{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Preliminary setup\n", "\n", "Before we delve into coding, you need to prepare your own pc, or your account in an HPC to be able to run codes.\n", "\n", "The below instructions are some highlights. I assume you already know how to install the rest (for example the jupyter notebook), or how to run a python script without a jupyter notebook. I also assume you are familiar with terminal and linux." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Anaconda/Miniconda\n", "\n", "This recipe is intended for Linux, specifically Ubuntu 16.04 or higher (64-bit). If you are using windows, please consider installing WSL2. There is no official GPU support for MacOs, so please avoid.\n", "\n", "1. Install Miniconda\n", "\n", "Miniconda is the recommended approach for installing TensorFlow with GPU support. It creates a separate environment to avoid changing any installed software in your system. This is also the easiest way to install the required software especially for the GPU setup.\n", "\n", "You can use the following command to install Miniconda. During installation, you may need to press enter and type \"yes\".\n", "\n", "curl https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -o Miniconda3-latest-Linux-x86_64.sh\n", "bash Miniconda3-latest-Linux-x86_64.sh\n", "\n", "You may need to restart your terminal or source ~/.bashrc to enable the conda command. Use conda -V to test if it is installed successfully.\n", "2. Create a conda environment\n", "\n", "Create a new conda environment named qml with the following command.\n", "\n", "conda create --name qml python=3.8\n", "\n", "You can deactivate and activate it with the following commands.\n", "\n", "conda deactivate\n", "conda activate qml\n", "\n", "Make sure it is activated for the rest of the installation.\n", "3. GPU setup\n", "\n", "You can skip this section if you only run TensorFlow on the CPU.\n", "\n", "First install the NVIDIA GPU driver if you have not. You can use the following command to verify it is installed.\n", "\n", "```bash\n", "nvidia-smi\n", "```\n", "\n", "Then install CUDA and cuDNN with conda.\n", "\n", "```bash\n", "conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0\n", "```\n", "\n", "Configure the system paths. You can do it with following command everytime your start a new terminal after activating your conda environment.\n", "\n", "```bash\n", "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/\n", "```\n", "\n", "For your convenience it is recommended that you automate it with the following commands. The system paths will be automatically configured when you activate this conda environment.\n", "\n", "```bash\n", "mkdir -p $CONDA_PREFIX/etc/conda/activate.d\n", "echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/' > $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh\n", "```\n", "\n", "4. Install TensorFlow\n", "\n", "TensorFlow requires a recent version of pip, so upgrade your pip installation to be sure you're running the latest version.\n", "\n", "```bash\n", "pip install --upgrade pip\n", "```\n", "\n", "Then, install TensorFlow with pip.\n", "Note: Do not install TensorFlow with conda. It may not have the latest stable version. pip is recommended since TensorFlow is only officially released to PyPI.\n", "```bash\n", "pip install tensorflow\n", "```\n", "\n", "6. Verify install\n", "\n", "Verify the CPU setup:\n", "\n", "```bash\n", "python3 -c \"import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))\"\n", "```\n", "\n", "If a tensor is returned, you've installed TensorFlow successfully.\n", "\n", "Verify the GPU setup:\n", "\n", "```bash\n", "python3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"\n", "```\n", "\n", "If a list of GPU devices is returned, you've installed TensorFlow successfully.\n", "\n", "We will install other packages as we progress\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Aptainer (formerly Singularity)\n", "\n", "Apptainer/Singularity is the most widely used container system for HPC. It is designed to execute applications at bare-metal performance while being secure, portable, and 100% reproducible. Apptainer is an open-source project with a friendly community of developers and users. The user base continues to expand, with Apptainer/Singularity now used across industry and academia in many areas.\n", "\n", " Apptainer/Singularity is already installed in carbon.physics.metu.edu.tr, so you can start using it immediately. If you want to install it to your own pc, the instructions are at https://docs.sylabs.io/guides/3.0/user-guide/quick_start.html\n", "\n", "NVIDIA kindly provides, optimized containers for numerous academic software. Please check out [NGC Catalog](https://catalog.ngc.nvidia.com/)\n", "\n", "In carbon, you can find the TensorFlow Containers at `/share/apps/singularity-containers/`\n", "\n", "If you want to \"pull\" i.e. download and compile a container from NGC, try something like `singularity pull tensorflow-22.09-tf1-py3.sif docker://nvcr.io/nvidia/tensorflow:22.09-tf1-py`. This will download (a lot) and compile (a lot) to produce a sif image for you. Then you can run this image with `singularity run --nv '-B:/host_pwd' --pwd /host_pwd tensorflow-22.09-tf1-py3.sif`\n", "\n", "Running a singularity container in an HPC environment is similar to running it in your own computer. An example script can be found [here](https://obm.physics.metu.edu.tr/node/111). \n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Basic regression: Predict fuel efficiency" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In a *regression* problem, the aim is to predict the output of a continuous value, like a price or a probability. Contrast this with a *classification* problem, where the aim is to select a class from a list of classes (for example, where a picture contains an apple or an orange, recognizing which fruit is in the picture).\n", "\n", "This tutorial uses the classic [Auto MPG](https://archive.ics.uci.edu/ml/datasets/auto+mpg) dataset and demonstrates how to build models to predict the fuel efficiency of the late-1970s and early 1980s automobiles. To do this, you will provide the models with a description of many automobiles from that time period. This description includes attributes like cylinders, displacement, horsepower, and weight.\n", "\n", "This example uses the Keras API. (Visit the Keras [tutorials](https://www.tensorflow.org/tutorials/keras) and [guides](https://www.tensorflow.org/guide/keras) to learn more.)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/bin/bash: /home/obm/Prog/miniconda3/envs/qml/lib/libtinfo.so.6: no version information available (required by /bin/bash)\r\n" ] } ], "source": [ "# Use seaborn for pairplot.\n", "!pip install -q seaborn" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "\n", "# Make NumPy printouts easier to read.\n", "np.set_printoptions(precision=3, suppress=True)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2022-10-20 12:19:54.715755: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", "2022-10-20 12:19:54.821454: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", "2022-10-20 12:19:55.254937: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/home/obm/Prog/miniconda3/envs/qml/lib/\n", "2022-10-20 12:19:55.254997: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/home/obm/Prog/miniconda3/envs/qml/lib/\n", "2022-10-20 12:19:55.255002: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2.10.0\n" ] } ], "source": [ "import tensorflow as tf\n", "\n", "from tensorflow import keras\n", "from tensorflow.keras import layers\n", "\n", "print(tf.__version__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Auto MPG dataset\n", "\n", "The dataset is available from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/).\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get the data\n", "First download and import the dataset using pandas:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data'\n", "column_names = ['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight',\n", " 'Acceleration', 'Model Year', 'Origin']\n", "\n", "raw_dataset = pd.read_csv(url, names=column_names,\n", " na_values='?', comment='\\t',\n", " sep=' ', skipinitialspace=True)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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MPGCylindersDisplacementHorsepowerWeightAccelerationModel YearOrigin
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" ], "text/plain": [ " MPG Cylinders Displacement Horsepower Weight Acceleration \\\n", "393 27.0 4 140.0 86.0 2790.0 15.6 \n", "394 44.0 4 97.0 52.0 2130.0 24.6 \n", "395 32.0 4 135.0 84.0 2295.0 11.6 \n", "396 28.0 4 120.0 79.0 2625.0 18.6 \n", "397 31.0 4 119.0 82.0 2720.0 19.4 \n", "\n", " Model Year Origin \n", "393 82 1 \n", "394 82 2 \n", "395 82 1 \n", "396 82 1 \n", "397 82 1 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset = raw_dataset.copy()\n", "dataset.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Clean the data\n", "\n", "The dataset contains a few unknown values:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "MPG 0\n", "Cylinders 0\n", "Displacement 0\n", "Horsepower 6\n", "Weight 0\n", "Acceleration 0\n", "Model Year 0\n", "Origin 0\n", "dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.isna().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Drop those rows to keep this initial tutorial simple:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "dataset = dataset.dropna()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `\"Origin\"` column is categorical, not numeric. So the next step is to one-hot encode the values in the column with [pd.get_dummies](https://pandas.pydata.org/docs/reference/api/pandas.get_dummies.html).\n", "\n", "Note: You can set up the `tf.keras.Model` to do this kind of transformation for you but that's beyond the scope of this tutorial. Check out the [Classify structured data using Keras preprocessing layers](../structured_data/preprocessing_layers.ipynb) or [Load CSV data](../load_data/csv.ipynb) tutorials for examples." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "dataset['Origin'] = dataset['Origin'].map({1: 'USA', 2: 'Europe', 3: 'Japan'})" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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MPGCylindersDisplacementHorsepowerWeightAccelerationModel YearEuropeJapanUSA
