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Data preprocessing added on chapter11_part02_sequence-models #249

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Data preprocessing added on chapter11_part02_sequence-models #249

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12 changes: 8 additions & 4 deletions chapter11_part01_introduction.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,11 @@
"colab_type": "text"
},
"source": [
"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\n\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\n\nThis notebook was generated for TensorFlow 2.6."
"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\n",
"\n",
"**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\n",
"\n",
"This notebook was generated for TensorFlow 2.6."
]
},
{
Expand Down Expand Up @@ -746,9 +750,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
"nbformat_minor": 1
}
63 changes: 55 additions & 8 deletions chapter11_part02_sequence-models.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,11 @@
"colab_type": "text"
},
"source": [
"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\n\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\n\nThis notebook was generated for TensorFlow 2.6."
"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\n",
"\n",
"**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\n",
"\n",
"This notebook was generated for TensorFlow 2.6."
]
},
{
Expand Down Expand Up @@ -90,8 +94,51 @@
")\n",
"test_ds = keras.utils.text_dataset_from_directory(\n",
" \"aclImdb/test\", batch_size=batch_size\n",
")\n",
"text_only_train_ds = train_ds.map(lambda x, y: x)"
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Preprocessing Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def clean_text(text):\n",
" # Convert to lowercase\n",
" text = tf.strings.lower(text)\n",
" \n",
" # Remove non-UTF-8 characters (ignore decoding errors)\n",
" text = tf.strings.regex_replace(text, r'[^\\x00-\\x7F]+', '') \n",
" \n",
" # Keep only letters, numbers, spaces, and some punctuation\n",
" text = tf.strings.regex_replace(text, r\"[^a-zA-Z0-9\\s.,!?']\", \"\")\n",
" \n",
" return text\n",
"\n",
"def preprocess_text(text, label):\n",
" \n",
" text = clean_text(text) # Apply cleaning function\n",
" \n",
" return text, label"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train = train_ds.map(preprocess_text)\n",
"val = val_ds.map(preprocess_text)\n",
"test = test_ds.map(preprocess_text)\n",
"text_only_train_ds = train.map(lambda x, y: x)"
]
},
{
Expand Down Expand Up @@ -122,13 +169,13 @@
")\n",
"text_vectorization.adapt(text_only_train_ds)\n",
"\n",
"int_train_ds = train_ds.map(\n",
"int_train_ds = train.map(\n",
" lambda x, y: (text_vectorization(x), y),\n",
" num_parallel_calls=4)\n",
"int_val_ds = val_ds.map(\n",
"int_val_ds = val.map(\n",
" lambda x, y: (text_vectorization(x), y),\n",
" num_parallel_calls=4)\n",
"int_test_ds = test_ds.map(\n",
"int_test_ds = test.map(\n",
" lambda x, y: (text_vectorization(x), y),\n",
" num_parallel_calls=4)"
]
Expand Down Expand Up @@ -470,9 +517,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 0
"nbformat_minor": 1
}