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未验证 提交 601a9386 编写于 作者: C Chen Long 提交者： GitHub
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paddle2.0_docs/image_classification/mnist_lenet_classification.ipynb
浏览文件 @ 601a9386
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"cell_type": "markdown",
"metadata": {},
"source": [
"# 环境\n",
"## 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": 35,
"execution_count": 1,
"metadata": {},
"outputs": [
{
......@@ -40,13 +40,13 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# 加载数据集\n",
"## 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": 36,
"execution_count": 3,
"metadata": {},
"outputs": [
{
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},
{
"cell_type": "code",
"execution_count": 37,
"execution_count": 4,
"metadata": {},
"outputs": [
{
......@@ -111,13 +111,13 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2.组网\n",
"## 组网\n",
"用paddle.nn下的API，如`Conv2d`、`Pool2D`、`Linead`完成LeNet的构建。"
]
},
{
"cell_type": "code",
"execution_count": 38,
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
......@@ -155,39 +155,39 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3.训练方式一\n",
"## 训练方式一\n",
"组网后，开始对模型进行训练，先构建`train_loader`，加载训练数据，然后定义`train`函数，设置好损失函数后，按batch加载数据，完成模型的训练。"
]
},
{
"cell_type": "code",
"execution_count": 39,
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch: 0, batch_id: 0, loss is: [2.3064885], acc is: [0.109375]\n",
"epoch: 0, batch_id: 100, loss is: [1.5477252], acc is: [1.]\n",
"epoch: 0, batch_id: 200, loss is: [1.5201148], acc is: [1.]\n",
"epoch: 0, batch_id: 300, loss is: [1.525354], acc is: [0.953125]\n",
"epoch: 0, batch_id: 400, loss is: [1.5201038], acc is: [1.]\n",
"epoch: 0, batch_id: 500, loss is: [1.4901408], acc is: [1.]\n",
"epoch: 0, batch_id: 600, loss is: [1.4925538], acc is: [0.984375]\n",
"epoch: 0, batch_id: 700, loss is: [1.5247533], acc is: [0.96875]\n",
"epoch: 0, batch_id: 800, loss is: [1.5365943], acc is: [1.]\n",
"epoch: 0, batch_id: 900, loss is: [1.5154861], acc is: [0.984375]\n",
"epoch: 1, batch_id: 0, loss is: [1.4988302], acc is: [0.984375]\n",
"epoch: 1, batch_id: 100, loss is: [1.493154], acc is: [0.984375]\n",
"epoch: 1, batch_id: 200, loss is: [1.4974915], acc is: [1.]\n",
"epoch: 1, batch_id: 300, loss is: [1.5089471], acc is: [0.984375]\n",
"epoch: 1, batch_id: 400, loss is: [1.5041347], acc is: [1.]\n",
"epoch: 1, batch_id: 500, loss is: [1.5145375], acc is: [1.]\n",
"epoch: 1, batch_id: 600, loss is: [1.4904011], acc is: [0.984375]\n",
"epoch: 1, batch_id: 700, loss is: [1.5121607], acc is: [0.96875]\n",
"epoch: 1, batch_id: 800, loss is: [1.5078678], acc is: [1.]\n",
"epoch: 1, batch_id: 900, loss is: [1.500349], acc is: [0.984375]\n"
"epoch: 0, batch_id: 0, loss is: [2.3029077], acc is: [0.15625]\n",
"epoch: 0, batch_id: 100, loss is: [1.6757016], acc is: [0.84375]\n",
"epoch: 0, batch_id: 200, loss is: [1.5340967], acc is: [0.96875]\n",
"epoch: 0, batch_id: 300, loss is: [1.4943825], acc is: [0.984375]\n",
"epoch: 0, batch_id: 400, loss is: [1.5084226], acc is: [1.]\n",
"epoch: 0, batch_id: 500, loss is: [1.5035012], acc is: [0.984375]\n",
"epoch: 0, batch_id: 600, loss is: [1.4784969], acc is: [0.984375]\n",