39327.04140.086.02790.015.682001
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39532.04135.084.02295.011.682001
39628.04120.079.02625.018.682001
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" ], "text/plain": [ " MPG Cylinders Displacement Horsepower Weight Acceleration \\\n", "393 27.0 4 140.0 86.0 2790.0 15.6 \n", "394 44.0 4 97.0 52.0 2130.0 24.6 \n", "395 32.0 4 135.0 84.0 2295.0 11.6 \n", "396 28.0 4 120.0 79.0 2625.0 18.6 \n", "397 31.0 4 119.0 82.0 2720.0 19.4 \n", "\n", " Model Year Europe Japan USA \n", "393 82 0 0 1 \n", "394 82 1 0 0 \n", "395 82 0 0 1 \n", "396 82 0 0 1 \n", "397 82 0 0 1 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset = pd.get_dummies(dataset, columns=['Origin'], prefix='', prefix_sep='')\n", "dataset.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Split the data into training and test sets\n", "\n", "Now, split the dataset into a training set and a test set. You will use the test set in the final evaluation of your models." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "train_dataset = dataset.sample(frac=0.8, random_state=0)\n", "test_dataset = dataset.drop(train_dataset.index)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Inspect the data\n", "\n", "Review the joint distribution of a few pairs of columns from the training set.\n", "\n", "The top row suggests that the fuel efficiency (MPG) is a function of all the other parameters. The other rows indicate they are functions of each other." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.pairplot(train_dataset[['MPG', 'Cylinders', 'Displacement', 'Weight']], diag_kind='kde')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's also check the overall statistics. Note how each feature covers a very different range:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countmeanstdmin25%50%75%max
MPG314.023.3105107.72865210.017.0022.028.9546.6
Cylinders314.05.4777071.6997883.04.004.08.008.0
Displacement314.0195.318471104.33158968.0105.50151.0265.75455.0
Horsepower314.0104.86942738.09621446.076.2594.5128.00225.0
Weight314.02990.251592843.8985961649.02256.502822.53608.005140.0
Acceleration314.015.5592362.7892308.013.8015.517.2024.8
Model Year314.075.8980893.67564270.073.0076.079.0082.0
Europe314.00.1783440.3834130.00.000.00.001.0
Japan314.00.1974520.3987120.00.000.00.001.0
USA314.00.6242040.4851010.00.001.01.001.0
\n", "
" ], "text/plain": [ " count mean std min 25% 50% \\\n", "MPG 314.0 23.310510 7.728652 10.0 17.00 22.0 \n", "Cylinders 314.0 5.477707 1.699788 3.0 4.00 4.0 \n", "Displacement 314.0 195.318471 104.331589 68.0 105.50 151.0 \n", "Horsepower 314.0 104.869427 38.096214 46.0 76.25 94.5 \n", "Weight 314.0 2990.251592 843.898596 1649.0 2256.50 2822.5 \n", "Acceleration 314.0 15.559236 2.789230 8.0 13.80 15.5 \n", "Model Year 314.0 75.898089 3.675642 70.0 73.00 76.0 \n", "Europe 314.0 0.178344 0.383413 0.0 0.00 0.0 \n", "Japan 314.0 0.197452 0.398712 0.0 0.00 0.0 \n", "USA 314.0 0.624204 0.485101 0.0 0.00 1.0 \n", "\n", " 75% max \n", "MPG 28.95 46.6 \n", "Cylinders 8.00 8.0 \n", "Displacement 265.75 455.0 \n", "Horsepower 128.00 225.0 \n", "Weight 3608.00 5140.0 \n", "Acceleration 17.20 24.8 \n", "Model Year 79.00 82.0 \n", "Europe 0.00 1.0 \n", "Japan 0.00 1.0 \n", "USA 1.00 1.0 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_dataset.describe().transpose()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Split features from labels\n", "\n", "Separate the target value—the \"label\"—from the features. This label is the value that you will train the model to predict." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "train_features = train_dataset.copy()\n", "test_features = test_dataset.copy()\n", "\n", "train_labels = train_features.pop('MPG')\n", "test_labels = test_features.pop('MPG')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Normalization\n", "\n", "In the table of statistics it's easy to see how different the ranges of each feature are:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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meanstd
MPG23.3105107.728652
Cylinders5.4777071.699788
Displacement195.318471104.331589
Horsepower104.86942738.096214
Weight2990.251592843.898596
Acceleration15.5592362.789230
Model Year75.8980893.675642
Europe0.1783440.383413
Japan0.1974520.398712
USA0.6242040.485101
\n", "
" ], "text/plain": [ " mean std\n", "MPG 23.310510 7.728652\n", "Cylinders 5.477707 1.699788\n", "Displacement 195.318471 104.331589\n", "Horsepower 104.869427 38.096214\n", "Weight 2990.251592 843.898596\n", "Acceleration 15.559236 2.789230\n", "Model Year 75.898089 3.675642\n", "Europe 0.178344 0.383413\n", "Japan 0.197452 0.398712\n", "USA 0.624204 0.485101" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_dataset.describe().transpose()[['mean', 'std']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is good practice to normalize features that use different scales and ranges.\n", "\n", "One reason this is important is because the features are multiplied by the model weights. So, the scale of the outputs and the scale of the gradients are affected by the scale of the inputs.\n", "\n", "Although a model *might* converge without feature normalization, normalization makes training much more stable.\n", "\n", "Note: There is no advantage to normalizing the one-hot features—it is done here for simplicity. For more details on how to use the preprocessing layers, refer to the [Working with preprocessing layers](https://www.tensorflow.org/guide/keras/preprocessing_layers) guide and the [Classify structured data using Keras preprocessing layers](../structured_data/preprocessing_layers.ipynb) tutorial." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The Normalization layer\n", "\n", "The `tf.keras.layers.Normalization` is a clean and simple way to add feature normalization into your model.\n", "\n", "The first step is to create the layer:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "normalizer = tf.keras.layers.Normalization(axis=-1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, fit the state of the preprocessing layer to the data by calling `Normalization.adapt`:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2022-10-20 12:19:58.186710: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-10-20 12:19:58.212756: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-10-20 12:19:58.212970: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-10-20 12:19:58.213481: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", "2022-10-20 12:19:58.215680: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-10-20 12:19:58.215847: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-10-20 12:19:58.215993: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-10-20 12:19:58.574165: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-10-20 12:19:58.574366: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-10-20 12:19:58.574520: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-10-20 12:19:58.574652: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5481 MB memory: -> device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:21:00.0, compute capability: 7.5\n" ] } ], "source": [ "normalizer.adapt(np.array(train_features))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate the mean and variance, and store them in the layer:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 5.478 195.318 104.869 2990.252 15.559 75.898 0.178 0.197\n", " 0.624]]\n" ] } ], "source": [ "print(normalizer.mean.numpy())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When the layer is called, it returns the input data, with each feature independently normalized:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First example: [[ 4. 90. 75. 2125. 14.5 74. 0. 0. 1. ]]\n", "\n", "Normalized: [[-0.87 -1.01 -0.79 -1.03 -0.38 -0.52 -0.47 -0.5 0.78]]\n" ] } ], "source": [ "first = np.array(train_features[:1])\n", "\n", "with np.printoptions(precision=2, suppress=True):\n", " print('First example:', first)\n", " print()\n", " print('Normalized:', normalizer(first).numpy())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Linear regression\n", "\n", "Before building a deep neural network model, start with linear regression using one and several variables." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Linear regression with one variable\n", "\n", "Begin with a single-variable linear regression to predict `'MPG'` from `'Horsepower'`.\n", "\n", "Training a model with `tf.keras` typically starts by defining the model architecture. Use a `tf.keras.Sequential` model, which [represents a sequence of steps](https://www.tensorflow.org/guide/keras/sequential_model).\n", "\n", "There are two steps in your single-variable linear regression model:\n", "\n", "- Normalize the `'Horsepower'` input features using the `tf.keras.layers.Normalization` preprocessing layer.\n", "- Apply a linear transformation ($y = mx+b$) to produce 1 output using a linear layer (`tf.keras.layers.Dense`).\n", "\n", "The number of _inputs_ can either be set by the `input_shape` argument, or automatically when the model is run for the first time." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, create a NumPy array made of the `'Horsepower'` features. Then, instantiate the `tf.keras.layers.Normalization` and fit its state to the `horsepower` data:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "horsepower = np.array(train_features['Horsepower'])\n", "\n", "horsepower_normalizer = layers.Normalization(input_shape=[1,], axis=None)\n", "horsepower_normalizer.adapt(horsepower)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Build the Keras Sequential model:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " normalization_1 (Normalizat (None, 1) 3 \n", " ion) \n", " \n", " dense (Dense) (None, 1) 2 \n", " \n", "=================================================================\n", "Total params: 5\n", "Trainable params: 2\n", "Non-trainable params: 3\n", "_________________________________________________________________\n" ] } ], "source": [ "horsepower_model = tf.keras.Sequential([\n", " horsepower_normalizer,\n", " layers.Dense(units=1)\n", "])\n", "\n", "horsepower_model.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This model will predict `'MPG'` from `'Horsepower'`.