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Sylvain Gugger 2020-05-20 08:19:05 -07:00
parent cd1aa1f758
commit 3acc79d59a
5 changed files with 16 additions and 16 deletions
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12
12_nlp_dive.ipynb
View File

@ -975,7 +975,7 @@
],
"source": [
"learn = Learner(dls, LMModel3(len(vocab), 64), loss_func=F.cross_entropy,\n",
" metrics=accuracy, cbs=ModelReseter)\n",
" metrics=accuracy, cbs=ModelResetter)\n",
"learn.fit_one_cycle(10, 3e-3)"
]
},
@ -1250,7 +1250,7 @@
],
"source": [
"learn = Learner(dls, LMModel4(len(vocab), 64), loss_func=loss_func,\n",
" metrics=accuracy, cbs=ModelReseter)\n",
" metrics=accuracy, cbs=ModelResetter)\n",
"learn.fit_one_cycle(15, 3e-3)"
]
},
@ -1478,7 +1478,7 @@
"source": [
"learn = Learner(dls, LMModel5(len(vocab), 64, 2), \n",
" loss_func=CrossEntropyLossFlat(), \n",
" metrics=accuracy, cbs=ModelReseter)\n",
" metrics=accuracy, cbs=ModelResetter)\n",
"learn.fit_one_cycle(15, 3e-3)"
]
},
@ -1871,7 +1871,7 @@
"source": [
"learn = Learner(dls, LMModel6(len(vocab), 64, 2), \n",
" loss_func=CrossEntropyLossFlat(), \n",
" metrics=accuracy, cbs=ModelReseter)\n",
" metrics=accuracy, cbs=ModelResetter)\n",
"learn.fit_one_cycle(15, 1e-2)"
]
},
@ -2065,7 +2065,7 @@
"source": [
"learn = Learner(dls, LMModel7(len(vocab), 64, 2, 0.5),\n",
" loss_func=CrossEntropyLossFlat(), metrics=accuracy,\n",
" cbs=[ModelReseter, RNNRegularizer(alpha=2, beta=1)])"
" cbs=[ModelResetter, RNNRegularizer(alpha=2, beta=1)])"
]
},
{
@ -2285,7 +2285,7 @@
"1. Why can maintaining the hidden state in an RNN lead to memory and performance problems? How do we fix this problem?\n",
"1. What is \"BPTT\"?\n",
"1. Write code to print out the first few batches of the validation set, including converting the token IDs back into English strings, as we showed for batches of IMDb data in <<chapter_nlp>>.\n",
"1. What does the `ModelReseter` callback do? Why do we need it?\n",
"1. What does the `ModelResetter` callback do? Why do we need it?\n",
"1. What are the downsides of predicting just one output word for each three input words?\n",
"1. Why do we need a custom loss function for `LMModel4`?\n",
"1. Why is the training of `LMModel4` unstable?\n",

4
16_accel_sgd.ipynb
View File

@ -1037,7 +1037,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's take a look at an example. Do you recall how in <<chapter_nlp_dive>> we needed to ensure that our special `reset` method was called at the start of training and validation for each epoch? We used the `ModelReseter` callback provided by fastai to do this for us. But how does it owrk? Here's the full source code for that class:"
"Let's take a look at an example. Do you recall how in <<chapter_nlp_dive>> we needed to ensure that our special `reset` method was called at the start of training and validation for each epoch? We used the `ModelResetter` callback provided by fastai to do this for us. But how does it owrk? Here's the full source code for that class:"
]
},
{
@ -1046,7 +1046,7 @@
"metadata": {},
"outputs": [],
"source": [
"class ModelReseter(Callback):\n",
"class ModelResetter(Callback):\n",
" def begin_train(self): self.model.reset()\n",
" def begin_validate(self): self.model.reset()"
]