"epoch: 0, batch_id: 700, loss is: [1.5656701], acc is: [0.96875]\n",
"epoch: 0, batch_id: 800, loss is: [1.5226105], acc is: [1.]\n",
"epoch: 0, batch_id: 900, loss is: [1.5094678], acc is: [1.]\n",
"epoch: 1, batch_id: 0, loss is: [1.4956206], acc is: [0.984375]\n",
"epoch: 1, batch_id: 100, loss is: [1.4908005], acc is: [1.]\n",
"epoch: 1, batch_id: 200, loss is: [1.485649], acc is: [0.984375]\n",
"epoch: 1, batch_id: 300, loss is: [1.5090752], acc is: [1.]\n",
"epoch: 1, batch_id: 400, loss is: [1.5163708], acc is: [1.]\n",
"epoch: 1, batch_id: 500, loss is: [1.4863018], acc is: [0.984375]\n",
"epoch: 1, batch_id: 600, loss is: [1.4764814], acc is: [0.984375]\n",
"epoch: 1, batch_id: 700, loss is: [1.5496588], acc is: [0.984375]\n",
"epoch: 1, batch_id: 800, loss is: [1.4998187], acc is: [1.]\n",
"epoch: 1, batch_id: 900, loss is: [1.5110929], acc is: [1.]\n"
]
}
],
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},
{
"cell_type": "code",
"execution_count": 40,
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"batch_id: 0, loss is: [1.4659549], acc is: [1.]\n",
"batch_id: 100, loss is: [1.4933192], acc is: [0.984375]\n",
"batch_id: 200, loss is: [1.4779761], acc is: [1.]\n",
"batch_id: 300, loss is: [1.4919193], acc is: [0.984375]\n",
"batch_id: 400, loss is: [1.5036212], acc is: [1.]\n",
"batch_id: 500, loss is: [1.4922347], acc is: [0.984375]\n",
"batch_id: 600, loss is: [1.4765416], acc is: [0.984375]\n",
"batch_id: 700, loss is: [1.4997746], acc is: [0.984375]\n",
"batch_id: 800, loss is: [1.4831288], acc is: [1.]\n",
"batch_id: 900, loss is: [1.498342], acc is: [0.984375]\n"
"batch_id: 0, loss is: [1.4929559], acc is: [0.984375]\n",
"batch_id: 100, loss is: [1.4921299], acc is: [0.984375]\n",
"batch_id: 200, loss is: [1.5021144], acc is: [1.]\n",
"batch_id: 300, loss is: [1.4809179], acc is: [0.984375]\n",
"batch_id: 400, loss is: [1.4768506], acc is: [1.]\n",
"batch_id: 500, loss is: [1.4768407], acc is: [1.]\n",
"batch_id: 600, loss is: [1.476671], acc is: [0.984375]\n",
"batch_id: 700, loss is: [1.5093586], acc is: [1.]\n",
"batch_id: 800, loss is: [1.5057312], acc is: [1.]\n",
"batch_id: 900, loss is: [1.4923737], acc is: [1.]\n"
]
}
],
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},
{
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"metadata": {},
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"source": [
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},
{
"cell_type": "code",
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"execution_count": 11,
"metadata": {},
"outputs": [
{
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"Epoch 1/2\n",
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"save checkpoint at /Users/chenlong/online_repo/book/paddle2.0_docs/image_classification/mnist_checkpoint/0\n",
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{
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"step 880/938 - loss: 1.4772 - acc_top1: 0.9765 - acc_top2: 0.9935 - 14ms/step\n",
"step 890/938 - loss: 1.4646 - acc_top1: 0.9766 - acc_top2: 0.9936 - 14ms/step\n",
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"step 938/938 - loss: 1.4617 - acc_top1: 0.9769 - acc_top2: 0.9937 - 14ms/step\n",