\n", "\n", "Run the untrained model on the first 10 'Horsepower' values. The output won't be good, but notice that it has the expected shape of `(10, 1)`:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 425ms/step\n" ] }, { "data": { "text/plain": [ "array([[-1.255],\n", " [-0.709],\n", " [ 2.316],\n", " [-1.759],\n", " [-1.591],\n", " [-0.625],\n", " [-1.885],\n", " [-1.591],\n", " [-0.415],\n", " [-0.709]], dtype=float32)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "horsepower_model.predict(horsepower[:10])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once the model is built, configure the training procedure using the Keras `Model.compile` method. The most important arguments to compile are the `loss` and the `optimizer`, since these define what will be optimized (`mean_absolute_error`) and how (using the `tf.keras.optimizers.Adam`)." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "horsepower_model.compile(\n", " optimizer=tf.keras.optimizers.Adam(learning_rate=0.1),\n", " loss='mean_absolute_error')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use Keras `Model.fit` to execute the training for 100 epochs:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/obm/Prog/miniconda3/envs/qml/lib/python3.8/site-packages/keras/engine/data_adapter.py:1699: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n", " return t[start:end]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3.49 s, sys: 720 ms, total: 4.21 s\n", "Wall time: 2.94 s\n" ] } ], "source": [ "%%time\n", "history = horsepower_model.fit(\n", " train_features['Horsepower'],\n", " train_labels,\n", " epochs=100,\n", " # Suppress logging.\n", " verbose=0,\n", " # Calculate validation results on 20% of the training data.\n", " validation_split = 0.2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visualize the model's training progress using the stats stored in the `history` object:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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lossval_lossepoch
953.8021674.18435895
963.8032604.19147296
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" ], "text/plain": [ " loss val_loss epoch\n", "95 3.802167 4.184358 95\n", "96 3.803260 4.191472 96\n", "97 3.803856 4.188847 97\n", "98 3.802086 4.189030 98\n", "99 3.802913 4.190779 99" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hist = pd.DataFrame(history.history)\n", "hist['epoch'] = history.epoch\n", "hist.tail()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "def plot_loss(history):\n", " plt.plot(history.history['loss'], label='loss')\n", " plt.plot(history.history['val_loss'], label='val_loss')\n", " plt.ylim([0, 10])\n", " plt.xlabel('Epoch')\n", " plt.ylabel('Error [MPG]')\n", " plt.legend()\n", " plt.grid(True)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_loss(history)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Collect the results on the test set for later:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "test_results = {}\n", "\n", "test_results['horsepower_model'] = horsepower_model.evaluate(\n", " test_features['Horsepower'],\n", " test_labels, verbose=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since this is a single variable regression, it's easy to view the model's predictions as a function of the input:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8/8 [==============================] - 0s 741us/step\n" ] } ], "source": [ "x = tf.linspace(0.0, 250, 251)\n", "y = horsepower_model.predict(x)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "def plot_horsepower(x, y):\n", " plt.scatter(train_features['Horsepower'], train_labels, label='Data')\n", " plt.plot(x, y, color='k', label='Predictions')\n", " plt.xlabel('Horsepower')\n", " plt.ylabel('MPG')\n", " plt.legend()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_horsepower(x, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Linear regression with multiple inputs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can use an almost identical setup to make predictions based on multiple inputs. This model still does the same $y = mx+b$ except that $m$ is a matrix and $b$ is a vector.\n", "\n", "Create a two-step Keras Sequential model again with the first layer being `normalizer` (`tf.keras.layers.Normalization(axis=-1)`) you defined earlier and adapted to the whole dataset:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "linear_model = tf.keras.Sequential([\n", " normalizer,\n", " layers.Dense(units=1)\n", "])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When you call `Model.predict` on a batch of inputs, it produces `units=1` outputs for each example:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 32ms/step\n" ] }, { "data": { "text/plain": [ "array([[-1.299],\n", " [-0.978],\n", " [-0.513],\n", " [-0.287],\n", " [ 1.706],\n", " [-0.56 ],\n", " [ 1.639],\n", " [ 0.547],\n", " [-0.948],\n", " [ 0.671]], dtype=float32)" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "linear_model.predict(train_features[:10])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When you call the model, its weight matrices will be built—check that the `kernel` weights (the $m$ in $y=mx+b$) have a shape of `(9, 1)`:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "linear_model.layers[1].kernel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Configure the model with Keras `Model.compile` and train with `Model.fit` for 100 epochs:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "linear_model.compile(\n", " optimizer=tf.keras.optimizers.Adam(learning_rate=0.1),\n", " loss='mean_absolute_error')" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/obm/Prog/miniconda3/envs/qml/lib/python3.8/site-packages/keras/engine/data_adapter.py:1699: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n", " return t[start:end]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3.52 s, sys: 761 ms, total: 4.28 s\n", "Wall time: 2.92 s\n" ] } ], "source": [ "%%time\n", "history = linear_model.fit(\n", " train_features,\n", " train_labels,\n", " epochs=100,\n", " # Suppress logging.\n", " verbose=0,\n", " # Calculate validation results on 20% of the training data.\n", " validation_split = 0.2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using all the inputs in this regression model achieves a much lower training and validation error than the `horsepower_model`, which had one input:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_loss(history)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Collect the results on the test set for later:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "test_results['linear_model'] = linear_model.evaluate(\n", " test_features, test_labels, verbose=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Regression with a deep neural network (DNN)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the previous section, you implemented two linear models for single and multiple inputs.\n", "\n", "Here, you will implement single-input and multiple-input DNN models.\n", "\n", "The code is basically the same except the model is expanded to include some \"hidden\" non-linear layers. The name \"hidden\" here just means not directly connected to the inputs or outputs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These models will contain a few more layers than the linear model:\n", "\n", "* The normalization layer, as before (with `horsepower_normalizer` for a single-input model and `normalizer` for a multiple-input model).\n", "* Two hidden, non-linear, `Dense` layers with the ReLU (`relu`) activation function nonlinearity.\n", "* A linear `Dense` single-output layer.\n", "\n", "Both models will use the same training procedure, so the `compile` method is included in the `build_and_compile_model` function below." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "def build_and_compile_model(norm):\n", " model = keras.Sequential([\n", " norm,\n", " layers.Dense(64, activation='relu'),\n", " layers.Dense(64, activation='relu'),\n", " layers.Dense(1)\n", " ])\n", "\n", " model.compile(loss='mean_absolute_error',\n", " optimizer=tf.keras.optimizers.Adam(0.001))\n", " return model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Regression using a DNN and a single input" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a DNN model with only `'Horsepower'` as input and `horsepower_normalizer` (defined earlier) as the normalization layer:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "dnn_horsepower_model = build_and_compile_model(horsepower_normalizer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This model has quite a few more trainable parameters than the linear models:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_2\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " normalization_1 (Normalizat (None, 1) 3 \n", " ion) \n", " \n", " dense_2 (Dense) (None, 64) 128 \n", " \n", " dense_3 (Dense) (None, 64) 4160 \n", " \n", " dense_4 (Dense) (None, 1) 65 \n", " \n", "=================================================================\n", "Total params: 4,356\n", "Trainable params: 4,353\n", "Non-trainable params: 3\n", "_________________________________________________________________\n" ] } ], "source": [ "dnn_horsepower_model.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Train the model with Keras `Model.fit`:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/obm/Prog/miniconda3/envs/qml/lib/python3.8/site-packages/keras/engine/data_adapter.py:1699: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n", " return t[start:end]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3.37 s, sys: 846 ms, total: 4.22 s\n", "Wall time: 2.9 s\n" ] } ], "source": [ "%%time\n", "history = dnn_horsepower_model.fit(\n", " train_features['Horsepower'],\n", " train_labels,\n", " validation_split=0.2,\n", " verbose=0, epochs=100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This model does slightly better than the linear single-input `horsepower_model`:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_loss(history)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you plot the predictions as a function of `'Horsepower'`, you should notice how this model takes advantage of the nonlinearity provided by the hidden layers:" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8/8 [==============================] - 0s 712us/step\n" ] } ], "source": [ "x = tf.linspace(0.0, 250, 251)\n", "y = dnn_horsepower_model.predict(x)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_horsepower(x, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Collect the results on the test set for later:" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "test_results['dnn_horsepower_model'] = dnn_horsepower_model.evaluate(\n", " test_features['Horsepower'], test_labels,\n", " verbose=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Regression using a DNN and multiple inputs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Repeat the previous process using all the inputs. The model's performance slightly improves on the validation dataset." ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_3\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " normalization (Normalizatio (None, 9) 19 \n", " n) \n", " \n", " dense_5 (Dense) (None, 64) 640 \n", " \n", " dense_6 (Dense) (None, 64) 4160 \n", " \n", " dense_7 (Dense) (None, 1) 65 \n", " \n", "=================================================================\n", "Total params: 4,884\n", "Trainable params: 4,865\n", "Non-trainable params: 19\n", "_________________________________________________________________\n" ] } ], "source": [ "dnn_model = build_and_compile_model(normalizer)\n", "dnn_model.summary()" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3.73 s, sys: 776 ms, total: 4.5 s\n", "Wall time: 3.13 s\n" ] } ], "source": [ "%%time\n", "history = dnn_model.fit(\n", " train_features,\n", " train_labels,\n", " validation_split=0.2,\n", " verbose=0, epochs=100)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_loss(history)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Collect the results on the test set:" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "test_results['dnn_model'] = dnn_model.evaluate(test_features, test_labels, verbose=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Performance" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since all models have been trained, you can review their test set performance:" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Mean absolute error [MPG]
horsepower_model3.654480
linear_model2.520267
dnn_horsepower_model2.878578
dnn_model1.719923
\n", "
" ], "text/plain": [ " Mean absolute error [MPG]\n", "horsepower_model 3.654480\n", "linear_model 2.520267\n", "dnn_horsepower_model 2.878578\n", "dnn_model 1.719923" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(test_results, index=['Mean absolute error [MPG]']).T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These results match the validation error observed during training." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Make predictions\n", "\n", "You can now make predictions with the `dnn_model` on the test set using Keras `Model.predict` and review the loss:" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3/3 [==============================] - 0s 1ms/step\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "test_predictions = dnn_model.predict(test_features).flatten()\n", "\n", "a = plt.axes(aspect='equal')\n", "plt.scatter(test_labels, test_predictions)\n", "plt.xlabel('True Values [MPG]')\n", "plt.ylabel('Predictions [MPG]')\n", "lims = [0, 50]\n", "plt.xlim(lims)\n", "plt.ylim(lims)\n", "_ = plt.plot(lims, lims)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It appears that the model predicts reasonably well.\n", "\n", "Now, check the error distribution:" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "error = test_predictions - test_labels\n", "plt.hist(error, bins=25)\n", "plt.xlabel('Prediction Error [MPG]')\n", "_ = plt.ylabel('Count')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you're happy with the model, save it for later use with `Model.save`:" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: dnn_model/assets\n" ] } ], "source": [ "dnn_model.save('dnn_model')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you reload the model, it gives identical output:" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "reloaded = tf.keras.models.load_model('dnn_model')\n", "\n", "test_results['reloaded'] = reloaded.evaluate(\n", " test_features, test_labels, verbose=0)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Mean absolute error [MPG]
horsepower_model3.654480
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dnn_horsepower_model2.878578
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" ], "text/plain": [ " Mean absolute error [MPG]\n", "horsepower_model 3.654480\n", "linear_model 2.520267\n", "dnn_horsepower_model 2.878578\n", "dnn_model 1.719923\n", "reloaded 1.719923" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(test_results, index=['Mean absolute error [MPG]']).T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Takeaways:\n", "\n", "This part introduced a few techniques to handle a regression problem. Here are a few more tips that may help:\n", "\n", "- Mean squared error (MSE) (`tf.keras.losses.MeanSquaredError`) and mean absolute error (MAE) (`tf.keras.losses.MeanAbsoluteError`) are common loss functions used for regression problems. MAE is less sensitive to outliers. Different loss functions are used for classification problems.\n", "- Similarly, evaluation metrics used for regression differ from classification.\n", "- When numeric input data features have values with different ranges, each feature should be scaled independently to the same range.\n", "- Overfitting is a common problem for DNN models, though it wasn't a problem for this tutorial.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# DO IT YOURSELF 1:\n", "\n", "Please repeat the same steps as above, using [the superconductivity dataset](https://archive.ics.uci.edu/ml/datasets/Superconductivty+Data). Write it as a standalone python script and run it in carbon.physics.metu.edu.tr using singularity." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Overfit and underfit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As always, the code in this example will use the `tf.keras` API, which you can learn more about in the TensorFlow [Keras guide](https://www.tensorflow.org/guide/keras).\n", "\n", "In both of the previous examples—[classifying text](text_classification_with_hub.ipynb) and [predicting fuel efficiency](regression.ipynb)—the accuracy of models on the validation data would peak after training for a number of epochs and then stagnate or start decreasing.\n", "\n", "In other words, your model would *overfit* to the training data. Learning how to deal with overfitting is important. Although it's often possible to achieve high accuracy on the *training set*, what you really want is to develop models that generalize well to a *testing set* (or data they haven't seen before).\n", "\n", "The opposite of overfitting is *underfitting*. Underfitting occurs when there is still room for improvement on the train data. This can happen for a number of reasons: If the model is not powerful enough, is over-regularized, or has simply not been trained long enough. This means the network has not learned the relevant patterns in the training data.\n", "\n", "If you train for too long though, the model will start to overfit and learn patterns from the training data that don't generalize to the test data. You need to strike a balance. Understanding how to train for an appropriate number of epochs as you'll explore below is a useful skill.\n", "\n", "To prevent overfitting, the best solution is to use more complete training data. The dataset should cover the full range of inputs that the model is expected to handle. Additional data may only be useful if it covers new and interesting cases.\n", "\n", "A model trained on more complete data will naturally generalize better. When that is no longer possible, the next best solution is to use techniques like regularization. These place constraints on the quantity and type of information your model can store. If a network can only afford to memorize a small number of patterns, the optimization process will force it to focus on the most prominent patterns, which have a better chance of generalizing well.\n", "\n", "In this notebook, you'll explore several common regularization techniques, and use them to improve on a classification model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before getting started, import the necessary packages:" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.10.0\n" ] } ], "source": [ "import tensorflow as tf\n", "\n", "from tensorflow.keras import layers\n", "from tensorflow.keras import regularizers\n", "\n", "print(tf.__version__)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/bin/bash: /home/obm/Prog/miniconda3/envs/qml/lib/libtinfo.so.6: no version information available (required by /bin/bash)\n", "Collecting git+https://github.com/tensorflow/docs\n", " Cloning https://github.com/tensorflow/docs to /tmp/pip-req-build-1negjlc4\n", " Running command git clone --filter=blob:none --quiet https://github.com/tensorflow/docs /tmp/pip-req-build-1negjlc4\n", " Resolved https://github.com/tensorflow/docs to commit 4a7c94274eec86dc1e0f2637fdb4e6074b3127fc\n", " Preparing metadata (setup.py) ... \u001b[?25ldone\n", "\u001b[?25hRequirement already satisfied: astor in /home/obm/Prog/miniconda3/envs/qml/lib/python3.8/site-packages (from tensorflow-docs==0.0.0.dev0) (0.8.1)\n", "Requirement already satisfied: absl-py in /home/obm/Prog/miniconda3/envs/qml/lib/python3.8/site-packages (from tensorflow-docs==0.0.0.dev0) (1.3.0)\n", "Requirement already satisfied: jinja2 in /home/obm/Prog/miniconda3/envs/qml/lib/python3.8/site-packages (from tensorflow-docs==0.0.0.dev0) (3.0.3)\n", "Requirement already satisfied: nbformat in /home/obm/Prog/miniconda3/envs/qml/lib/python3.8/site-packages (from tensorflow-docs==0.0.0.dev0) (5.5.0)\n", "Requirement already satisfied: protobuf<3.20,>=3.12.0 in /home/obm/Prog/miniconda3/envs/qml/lib/python3.8/site-packages (from tensorflow-docs==0.0.0.dev0) (3.19.6)\n", "Requirement already satisfied: pyyaml in /home/obm/Prog/miniconda3/envs/qml/lib/python3.8/site-packages (from tensorflow-docs==0.0.0.dev0) (6.0)\n", "Requirement already satisfied: MarkupSafe>=2.0 in /home/obm/Prog/miniconda3/envs/qml/lib/python3.8/site-packages (from jinja2->tensorflow-docs==0.0.0.dev0) (2.1.1)\n", "Requirement already