2
app_blog.ipynb
View File

@ -49,7 +49,7 @@
"source": [
"A great solution is to host your blog on a platform called [GitHub Pages](https://pages.github.com/), which is free, has no ads or pay wall, and makes your data available in a standard way such that you can at any time move your blog to another host. But all the approaches I’ve seen to using GitHub Pages have required knowledge of the command line and arcane tools that only software developers are likely to be familiar with. For instance, GitHub's [own documentation](https://help.github.com/en/github/working-with-github-pages/creating-a-github-pages-site-with-jekyll) on setting up a blog includes a long list of instructions that involve installing the Ruby programming language, using the `git` command-line tool, copying over version numbers, and more—17 steps in total!\n",
"\n",
"To cut down the hassle, weve created an easy approach that allows you to use an *entirely browser-based interface* for all your blogging needs. You will be up and running with your new blog within about five minutes. It doesn’t cost anything, and you can easily add your own custom domain to it if you wish to. In this section, we'll explain how to do it, using a template we've created called *fast\\_template*. (NB: be sure to check the [book's website](https://book.fast.ai) for the latest blog recommendations, since new tools are always coming out; for instance, we're currently working with GitHub on creating a new tool called `fastpages` that is a more advanced version of `fast_template` particularly designed for people using Jupyter notebooks)."
"To cut down the hassle, weve created an easy approach that allows you to use an *entirely browser-based interface* for all your blogging needs. You will be up and running with your new blog within about five minutes. It doesn’t cost anything, and you can easily add your own custom domain to it if you wish to. In this section, we'll explain how to do it, using a template we've created called *fast\\_template*. (NB: be sure to check the [book's website](https://book.fast.ai) for the latest blog recommendations, since new tools are always coming out)."
]
},
{

12
clean/12_nlp_dive.ipynb
View File

@ -626,7 +626,7 @@
],
"source": [
"learn = Learner(dls, LMModel3(len(vocab), 64), loss_func=F.cross_entropy,\n",
" metrics=accuracy, cbs=ModelReseter)\n",
" metrics=accuracy, cbs=ModelResetter)\n",
"learn.fit_one_cycle(10, 3e-3)"
]
},
@ -845,7 +845,7 @@
],
"source": [
"learn = Learner(dls, LMModel4(len(vocab), 64), loss_func=loss_func,\n",
" metrics=accuracy, cbs=ModelReseter)\n",
" metrics=accuracy, cbs=ModelResetter)\n",
"learn.fit_one_cycle(15, 3e-3)"
]
},
@ -1022,7 +1022,7 @@
"source": [
"learn = Learner(dls, LMModel5(len(vocab), 64, 2), \n",
" loss_func=CrossEntropyLossFlat(), \n",
" metrics=accuracy, cbs=ModelReseter)\n",
" metrics=accuracy, cbs=ModelResetter)\n",
"learn.fit_one_cycle(15, 3e-3)"
]
},
@ -1303,7 +1303,7 @@
"source": [
"learn = Learner(dls, LMModel6(len(vocab), 64, 2), \n",
" loss_func=CrossEntropyLossFlat(), \n",
" metrics=accuracy, cbs=ModelReseter)\n",
" metrics=accuracy, cbs=ModelResetter)\n",
"learn.fit_one_cycle(15, 1e-2)"
]
},
@ -1382,7 +1382,7 @@
"source": [
"learn = Learner(dls, LMModel7(len(vocab), 64, 2, 0.5),\n",
" loss_func=CrossEntropyLossFlat(), metrics=accuracy,\n",
" cbs=[ModelReseter, RNNRegularizer(alpha=2, beta=1)])"
" cbs=[ModelResetter, RNNRegularizer(alpha=2, beta=1)])"
]
},
{
@ -1565,7 +1565,7 @@
"1. Why can maintaining the hidden state in an RNN lead to memory and performance problems? How do we fix this problem?\n",
"1. What is \"BPTT\"?\n",
"1. Write code to print out the first few batches of the validation set, including converting the token IDs back into English strings, as we showed for batches of IMDb data in <<chapter_nlp>>.\n",
"1. What does the `ModelReseter` callback do? Why do we need it?\n",
"1. What does the `ModelResetter` callback do? Why do we need it?\n",
"1. What are the downsides of predicting just one output word for each three input words?\n",
"1. Why do we need a custom loss function for `LMModel4`?\n",
"1. Why is the training of `LMModel4` unstable?\n",

2
clean/16_accel_sgd.ipynb
View File

@ -614,7 +614,7 @@
"metadata": {},
"outputs": [],
"source": [
"class ModelReseter(Callback):\n",
"class ModelResetter(Callback):\n",
" def begin_train(self): self.model.reset()\n",
" def begin_validate(self): self.model.reset()"
]