"save checkpoint at /Users/chenlong/online_repo/book/paddle2.0_docs/image_classification/mnist_checkpoint/1\n",
"save checkpoint at /Users/chenlong/online_repo/book/paddle2.0_docs/image_classification/mnist_checkpoint/final\n"
]
......@@ -542,7 +542,7 @@
},
{
"cell_type": "code",
"execution_count": 43,
"execution_count": 12,
"metadata": {},
"outputs": [
{
......@@ -550,32 +550,32 @@
"output_type": "stream",
"text": [
"Eval begin...\n",
"step 10/157 - loss: 1.5447 - acc_top1: 0.8953 - acc_top2: 0.9078 - 5ms/step\n",
"step 20/157 - loss: 1.6185 - acc_top1: 0.8930 - acc_top2: 0.9078 - 5ms/step\n",
"step 30/157 - loss: 1.6497 - acc_top1: 0.8917 - acc_top2: 0.9057 - 5ms/step\n",
"step 40/157 - loss: 1.6318 - acc_top1: 0.8902 - acc_top2: 0.9055 - 5ms/step\n",
"step 50/157 - loss: 1.5533 - acc_top1: 0.8856 - acc_top2: 0.9012 - 5ms/step\n",
"step 60/157 - loss: 1.6212 - acc_top1: 0.8878 - acc_top2: 0.9036 - 5ms/step\n",
"step 70/157 - loss: 1.5674 - acc_top1: 0.8839 - acc_top2: 0.9002 - 5ms/step\n",
"step 80/157 - loss: 1.5409 - acc_top1: 0.8891 - acc_top2: 0.9043 - 5ms/step\n",
"step 90/157 - loss: 1.6133 - acc_top1: 0.8903 - acc_top2: 0.9045 - 5ms/step\n",
"step 100/157 - loss: 1.5535 - acc_top1: 0.8909 - acc_top2: 0.9044 - 5ms/step\n",
"step 110/157 - loss: 1.5690 - acc_top1: 0.8916 - acc_top2: 0.9054 - 5ms/step\n",
"step 120/157 - loss: 1.6147 - acc_top1: 0.8926 - acc_top2: 0.9055 - 5ms/step\n",
"step 130/157 - loss: 1.5203 - acc_top1: 0.8944 - acc_top2: 0.9066 - 5ms/step\n",
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"step 150/157 - loss: 1.5536 - acc_top1: 0.8958 - acc_top2: 0.9072 - 5ms/step\n",
"step 157/157 - loss: 1.5855 - acc_top1: 0.8956 - acc_top2: 0.9076 - 5ms/step\n",
"step 10/157 - loss: 1.5023 - acc_top1: 0.9781 - acc_top2: 0.9969 - 7ms/step\n",
"step 20/157 - loss: 1.5326 - acc_top1: 0.9750 - acc_top2: 0.9953 - 7ms/step\n",
"step 30/157 - loss: 1.4881 - acc_top1: 0.9745 - acc_top2: 0.9943 - 7ms/step\n",
"step 40/157 - loss: 1.4703 - acc_top1: 0.9715 - acc_top2: 0.9934 - 6ms/step\n",
"step 50/157 - loss: 1.4793 - acc_top1: 0.9728 - acc_top2: 0.9934 - 6ms/step\n",
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"step 70/157 - loss: 1.4801 - acc_top1: 0.9721 - acc_top2: 0.9922 - 6ms/step\n",
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"step 100/157 - loss: 1.4780 - acc_top1: 0.9758 - acc_top2: 0.9939 - 6ms/step\n",
"step 110/157 - loss: 1.4686 - acc_top1: 0.9763 - acc_top2: 0.9942 - 6ms/step\n",
"step 120/157 - loss: 1.4624 - acc_top1: 0.9780 - acc_top2: 0.9947 - 6ms/step\n",
"step 130/157 - loss: 1.4968 - acc_top1: 0.9787 - acc_top2: 0.9948 - 6ms/step\n",
"step 140/157 - loss: 1.4612 - acc_top1: 0.9798 - acc_top2: 0.9952 - 6ms/step\n",
"step 150/157 - loss: 1.4613 - acc_top1: 0.9806 - acc_top2: 0.9955 - 6ms/step\n",
"step 157/157 - loss: 1.4612 - acc_top1: 0.9803 - acc_top2: 0.9955 - 6ms/step\n",
"Eval samples: 10000\n"
]
},
{
"data": {
"text/plain": [
"{'loss': [1.585474], 'acc_top1': 0.8956, 'acc_top2': 0.9076}"
"{'loss': [1.4611506], 'acc_top1': 0.9803, 'acc_top2': 0.9955}"
]
},
"execution_count": 43,
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
......@@ -605,13 +605,6 @@
"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": {
......