satisfied: traitlets>=5.1 in /home/obm/Prog/miniconda3/envs/qml/lib/python3.8/site-packages (from nbformat->tensorflow-docs==0.0.0.dev0) (5.1.1)\n", "Requirement already satisfied: jupyter_core in /home/obm/Prog/miniconda3/envs/qml/lib/python3.8/site-packages (from nbformat->tensorflow-docs==0.0.0.dev0) (4.11.1)\n", "Requirement already satisfied: jsonschema>=2.6 in /home/obm/Prog/miniconda3/envs/qml/lib/python3.8/site-packages (from nbformat->tensorflow-docs==0.0.0.dev0) (4.16.0)\n", "Requirement already satisfied: fastjsonschema in /home/obm/Prog/miniconda3/envs/qml/lib/python3.8/site-packages (from nbformat->tensorflow-docs==0.0.0.dev0) (2.16.2)\n", "Requirement already satisfied: pkgutil-resolve-name>=1.3.10 in /home/obm/Prog/miniconda3/envs/qml/lib/python3.8/site-packages (from jsonschema>=2.6->nbformat->tensorflow-docs==0.0.0.dev0) (1.3.10)\n", "Requirement already satisfied: attrs>=17.4.0 in /home/obm/Prog/miniconda3/envs/qml/lib/python3.8/site-packages (from jsonschema>=2.6->nbformat->tensorflow-docs==0.0.0.dev0) (21.4.0)\n", "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /home/obm/Prog/miniconda3/envs/qml/lib/python3.8/site-packages (from jsonschema>=2.6->nbformat->tensorflow-docs==0.0.0.dev0) (0.18.0)\n", "Requirement already satisfied: importlib-resources>=1.4.0 in /home/obm/Prog/miniconda3/envs/qml/lib/python3.8/site-packages (from jsonschema>=2.6->nbformat->tensorflow-docs==0.0.0.dev0) (5.2.0)\n", "Requirement already satisfied: zipp>=3.1.0 in /home/obm/Prog/miniconda3/envs/qml/lib/python3.8/site-packages (from importlib-resources>=1.4.0->jsonschema>=2.6->nbformat->tensorflow-docs==0.0.0.dev0) (3.9.0)\n" ] } ], "source": [ "!pip install git+https://github.com/tensorflow/docs\n", "\n", "import tensorflow_docs as tfdocs\n", "import tensorflow_docs.modeling\n", "import tensorflow_docs.plots" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "from IPython import display\n", "from matplotlib import pyplot as plt\n", "\n", "import numpy as np\n", "\n", "import pathlib\n", "import shutil\n", "import tempfile\n" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "logdir = pathlib.Path(tempfile.mkdtemp())/\"tensorboard_logs\"\n", "shutil.rmtree(logdir, ignore_errors=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Higgs dataset\n", "\n", "The goal of this tutorial is not to do particle physics, so don't dwell on the details of the dataset. It contains 11,000,000 examples, each with 28 features, and a binary class label." ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from http://mlphysics.ics.uci.edu/data/higgs/HIGGS.csv.gz\n", "2816407858/2816407858 [==============================] - 241s 0us/step\n" ] } ], "source": [ "gz = tf.keras.utils.get_file('HIGGS.csv.gz', 'http://mlphysics.ics.uci.edu/data/higgs/HIGGS.csv.gz')" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "FEATURES = 28" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `tf.data.experimental.CsvDataset` class can be used to read csv records directly from a gzip file with no intermediate decompression step." ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [], "source": [ "ds = tf.data.experimental.CsvDataset(gz,[float(),]*(FEATURES+1), compression_type=\"GZIP\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That csv reader class returns a list of scalars for each record. The following function repacks that list of scalars into a (feature_vector, label) pair." ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "def pack_row(*row):\n", " label = row[0]\n", " features = tf.stack(row[1:],1)\n", " return features, label" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TensorFlow is most efficient when operating on large batches of data.\n", "\n", "So, instead of repacking each row individually make a new `tf.data.Dataset` that takes batches of 10,000 examples, applies the `pack_row` function to each batch, and then splits the batches back up into individual records:" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [], "source": [ "packed_ds = ds.batch(10000).map(pack_row).unbatch()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inspect some of the records from this new `packed_ds`.\n", "\n", "The features are not perfectly normalized, but this is sufficient for this tutorial." ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tf.Tensor(\n", "[ 0.869 -0.635 0.226 0.327 -0.69 0.754 -0.249 -1.092 0. 1.375\n", " -0.654 0.93 1.107 1.139 -1.578 -1.047 0. 0.658 -0.01 -0.046\n", " 3.102 1.354 0.98 0.978 0.92 0.722 0.989 0.877], shape=(28,), dtype=float32)\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for features,label in packed_ds.batch(1000).take(1):\n", " print(features[0])\n", " plt.hist(features.numpy().flatten(), bins = 101)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To keep this tutorial relatively short, use just the first 1,000 samples for validation, and the next 10,000 for training:" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "N_VALIDATION = int(1e3)\n", "N_TRAIN = int(1e4)\n", "BUFFER_SIZE = int(1e4)\n", "BATCH_SIZE = 500\n", "STEPS_PER_EPOCH = N_TRAIN//BATCH_SIZE" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `Dataset.skip` and `Dataset.take` methods make this easy.\n", "\n", "At the same time, use the `Dataset.cache` method to ensure that the loader doesn't need to re-read the data from the file on each epoch:" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [], "source": [ "validate_ds = packed_ds.take(N_VALIDATION).cache()\n", "train_ds = packed_ds.skip(N_VALIDATION).take(N_TRAIN).cache()" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_ds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These datasets return individual examples. Use the `Dataset.batch` method to create batches of an appropriate size for training. Before batching, also remember to use `Dataset.shuffle` and `Dataset.repeat` on the training set." ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "validate_ds = validate_ds.batch(BATCH_SIZE)\n", "train_ds = train_ds.shuffle(BUFFER_SIZE).repeat().batch(BATCH_SIZE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Demonstrate overfitting\n", "\n", "The simplest way to prevent overfitting is to start with a small model: A model with a small number of learnable parameters (which is determined by the number of layers and the number of units per layer). In deep learning, the number of learnable parameters in a model is often referred to as the model's \"capacity\".\n", "\n", "Intuitively, a model with more parameters will have more \"memorization capacity\" and therefore will be able to easily learn a perfect dictionary-like mapping between training samples and their targets, a mapping without any generalization power, but this would be useless when making predictions on previously unseen data.\n", "\n", "Always keep this in mind: deep learning models tend to be good at fitting to the training data, but the real challenge is generalization, not fitting.\n", "\n", "On the other hand, if the network has limited memorization resources, it will not be able to learn the mapping as easily. To minimize its loss, it will have to learn compressed representations that have more predictive power. At the same time, if you make your model too small, it will have difficulty fitting to the training data. There is a balance between \"too much capacity\" and \"not enough capacity\".\n", "\n", "Unfortunately, there is no magical formula to determine the right size or architecture of your model (in terms of the number of layers, or the right size for each layer). You will have to experiment using a series of different architectures.\n", "\n", "To find an appropriate model size, it's best to start with relatively few layers and parameters, then begin increasing the size of the layers or adding new layers until you see diminishing returns on the validation loss.\n", "\n", "Start with a simple model using only densely-connected layers (`tf.keras.layers.Dense`) as a baseline, then create larger models, and compare them." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Training procedure" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Many models train better if you gradually reduce the learning rate during training. Use `tf.keras.optimizers.schedules` to reduce the learning rate over time:" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "lr_schedule = tf.keras.optimizers.schedules.InverseTimeDecay(\n", " 0.001,\n", " decay_steps=STEPS_PER_EPOCH*1000,\n", " decay_rate=1,\n", " staircase=False)\n", "\n", "def get_optimizer():\n", " return tf.keras.optimizers.Adam(lr_schedule)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The code above sets a `tf.keras.optimizers.schedules.InverseTimeDecay` to hyperbolically decrease the learning rate to 1/2 of the base rate at 1,000 epochs, 1/3 at 2,000 epochs, and so on." ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "step = np.linspace(0,100000)\n", "lr = lr_schedule(step)\n", "plt.figure(figsize = (8,6))\n", "plt.plot(step/STEPS_PER_EPOCH, lr)\n", "plt.ylim([0,max(plt.ylim())])\n", "plt.xlabel('Epoch')\n", "_ = plt.ylabel('Learning Rate')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each model in this tutorial will use the same training configuration. So set these up in a reusable way, starting with the list of callbacks.\n", "\n", "The training for this tutorial runs for many short epochs. To reduce the logging noise use the `tfdocs.EpochDots` which simply prints a `.` for each epoch, and a full set of metrics every 100 epochs.\n", "\n", "Next include `tf.keras.callbacks.EarlyStopping` to avoid long and unnecessary training times. Note that this callback is set to monitor the `val_binary_crossentropy`, not the `val_loss`. This difference will be important later.\n", "\n", "Use `callbacks.TensorBoard` to generate TensorBoard logs for the training.