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Merge pull request #286 from ibeauregard/changes-chap13 

A few changes in Chapter 13 notebook
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Aurélien Geron 2021-03-02 12:14:48 +13:00 committed by GitHub
parent da7b8d3a82 08e3870053
commit d46938857c
1 changed files with 5 additions and 5 deletions
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10
13_loading_and_preprocessing_data.ipynb
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@ -2040,8 +2040,8 @@
"outputs": [],
"source": [
"train_set = mnist_dataset(train_filepaths, shuffle_buffer_size=60000)\n",
"valid_set = mnist_dataset(train_filepaths)\n",
"test_set = mnist_dataset(train_filepaths)"
"valid_set = mnist_dataset(valid_filepaths)\n",
"test_set = mnist_dataset(test_filepaths)"
]
},
{
@ -2274,7 +2274,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"But let's pretend the dataset does not fit in memory, just to make things more interesting. Luckily, each review fits on just one line (they use `<br />` to indicate line breaks), so we can read the reviews using a `TextLineDataset`. If they didn't we would have to preprocess the input files (e.g., converting them to TFRecords). For very large datasets, it would make sense a tool like Apache Beam for that."
"But let's pretend the dataset does not fit in memory, just to make things more interesting. Luckily, each review fits on just one line (they use `<br />` to indicate line breaks), so we can read the reviews using a `TextLineDataset`. If they didn't we would have to preprocess the input files (e.g., converting them to TFRecords). For very large datasets, it would make sense to use a tool like Apache Beam for that."
]
},
{
@ -2473,7 +2473,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's run it on the same `X_example`, just to make sure the word IDs are larger now, since the vocabulary bigger:"
"Let's run it on the same `X_example`, just to make sure the word IDs are larger now, since the vocabulary is bigger:"
]
},
{
@ -2540,7 +2540,7 @@
"source": [
"class BagOfWords(keras.layers.Layer):\n",
" def __init__(self, n_tokens, dtype=tf.int32, **kwargs):\n",
" super().__init__(dtype=tf.int32, **kwargs)\n",
" super().__init__(dtype=dtype, **kwargs)\n",
" self.n_tokens = n_tokens\n",
" def call(self, inputs):\n",
" one_hot = tf.one_hot(inputs, self.n_tokens)\n",