paddle2.0_docs/n_gram_model/n_gram_model.ipynb
浏览文件 @ 601a9386
......@@ -5,7 +5,7 @@
"metadata": {},
"source": [
"\n",
"## 用N-Gram模型在莎士比亚文集中训练word embedding\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。"
......@@ -15,13 +15,13 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# 环境\n",
"## 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": 17,
"execution_count": 1,
"metadata": {},
"outputs": [
{
......@@ -30,7 +30,7 @@
"'0.0.0'"
]
},
"execution_count": 17,
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
......@@ -51,23 +51,22 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2020-09-03 08:41:10-- https://ocw.mit.edu/ans7870/6/6.006/s08/lecturenotes/files/t8.shakespeare.txt\n",
"正在解析主机 ocw.mit.edu (ocw.mit.edu)... 151.101.230.133\n",
"正在连接 ocw.mit.edu (ocw.mit.edu)|151.101.230.133|:443... 已连接。\n",
"已发出 HTTP 请求，正在等待回应... 200 OK\n",
"--2020-09-08 19:07:26-- https://ocw.mit.edu/ans7870/6/6.006/s08/lecturenotes/files/t8.shakespeare.txt\n",
"正在连接 172.19.57.45:3128... 已连接。\n",
"已发出 Proxy 请求，正在等待回应... 200 OK\n",
"长度：5458199 (5.2M) [text/plain]\n",
"正在保存至: “t8.shakespeare.txt.1”\n",
"正在保存至: “t8.shakespeare.txt”\n",
"\n",
"t8.shakespeare.txt. 100%[===================>] 5.21M 26.1KB/s 用时 4m 14s \n",
"t8.shakespeare.txt 100%[===================>] 5.21M 862KB/s 用时 7.1s \n",
"\n",
"2020-09-03 08:45:25 (21.0 KB/s) - 已保存 “t8.shakespeare.txt.1” [5458199/5458199])\n",
"2020-09-08 19:07:34 (755 KB/s) - 已保存 “t8.shakespeare.txt” [5458199/5458199])\n",
"\n"
]
}
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},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
......@@ -88,7 +87,7 @@
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 6,
"metadata": {},
"outputs": [
{
......@@ -113,12 +112,12 @@
"metadata": {},
"source": [
"## 去除标点符号\n",
"用`string`库中的punctuation，完成英文符号的替换。"
"因为标点符号本身无实际意义，用`string`库中的punctuation，完成英文符号的替换。"
]
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 7,
"metadata": {},
"outputs": [
{
......@@ -137,7 +136,7 @@
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": 8,
"metadata": {},
"outputs": [
{
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},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": 9,
"metadata": {},
"outputs": [
{
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},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
......@@ -233,7 +232,7 @@
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
......@@ -243,7 +242,7 @@
"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.embedding = paddle.nn.Embedding(num_embeddings=vocab_size, embedding_dim=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",
......@@ -260,90 +259,90 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### 定义`train()`函数，对模型进行训练。"
"## 定义`train()`函数，对模型进行训练。"
]
},
{
"cell_type": "code",
"execution_count": 28,
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch: 0, batch_id: 0, loss is: [10.252256]\n",
"epoch: 0, batch_id: 100, loss is: [7.0485706]\n",
"epoch: 0, batch_id: 200, loss is: [7.282592]\n",
"epoch: 0, batch_id: 300, loss is: [6.9604626]\n",
"epoch: 0, batch_id: 400, loss is: [6.7308316]\n",
"epoch: 0, batch_id: 500, loss is: [6.7940483]\n",
"epoch: 0, batch_id: 600, loss is: [6.6574802]\n",
"epoch: 0, batch_id: 700, loss is: [6.862562]\n",
"epoch: 0, batch_id: 800, loss is: [7.2091002]\n",
"epoch: 0, batch_id: 900, loss is: [7.0172606]\n",
"epoch: 0, batch_id: 1000, loss is: [6.9888105]\n",
"epoch: 0, batch_id: 1100, loss is: [6.9609995]\n",
"epoch: 0, batch_id: 1200, loss is: [6.550024]\n",
"epoch: 0, batch_id: 1300, loss is: [6.714109]\n",
"epoch: 0, batch_id: 1400, loss is: [6.995716]\n",
"epoch: 0, batch_id: 1500, loss is: [6.939434]\n",
"epoch: 0, batch_id: 1600, loss is: [6.5966253]\n",