\n" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "def get_callbacks(name):\n", " return [\n", " tfdocs.modeling.EpochDots(),\n", " tf.keras.callbacks.EarlyStopping(monitor='val_binary_crossentropy', patience=200),\n", " tf.keras.callbacks.TensorBoard(logdir/name),\n", " ]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly each model will use the same `Model.compile` and `Model.fit` settings:" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [], "source": [ "def compile_and_fit(model, name, optimizer=None, max_epochs=10000):\n", " if optimizer is None:\n", " optimizer = get_optimizer()\n", " model.compile(optimizer=optimizer,\n", " loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n", " metrics=[\n", " tf.keras.losses.BinaryCrossentropy(\n", " from_logits=True, name='binary_crossentropy'),\n", " 'accuracy'])\n", "\n", " model.summary()\n", "\n", " history = model.fit(\n", " train_ds,\n", " steps_per_epoch = STEPS_PER_EPOCH,\n", " epochs=max_epochs,\n", " validation_data=validate_ds,\n", " callbacks=get_callbacks(name),\n", " verbose=0)\n", " return history" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tiny model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Start by training a model:" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [], "source": [ "tiny_model = tf.keras.Sequential([\n", " layers.Dense(16, activation='elu', input_shape=(FEATURES,)),\n", " layers.Dense(1)\n", "])" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [], "source": [ "size_histories = {}" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_4\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " dense_8 (Dense) (None, 16) 464 \n", " \n", " dense_9 (Dense) (None, 1) 17 \n", " \n", "=================================================================\n", "Total params: 481\n", "Trainable params: 481\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "\n", "Epoch: 0, accuracy:0.4767, binary_crossentropy:0.7713, loss:0.7713, val_accuracy:0.4840, val_binary_crossentropy:0.7332, val_loss:0.7332, \n", "....................................................................................................\n", "Epoch: 100, accuracy:0.5970, binary_crossentropy:0.6268, loss:0.6268, val_accuracy:0.5650, val_binary_crossentropy:0.6340, val_loss:0.6340, \n", "....................................................................................................\n", "Epoch: 200, accuracy:0.6189, binary_crossentropy:0.6144, loss:0.6144, val_accuracy:0.5990, val_binary_crossentropy:0.6196, val_loss:0.6196, \n", "....................................................................................................\n", "Epoch: 300, accuracy:0.6293, binary_crossentropy:0.6064, loss:0.6064, val_accuracy:0.6190, val_binary_crossentropy:0.6124, val_loss:0.6124, \n", "....................................................................................................\n", "Epoch: 400, accuracy:0.6481, binary_crossentropy:0.5985, loss:0.5985, val_accuracy:0.6420, val_binary_crossentropy:0.6050, val_loss:0.6050, \n", "....................................................................................................\n", "Epoch: 500, accuracy:0.6564, binary_crossentropy:0.5922, loss:0.5922, val_accuracy:0.6500, val_binary_crossentropy:0.5966, val_loss:0.5966, \n", "....................................................................................................\n", "Epoch: 600, accuracy:0.6650, binary_crossentropy:0.5873, loss:0.5873, val_accuracy:0.6580, val_binary_crossentropy:0.5906, val_loss:0.5906, \n", "....................................................................................................\n", "Epoch: 700, accuracy:0.6670, binary_crossentropy:0.5833, loss:0.5833, val_accuracy:0.6750, val_binary_crossentropy:0.5848, val_loss:0.5848, \n", "....................................................................................................\n", "Epoch: 800, accuracy:0.6732, binary_crossentropy:0.5802, loss:0.5802, val_accuracy:0.6750, val_binary_crossentropy:0.5828, val_loss:0.5828, \n", "....................................................................................................\n", "Epoch: 900, accuracy:0.6799, binary_crossentropy:0.5779, loss:0.5779, val_accuracy:0.6650, val_binary_crossentropy:0.5823, val_loss:0.5823, \n", "....................................................................................................\n", "Epoch: 1000, accuracy:0.6823, binary_crossentropy:0.5761, loss:0.5761, val_accuracy:0.6650, val_binary_crossentropy:0.5826, val_loss:0.5826, \n", "....................................................................................................\n", "Epoch: 1100, accuracy:0.6844, binary_crossentropy:0.5744, loss:0.5744, val_accuracy:0.6600, val_binary_crossentropy:0.5831, val_loss:0.5831, \n", "....................................................................................................\n", "Epoch: 1200, accuracy:0.6846, binary_crossentropy:0.5731, loss:0.5731, val_accuracy:0.6830, val_binary_crossentropy:0.5795, val_loss:0.5795, \n", "....................................................................................................\n", "Epoch: 1300, accuracy:0.6865, binary_crossentropy:0.5720, loss:0.5720, val_accuracy:0.6720, val_binary_crossentropy:0.5798, val_loss:0.5798, \n", "............................................................." ] } ], "source": [ "size_histories['Tiny'] = compile_and_fit(tiny_model, 'sizes/Tiny')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now check how the model did:" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.5, 0.7)" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotter = tfdocs.plots.HistoryPlotter(metric = 'binary_crossentropy', smoothing_std=10)\n", "plotter.plot(size_histories)\n", "plt.ylim([0.5, 0.7])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Small model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To check if you can beat the performance of the small model, progressively train some larger models.\n", "\n", "Try two hidden layers with 16 units each:" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [], "source": [ "small_model = tf.keras.Sequential([\n", " # `input_shape` is only required here so that `.summary` works.\n", " layers.Dense(16, activation='elu', input_shape=(FEATURES,)),\n", " layers.Dense(16, activation='elu'),\n", " layers.Dense(1)\n", "])" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_5\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " dense_10 (Dense) (None, 16) 464 \n", " \n", " dense_11 (Dense) (None, 16) 272 \n", " \n", " dense_12 (Dense) (None, 1) 17 \n", " \n", "=================================================================\n", "Total params: 753\n", "Trainable params: 753\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "\n", "Epoch: 0, accuracy:0.4809, binary_crossentropy:0.7568, loss:0.7568, val_accuracy:0.4610, val_binary_crossentropy:0.7220, val_loss:0.7220, \n", "....................................................................................................\n", "Epoch: 100, accuracy:0.6029, binary_crossentropy:0.6207, loss:0.6207, val_accuracy:0.6060, val_binary_crossentropy:0.6205, val_loss:0.6205, \n", "....................................................................................................\n", "Epoch: 200, accuracy:0.6350, binary_crossentropy:0.6041, loss:0.6041, val_accuracy:0.6390, val_binary_crossentropy:0.6047, val_loss:0.6047, \n", "....................................................................................................\n", "Epoch: 300, accuracy:0.6580, binary_crossentropy:0.5881, loss:0.5881, val_accuracy:0.6200, val_binary_crossentropy:0.5968, val_loss:0.5968, \n", "....................................................................................................\n", "Epoch: 400, accuracy:0.6700, binary_crossentropy:0.5780, loss:0.5780, val_accuracy:0.6650, val_binary_crossentropy:0.5899, val_loss:0.5899, \n", "....................................................................................................\n", "Epoch: 500, accuracy:0.6826, binary_crossentropy:0.5703, loss:0.5703, val_accuracy:0.6660, val_binary_crossentropy:0.5909, val_loss:0.5909, \n", "....................................................................................................\n", "Epoch: 600, accuracy:0.6849, binary_crossentropy:0.5660, loss:0.5660, val_accuracy:0.6550, val_binary_crossentropy:0.5934, val_loss:0.5934, \n", "........................................................" ] } ], "source": [ "size_histories['Small'] = compile_and_fit(small_model, 'sizes/Small')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Medium model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now try three hidden layers with 64 units each:" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [], "source": [ "medium_model = tf.keras.Sequential([\n", " layers.Dense(64, activation='elu', input_shape=(FEATURES,)),\n", " layers.Dense(64, activation='elu'),\n", " layers.Dense(64, activation='elu'),\n", " layers.Dense(1)\n", "])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And train the model using the same data:" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_6\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " dense_13 (Dense) (None, 64) 1856 \n", " \n", " dense_14 (Dense) (None, 64) 4160 \n", " \n", " dense_15 (Dense) (None, 64) 4160 \n", " \n", " dense_16 (Dense) (None, 1) 65 \n", " \n", "=================================================================\n", "Total params: 10,241\n", "Trainable params: 10,241\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "\n", "Epoch: 0, accuracy:0.4809, binary_crossentropy:0.7009, loss:0.7009, val_accuracy:0.4680, val_binary_crossentropy:0.6839, val_loss:0.6839, \n", "....................................................................................................\n", "Epoch: 100, accuracy:0.7226, binary_crossentropy:0.5214, loss:0.5214, val_accuracy:0.6590, val_binary_crossentropy:0.6073, val_loss:0.6073, \n", "....................................................................................................