词'), '第三个词');其中，第三个词就是我们的目标。" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[['first', 'citizen'], 'before'], [['citizen', 'before'], 'we'], [['before', 'we'], 'proceed']]\n" + ] + } + ], + "source": [ + "trigram = [[[test_sentence[i], test_sentence[i + 1]], test_sentence[i + 2]]\n", + " for i in range(len(test_sentence) - 2)]\n", + "\n", + "word_to_idx = {word: i for i, word in enumerate(vocab)}\n", + "idx_to_word = {word_to_idx[word]: word for word in word_to_idx}\n", + "# 看一下数据集\n", + "print(trigram[:3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 构建`Dataset`类 加载数据\n", + "用`paddle.io.Dataset`构建数据集，然后作为参数传入到`paddle.io.DataLoader`，完成数据集的加载。" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "import paddle\n", + "import numpy as np\n", + "batch_size = 256\n", + "paddle.disable_static()\n", + "class TrainDataset(paddle.io.Dataset):\n", + " def __init__(self, tuple_data):\n", + " self.tuple_data = tuple_data\n", + "\n", + " def __getitem__(self, idx):\n", + " data = self.tuple_data[idx][0]\n", + " label = self.tuple_data[idx][1]\n", + " data = np.array(list(map(lambda w: word_to_idx[w], data)))\n", + " label = np.array(word_to_idx[label])\n", + " return data, label\n", + " \n", + " def __len__(self):\n", + " return len(self.tuple_data)\n", + "train_dataset = TrainDataset(trigram)\n", + "train_loader = paddle.io.DataLoader(train_dataset,places=paddle.fluid.cpu_places(), return_list=True,\n", + " shuffle=True, batch_size=batch_size, drop_last=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 组网&训练\n", + "这里用paddle动态图的方式组网。为了构建Trigram模型，用一层 `Embedding` 与两层 `Linear` 完成构建。`Embedding` 层对输入的前两个单词embedding，然后输入到后面的两个`Linear`层中，完成特征提取。" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "import paddle\n", + "import numpy as np\n", + "hidden_size = 1024\n", + "class NGramModel(paddle.nn.Layer):\n", + " def __init__(self, vocab_size, embedding_dim, context_size):\n", + " super(NGramModel, self).__init__()\n", + " self.embedding = paddle.nn.Embedding(size=[vocab_size, embedding_dim])\n", + " self.linear1 = paddle.nn.Linear(context_size * embedding_dim, hidden_size)\n", + " self.linear2 = paddle.nn.Linear(hidden_size, len(vocab))\n", + "\n", + " def forward(self, x):\n", + " x = self.embedding(x)\n", + " x = paddle.reshape(x, [-1, context_size * embedding_dim])\n", + " x = self.linear1(x)\n", + " x = paddle.nn.functional.relu(x)\n", + " x = self.linear2(x)\n", + " return x" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 定义`train()`函数，对模型进行训练。" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch: 0, batch_id: 0, loss is: [9.460927]\n", + "epoch: 0, batch_id: 100, loss is: [6.8197966]\n", + "epoch: 0, batch_id: 200, loss is: [7.0330124]\n", + "epoch: 0, batch_id: 300, loss is: [6.9797225]\n", + "epoch: 0, batch_id: 400, loss is: [6.9343157]\n", + "epoch: 0, batch_id: 500, loss is: [7.0574594]\n", + "epoch: 0, batch_id: 600, loss is: [6.646331]\n", + "epoch: 0, batch_id: 700, loss is: [6.867292]\n", + "epoch: 1, batch_id: 0, loss is: [6.328604]\n", + "epoch: 1, batch_id: 100, loss is: [6.5479784]\n", + "epoch: 1, batch_id: 200, loss is: [6.1158056]\n", + "epoch: 1, batch_id: 300, loss is: [6.1452174]\n", + "epoch: 1, batch_id: 400, loss is: [6.0391626]\n", + "epoch: 1, batch_id: 500, loss is: [6.3031235]\n", + "epoch: 1, batch_id: 600, loss is: [6.2294545]\n", + "epoch: 1, batch_id: 700, loss is: [7.0351915]\n", + "epoch: 2, batch_id: 