"epoch: 0, batch_id: 1700, loss is: [6.9880104]\n",
"epoch: 0, batch_id: 1800, loss is: [6.6459093]\n",
"epoch: 0, batch_id: 1900, loss is: [6.8095036]\n",
"epoch: 0, batch_id: 2000, loss is: [6.8447037]\n",
"epoch: 0, batch_id: 2100, loss is: [6.8313]\n",
"epoch: 0, batch_id: 2200, loss is: [6.808483]\n",
"epoch: 0, batch_id: 2300, loss is: [6.502908]\n",
"epoch: 0, batch_id: 2400, loss is: [6.561283]\n",
"epoch: 0, batch_id: 2500, loss is: [7.0093765]\n",
"epoch: 0, batch_id: 2600, loss is: [6.512396]\n",
"epoch: 0, batch_id: 2700, loss is: [6.809763]\n",
"epoch: 0, batch_id: 2800, loss is: [6.806659]\n",
"epoch: 0, batch_id: 2900, loss is: [6.95402]\n",
"epoch: 0, batch_id: 3000, loss is: [6.634927]\n",
"epoch: 0, batch_id: 3100, loss is: [6.644098]\n",
"epoch: 0, batch_id: 3200, loss is: [6.705504]\n",
"epoch: 0, batch_id: 3300, loss is: [6.2121572]\n",
"epoch: 0, batch_id: 3400, loss is: [6.638401]\n",
"epoch: 0, batch_id: 3500, loss is: [6.986831]\n",
"epoch: 1, batch_id: 0, loss is: [6.795429]\n",
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"epoch: 1, batch_id: 2400, loss is: [6.258934]\n",
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"epoch: 0, batch_id: 800, loss is: [7.1285286]\n",
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"epoch: 1, batch_id: 400, loss is: [6.553849]\n",
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"epoch: 1, batch_id: 1000, loss is: [6.4078493]\n",
"epoch: 1, batch_id: 1100, loss is: [6.5992503]\n",
"epoch: 1, batch_id: 1200, loss is: [6.2867823]\n",
"epoch: 1, batch_id: 1300, loss is: [6.1241736]\n",
"epoch: 1, batch_id: 1400, loss is: [6.903452]\n",
"epoch: 1, batch_id: 1500, loss is: [6.8167877]\n",
"epoch: 1, batch_id: 1600, loss is: [6.785468]\n",
"epoch: 1, batch_id: 1700, loss is: [6.72624]\n",
"epoch: 1, batch_id: 1800, loss is: [6.668326]\n",
"epoch: 1, batch_id: 1900, loss is: [6.592691]\n",
"epoch: 1, batch_id: 2000, loss is: [6.542628]\n",
"epoch: 1, batch_id: 2100, loss is: [6.616316]\n",
"epoch: 1, batch_id: 2200, loss is: [6.786495]\n",
"epoch: 1, batch_id: 2300, loss is: [6.4466743]\n",
"epoch: 1, batch_id: 2400, loss is: [6.931132]\n",
"epoch: 1, batch_id: 2500, loss is: [6.3207083]\n",
"epoch: 1, batch_id: 2600, loss is: [6.697523]\n",
"epoch: 1, batch_id: 2700, loss is: [6.8533525]\n",
"epoch: 1, batch_id: 2800, loss is: [6.375583]\n",
"epoch: 1, batch_id: 2900, loss is: [6.7229414]\n",
"epoch: 1, batch_id: 3000, loss is: [6.7564845]\n",
"epoch: 1, batch_id: 3100, loss is: [6.9129057]\n",
"epoch: 1, batch_id: 3200, loss is: [6.732751]\n",
"epoch: 1, batch_id: 3300, loss is: [6.6692004]\n",
"epoch: 1, batch_id: 3400, loss is: [6.4342775]\n",
"epoch: 1, batch_id: 3500, loss is: [6.594665]\n"
]
}
],
......@@ -383,22 +382,22 @@
},
{
"cell_type": "code",
"execution_count": 29,
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x166d69048>]"
"[<matplotlib.lines.Line2D at 0x14eba4550>]"
]
},
"execution_count": 29,
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -428,24 +427,16 @@
},
{
"cell_type": "code",
"execution_count": 30,
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the input words is: of, william\n",
"the predict words is: shakespeare\n",
"the true words is: shakespeare\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Library/Python/3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
" and should_run_async(code)\n"
"the input words is: complete, works\n",
"the predict words is: of\n",
"the true words is: of\n"
]
}
],
......@@ -466,13 +457,6 @@
" print('the true words is: ' + y_data)\n",
"test(model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
......@@ -480,6 +464,18 @@
"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,
......
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