\n", "Epoch: 200, accuracy:0.7883, binary_crossentropy:0.4271, loss:0.4271, val_accuracy:0.6400, val_binary_crossentropy:0.6828, val_loss:0.6828, \n", "..........................................................." ] } ], "source": [ "size_histories['Medium'] = compile_and_fit(medium_model, \"sizes/Medium\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Large model\n", "\n", "As an exercise, you can create an even larger model and check how quickly it begins overfitting. Next, add to this benchmark a network that has much more capacity, far more than the problem would warrant:" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [], "source": [ "large_model = tf.keras.Sequential([\n", " layers.Dense(512, activation='elu', input_shape=(FEATURES,)),\n", " layers.Dense(512, activation='elu'),\n", " layers.Dense(512, activation='elu'),\n", " layers.Dense(512, activation='elu'),\n", " layers.Dense(1)\n", "])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And, again, train the model using the same data:" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_7\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " dense_17 (Dense) (None, 512) 14848 \n", " \n", " dense_18 (Dense) (None, 512) 262656 \n", " \n", " dense_19 (Dense) (None, 512) 262656 \n", " \n", " dense_20 (Dense) (None, 512) 262656 \n", " \n", " dense_21 (Dense) (None, 1) 513 \n", " \n", "=================================================================\n", "Total params: 803,329\n", "Trainable params: 803,329\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "\n", "Epoch: 0, accuracy:0.5095, binary_crossentropy:0.7698, loss:0.7698, val_accuracy:0.4970, val_binary_crossentropy:0.6827, val_loss:0.6827, \n", "....................................................................................................\n", "Epoch: 100, accuracy:1.0000, binary_crossentropy:0.0022, loss:0.0022, val_accuracy:0.6660, val_binary_crossentropy:1.8206, val_loss:1.8206, \n", "....................................................................................................\n", "Epoch: 200, accuracy:1.0000, binary_crossentropy:0.0001, loss:0.0001, val_accuracy:0.6670, val_binary_crossentropy:2.4770, val_loss:2.4770, \n", "........................." ] } ], "source": [ "size_histories['large'] = compile_and_fit(large_model, \"sizes/large\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot the training and validation losses" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The solid lines show the training loss, and the dashed lines show the validation loss (remember: a lower validation loss indicates a better model)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While building a larger model gives it more power, if this power is not constrained somehow it can easily overfit to the training set.\n", "\n", "In this example, typically, only the `\"Tiny\"` model manages to avoid overfitting altogether, and each of the larger models overfit the data more quickly. This becomes so severe for the `\"large\"` model that you need to switch the plot to a log-scale to really figure out what's happening.\n", "\n", "This is apparent if you plot and compare the validation metrics to the training metrics.\n", "\n", "* It's normal for there to be a small difference.\n", "* If both metrics are moving in the same direction, everything is fine.\n", "* If the validation metric begins to stagnate while the training metric continues to improve, you are probably close to overfitting.\n", "* If the validation metric is going in the wrong direction, the model is clearly overfitting." ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'Epochs [Log Scale]')" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotter.plot(size_histories)\n", "a = plt.xscale('log')\n", "plt.xlim([5, max(plt.xlim())])\n", "plt.ylim([0.5, 0.7])\n", "plt.xlabel(\"Epochs [Log Scale]\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: All the above training runs used the `callbacks.EarlyStopping` to end the training once it was clear the model was not making progress." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### View in TensorBoard\n", "\n", "These models all wrote TensorBoard logs during training.\n", "\n", "Open an embedded TensorBoard viewer inside a notebook:" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#docs_infra: no_execute\n", "\n", "# Load the TensorBoard notebook extension\n", "%load_ext tensorboard\n", "\n", "# Open an embedded TensorBoard viewer\n", "%tensorboard --logdir {logdir}/sizes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can view the [results of a previous run](https://tensorboard.dev/experiment/vW7jmmF9TmKmy3rbheMQpw/#scalars&_smoothingWeight=0.97) of this notebook on [TensorBoard.dev](https://tensorboard.dev/).\n", "\n", "TensorBoard.dev is a managed experience for hosting, tracking, and sharing ML experiments with everyone.\n", "\n", "It's also included in an `\n", " " ], "text/plain": [ "" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "display.IFrame(\n", " src=\"https://tensorboard.dev/experiment/vW7jmmF9TmKmy3rbheMQpw/#scalars&_smoothingWeight=0.97\",\n", " width=\"100%\", height=\"800px\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to share TensorBoard results you can upload the logs to [TensorBoard.dev](https://tensorboard.dev/) by copying the following into a code-cell.\n", "\n", "Note: This step requires a Google account.\n", "\n", "```\n", "!tensorboard dev upload --logdir {logdir}/sizes\n", "```\n", "\n", "Caution: This command does not terminate. It's designed to continuously upload the results of long-running experiments. Once your data is uploaded you need to stop it using the \"interrupt execution\" option in your notebook tool." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Strategies to prevent overfitting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before getting into the content of this section copy the training logs from the `\"Tiny\"` model above, to use as a baseline for comparison." ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PosixPath('/tmp/tmp90xi0kdy/tensorboard_logs/regularizers/Tiny')" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "shutil.rmtree(logdir/'regularizers/Tiny', ignore_errors=True)\n", "shutil.copytree(logdir/'sizes/Tiny', logdir/'regularizers/Tiny')" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [], "source": [ "regularizer_histories = {}\n", "regularizer_histories['Tiny'] = size_histories['Tiny']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add weight regularization\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You may be familiar with Occam's Razor principle: given two explanations for something, the explanation most likely to be correct is the \"simplest\" one, the one that makes the least amount of assumptions. This also applies to the models learned by neural networks: given some training data and a network architecture, there are multiple sets of weights values (multiple models) that could explain the data, and simpler models are less likely to overfit than complex ones.\n", "\n", "A \"simple model\" in this context is a model where the distribution of parameter values has less entropy (or a model with fewer parameters altogether, as demonstrated in the section above). Thus a common way to mitigate overfitting is to put constraints on the complexity of a network by forcing its weights only to take small values, which makes the distribution of weight values more \"regular\". This is called \"weight regularization\", and it is done by adding to the loss function of the network a cost associated with having large weights. This cost comes in two flavors:\n", "\n", "* [L1 regularization](https://developers.google.com/machine-learning/glossary/#L1_regularization), where the cost added is proportional to the absolute value of the weights coefficients (i.e. to what is called the \"L1 norm\" of the weights).\n", "\n", "* [L2 regularization](https://developers.google.com/machine-learning/glossary/#L2_regularization), where the cost added is proportional to the square of the value of the weights coefficients (i.e. to what is called the squared \"L2 norm\" of the weights). L2 regularization is also called weight decay in the context of neural networks. Don't let the different name confuse you: weight decay is mathematically the exact same as L2 regularization.\n", "\n", "L1 regularization pushes weights towards exactly zero, encouraging a sparse model. L2 regularization will penalize the weights parameters without making them sparse since the penalty goes to zero for small weights—one reason why L2 is more common.\n", "\n", "In `tf.keras`, weight regularization is added by passing weight regularizer instances to layers as keyword arguments. Add L2 weight regularization:" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_8\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " dense_22 (Dense) (None, 512) 14848 \n", " \n", " dense_23 (Dense) (None, 512) 262656 \n", " \n", " dense_24 (Dense) (None, 512) 262656 \n", " \n", " dense_25 (Dense) (None, 512) 262656 \n", " \n", " dense_26 (Dense) (None, 1) 513 \n", " \n", "=================================================================\n", "Total params: 803,329\n", "Trainable params: 803,329\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "\n", "Epoch: 0, accuracy:0.5119, binary_crossentropy:0.8326, loss:2.3694, val_accuracy:0.5180, val_binary_crossentropy:0.6742, val_loss:2.1387, \n", "....................................................................................................\n", "Epoch: 100, accuracy:0.6627, binary_crossentropy:0.5976, loss:0.6219, val_accuracy:0.6540, val_binary_crossentropy:0.5830, val_loss:0.6069, \n", "....................................................................................................\n", "Epoch: 200, accuracy:0.6718, binary_crossentropy:0.5835, loss:0.6074, val_accuracy:0.6830, val_binary_crossentropy:0.5862, val_loss:0.6101, \n", "....................................................................................................\n", "Epoch: 300, accuracy:0.6840, binary_crossentropy:0.5734, loss:0.5985, val_accuracy:0.6540, val_binary_crossentropy:0.5822, val_loss:0.6069, \n", "....................................................................................................\n", "Epoch: 400, accuracy:0.6936, binary_crossentropy:0.5647, loss:0.5915, val_accuracy:0.6690, val_binary_crossentropy:0.5816, val_loss:0.6082, \n", "....................................................................................................\n", "Epoch: 500, accuracy:0.6980, binary_crossentropy:0.5582, loss:0.5855, val_accuracy:0.6580, val_binary_crossentropy:0.5857, val_loss:0.6132, \n", "....................................................................................................\n", "Epoch: 600, accuracy:0.6967, binary_crossentropy:0.5554, loss:0.5831, val_accuracy:0.6850, val_binary_crossentropy:0.5851, val_loss:0.6130, \n", "....................................................................................................