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chapter07_deep-dive-keras.ipynb

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"colab_type": "text"
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"source": [
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"This is a companion notebook for the book [Deep Learning with Python, Third Edition](https://www.manning.com/books/deep-learning-with-python-third-edition). 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\nThe book's contents are available online at [deeplearningwithpython.io](https://deeplearningwithpython.io).\n"
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"This is a companion notebook for the book [Deep Learning with Python, Third Edition](https://www.manning.com/books/deep-learning-with-python-third-edition). 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\nThe book's contents are available online at [deeplearningwithpython.io](https://deeplearningwithpython.io)."
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{

chapter11_image-segmentation.ipynb

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"model = get_model(img_size=img_size, num_classes=3)"
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"# \u26a0\ufe0fNOTE\u26a0\ufe0f: The following IoU metric is *very* slow on the PyTorch backend!\n",
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"# If you are running with PyTorch, we recommend re-running the notebook with Jax\n",
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"# or TensorFlow, or skipping to the next section of this chapter."
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]
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},
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"cell_type": "code",
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"execution_count": 0,

chapter14_text-classification.ipynb

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"model.summary(line_length=80)"
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]
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"# \u26a0\ufe0fNOTE\u26a0\ufe0f: The following fit call will error on a T4 GPU on the TensorFlow\n",
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"# backend due to a bug in TensorFlow. If you the follow cell errors out,\n",
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"# do one of the following:\n",
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"# - Skip the following two cells.\n",
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"# - Switch to the Jax or Torch backend and re-run this notebook.\n",
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"# - Change the GPU type in your runtime (requires Colab Pro as of this writing)."
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]
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},
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"cell_type": "code",
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"execution_count": 0,

chapter16_text-generation.ipynb

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"colab_type": "text"
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},
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"source": [
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"This is a companion notebook for the book [Deep Learning with Python, Third Edition](https://www.manning.com/books/deep-learning-with-python-third-edition). 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\nThe book's contents are available online at [deeplearningwithpython.io](https://deeplearningwithpython.io)."
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"This is a companion notebook for the book [Deep Learning with Python, Third Edition](https://www.manning.com/books/deep-learning-with-python-third-edition). 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\nThe book's contents are available online at [deeplearningwithpython.io](https://deeplearningwithpython.io).\n"
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{
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},
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"outputs": [],
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"source": [
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"# NOTE: If you are running on the free tier Colab GPUs, you will need to restart\n",
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"# your runtime and run the notebook from here to free up memory for this\n",
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"# 4 billion parameter model.\n",
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"# \u26a0\ufe0fNOTE\u26a0\ufe0f: If you are running on the free tier Colab GPUs, you will need to\n",
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"# restart your runtime and run the notebook from here to free up memory for\n",
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"# this 4 billion parameter model.\n",
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"import os\n",
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"\n",
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"os.environ[\"KERAS_BACKEND\"] = \"jax\"\n",

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