0, loss is: [5.9047413]\n", + "epoch: 2, batch_id: 100, loss is: [5.9303236]\n", + "epoch: 2, batch_id: 200, loss is: [6.0062647]\n", + "epoch: 2, batch_id: 300, loss is: [5.923294]\n", + "epoch: 2, batch_id: 400, loss is: [6.0315046]\n", + "epoch: 2, batch_id: 500, loss is: [6.00991]\n", + "epoch: 2, batch_id: 600, loss is: [6.2324004]\n", + "epoch: 2, batch_id: 700, loss is: [5.924728]\n", + "epoch: 3, batch_id: 0, loss is: [5.9861994]\n", + "epoch: 3, batch_id: 100, loss is: [5.886199]\n", + "epoch: 3, batch_id: 200, loss is: [6.216494]\n", + "epoch: 3, batch_id: 300, loss is: [6.066014]\n", + "epoch: 3, batch_id: 400, loss is: [5.7648573]\n", + "epoch: 3, batch_id: 500, loss is: [6.0842447]\n", + "epoch: 3, batch_id: 600, loss is: [6.032789]\n", + "epoch: 3, batch_id: 700, loss is: [5.875885]\n", + "epoch: 4, batch_id: 0, loss is: [5.923893]\n", + "epoch: 4, batch_id: 100, loss is: [5.838437]\n", + "epoch: 4, batch_id: 200, loss is: [5.8900037]\n", + "epoch: 4, batch_id: 300, loss is: [5.826645]\n", + "epoch: 4, batch_id: 400, loss is: [5.978036]\n", + "epoch: 4, batch_id: 500, loss is: [6.1803474]\n", + "epoch: 4, batch_id: 600, loss is: [5.9366255]\n", + "epoch: 4, batch_id: 700, loss is: [6.2263923]\n" + ] + } + ], + "source": [ + "import time\n", + "vocab_size = len(vocab)\n", + "epochs = 5\n", + "losses = []\n", + "def train(model):\n", + " model.train()\n", + " optim = paddle.optimizer.Adam(learning_rate=0.01, parameters=model.parameters())\n", + " for epoch in range(epochs):\n", + " for batch_id, data in enumerate(train_loader()):\n", + " x_data = data[0]\n", + " y_data = data[1]\n", + " predicts = model(x_data)\n", + " y_data = paddle.nn.functional.one_hot(y_data, len(vocab))\n", + " loss = paddle.nn.functional.softmax_with_cross_entropy(predicts, y_data,soft_label=True)\n", + " avg_loss = paddle.mean(loss)\n", + " avg_loss.backward()\n", + " if batch_id % 100 == 0:\n", + " losses.append(avg_loss.numpy())\n", + " print(\"epoch: {}, batch_id: {}, loss is: {}\".format(epoch, batch_id, avg_loss.numpy())) \n", + " optim.minimize(avg_loss)\n", + " model.clear_gradients()\n", + "model = NGramModel(vocab_size, embedding_dim, context_size)\n", + "train(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 打印loss下降曲线\n", + "通过可视化loss的曲线，可以看到模型训练的效果。" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.ticker as ticker\n", + "%matplotlib inline\n", + "\n", + "plt.figure()\n", + "plt.plot(losses)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 预测\n", + "用训练好的模型进行预测。" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the input words is: whiles, thou\n", + "the predict words is: art\n", + "the true words is: art\n" + ] + } + ], + "source": [ + "import random\n", + "def test(model):\n", + " model.eval()\n", + " # 从最后10组数据中随机选取1个\n", + " idx = random.randint(len(trigram)-10, len(trigram)-1)\n", + " print('the input words is: ' + trigram[idx][0][0] + ', ' + trigram[idx][0][1])\n", + " x_data = list(map(lambda w: word_to_idx[w], trigram[idx][0]))\n", + " x_data = paddle.to_tensor(np.array(x_data))\n", + " predicts = model(x_data)\n", + " predicts = predicts.numpy().tolist()[0]\n", + " predicts = predicts.index(max(predicts))\n", + " print('the predict words is: ' + idx_to_word[predicts])\n", + " y_data = trigram[idx][1]\n", + " print('the true words is: ' + y_data)\n", + "test(model)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}