\n", "Epoch: 700, accuracy:0.7082, binary_crossentropy:0.5425, loss:0.5714, val_accuracy:0.6770, val_binary_crossentropy:0.5886, val_loss:0.6174, \n", "..................................................................................." ] } ], "source": [ "l2_model = tf.keras.Sequential([\n", " layers.Dense(512, activation='elu',\n", " kernel_regularizer=regularizers.l2(0.001),\n", " input_shape=(FEATURES,)),\n", " layers.Dense(512, activation='elu',\n", " kernel_regularizer=regularizers.l2(0.001)),\n", " layers.Dense(512, activation='elu',\n", " kernel_regularizer=regularizers.l2(0.001)),\n", " layers.Dense(512, activation='elu',\n", " kernel_regularizer=regularizers.l2(0.001)),\n", " layers.Dense(1)\n", "])\n", "\n", "regularizer_histories['l2'] = compile_and_fit(l2_model, \"regularizers/l2\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`l2(0.001)` means that every coefficient in the weight matrix of the layer will add `0.001 * weight_coefficient_value**2` to the total **loss** of the network.\n", "\n", "That is why we're monitoring the `binary_crossentropy` directly. Because it doesn't have this regularization component mixed in.\n", "\n", "So, that same `\"Large\"` model with an `L2` regularization penalty performs much better:\n" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.5, 0.7)" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotter.plot(regularizer_histories)\n", "plt.ylim([0.5, 0.7])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As demonstrated in the diagram above, the `\"L2\"` regularized model is now much more competitive with the `\"Tiny\"` model. This `\"L2\"` model is also much more resistant to overfitting than the `\"Large\"` model it was based on despite having the same number of parameters." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### More info\n", "\n", "There are two important things to note about this sort of regularization:\n", "\n", "1. If you are writing your own training loop, then you need to be sure to ask the model for its regularization losses." ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [], "source": [ "result = l2_model(features)\n", "regularization_loss=tf.add_n(l2_model.losses)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2. This implementation works by adding the weight penalties to the model's loss, and then applying a standard optimization procedure after that.\n", "\n", "There is a second approach that instead only runs the optimizer on the raw loss, and then while applying the calculated step the optimizer also applies some weight decay. This \"decoupled weight decay\" is used in optimizers like `tf.keras.optimizers.Ftrl` and `tfa.optimizers.AdamW`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add dropout\n", "\n", "Dropout is one of the most effective and most commonly used regularization techniques for neural networks, developed by Hinton and his students at the University of Toronto.\n", "\n", "The intuitive explanation for dropout is that because individual nodes in the network cannot rely on the output of the others, each node must output features that are useful on their own.\n", "\n", "Dropout, applied to a layer, consists of randomly \"dropping out\" (i.e. set to zero) a number of output features of the layer during training. For example, a given layer would normally have returned a vector `[0.2, 0.5, 1.3, 0.8, 1.1]` for a given input sample during training; after applying dropout, this vector will have a few zero entries distributed at random, e.g. `[0, 0.5, 1.3, 0, 1.1]`.\n", "\n", "The \"dropout rate\" is the fraction of the features that are being zeroed-out; it is usually set between 0.2 and 0.5. At test time, no units are dropped out, and instead the layer's output values are scaled down by a factor equal to the dropout rate, so as to balance for the fact that more units are active than at training time.\n", "\n", "In Keras, you can introduce dropout in a network via the `tf.keras.layers.Dropout` layer, which gets applied to the output of layer right before.\n", "\n", "Add two dropout layers to your network to check how well they do at reducing overfitting:" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_9\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " dense_27 (Dense) (None, 512) 14848 \n", " \n", " dropout (Dropout) (None, 512) 0 \n", " \n", " dense_28 (Dense) (None, 512) 262656 \n", " \n", " dropout_1 (Dropout) (None, 512) 0 \n", " \n", " dense_29 (Dense) (None, 512) 262656 \n", " \n", " dropout_2 (Dropout) (None, 512) 0 \n", " \n", " dense_30 (Dense) (None, 512) 262656 \n", " \n", " dropout_3 (Dropout) (None, 512) 0 \n", " \n", " dense_31 (Dense) (None, 1) 513 \n", " \n", "=================================================================\n", "Total params: 803,329\n", "Trainable params: 803,329\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "\n", "Epoch: 0, accuracy:0.5019, binary_crossentropy:0.8041, loss:0.8041, val_accuracy:0.5490, val_binary_crossentropy:0.6754, val_loss:0.6754, \n", "....................................................................................................\n", "Epoch: 100, accuracy:0.6551, binary_crossentropy:0.5972, loss:0.5972, val_accuracy:0.6790, val_binary_crossentropy:0.5800, val_loss:0.5800, \n", "....................................................................................................\n", "Epoch: 200, accuracy:0.6896, binary_crossentropy:0.5554, loss:0.5554, val_accuracy:0.6710, val_binary_crossentropy:0.5932, val_loss:0.5932, \n", "....................................................................................................\n", "Epoch: 300, accuracy:0.7190, binary_crossentropy:0.5140, loss:0.5140, val_accuracy:0.6850, val_binary_crossentropy:0.5912, val_loss:0.5912, \n", "............................................" ] } ], "source": [ "dropout_model = tf.keras.Sequential([\n", " layers.Dense(512, activation='elu', input_shape=(FEATURES,)),\n", " layers.Dropout(0.5),\n", " layers.Dense(512, activation='elu'),\n", " layers.Dropout(0.5),\n", " layers.Dense(512, activation='elu'),\n", " layers.Dropout(0.5),\n", " layers.Dense(512, activation='elu'),\n", " layers.Dropout(0.5),\n", " layers.Dense(1)\n", "])\n", "\n", "regularizer_histories['dropout'] = compile_and_fit(dropout_model, \"regularizers/dropout\")" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.5, 0.7)" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotter.plot(regularizer_histories)\n", "plt.ylim([0.5, 0.7])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's clear from this plot that both of these regularization approaches improve the behavior of the `\"Large\"` model. But this still doesn't beat even the `\"Tiny\"` baseline.\n", "\n", "Next try them both, together, and see if that does better." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Combined L2 + dropout" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_10\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " dense_32 (Dense) (None, 512) 14848 \n", " \n", " dropout_4 (Dropout) (None, 512) 0 \n", " \n", " dense_33 (Dense) (None, 512) 262656 \n", " \n", " dropout_5 (Dropout) (None, 512) 0 \n", " \n", " dense_34 (Dense) (None, 512) 262656 \n", " \n", " dropout_6 (Dropout) (None, 512) 0 \n", " \n", " dense_35 (Dense) (None, 512) 262656 \n", " \n", " dropout_7 (Dropout) (None, 512) 0 \n", " \n", " dense_36 (Dense) (None, 1) 513 \n", " \n", "=================================================================\n", "Total params: 803,329\n", "Trainable params: 803,329\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "\n", "Epoch: 0, accuracy:0.5045, binary_crossentropy:0.8001, loss:0.9582, val_accuracy:0.5160, val_binary_crossentropy:0.7132, val_loss:0.8705, \n", "....................................................................................................\n", "Epoch: 100, accuracy:0.6471, binary_crossentropy:0.6060, loss:0.6355, val_accuracy:0.6560, val_binary_crossentropy:0.5890, val_loss:0.6184, \n", "....................................................................................................\n", "Epoch: 200, accuracy:0.6706, binary_crossentropy:0.5929, loss:0.6183, val_accuracy:0.6460, val_binary_crossentropy:0.5866, val_loss:0.6121, \n", "....................................................................................................\n", "Epoch: 300, accuracy:0.6703, binary_crossentropy:0.5832, loss:0.6106, val_accuracy:0.6660, val_binary_crossentropy:0.5666, val_loss:0.5940, \n", "....................................................................................................\n", "Epoch: 400, accuracy:0.6768, binary_crossentropy:0.5735, loss:0.6028, val_accuracy:0.6950, val_binary_crossentropy:0.5565, val_loss:0.5858, \n", "....................................................................................................\n", "Epoch: 500, accuracy:0.6791, binary_crossentropy:0.5725, loss:0.6036, val_accuracy:0.7020, val_binary_crossentropy:0.5522, val_loss:0.5833, \n", "....................................................................................................\n", "Epoch: 600, accuracy:0.6779, binary_crossentropy:0.5652, loss:0.5981, val_accuracy:0.6950, val_binary_crossentropy:0.5411, val_loss:0.5741, \n", "....................................................................................................\n", "Epoch: 700, accuracy:0.6869, binary_crossentropy:0.5625, loss:0.5967, val_accuracy:0.6940, val_binary_crossentropy:0.5504, val_loss:0.5846, \n", "....................................................................................................\n", "Epoch: 800, accuracy:0.6869, binary_crossentropy:0.5610, loss:0.5962, val_accuracy:0.6980, val_binary_crossentropy:0.5454, val_loss:0.5805, \n", "....................................................................................................\n", "Epoch: 900, accuracy:0.6925, binary_crossentropy:0.5583, loss:0.5947, val_accuracy:0.7050, val_binary_crossentropy:0.5447, val_loss:0.5811, \n", "............................................................................" ] } ], "source": [ "combined_model = tf.keras.Sequential([\n", " layers.Dense(512, kernel_regularizer=regularizers.l2(0.0001),\n", " activation='elu', input_shape=(FEATURES,)),\n", " layers.Dropout(0.5),\n", " layers.Dense(512, kernel_regularizer=regularizers.l2(0.0001),\n", " activation='elu'),\n", " layers.Dropout(0.5),\n", " layers.Dense(512, kernel_regularizer=regularizers.l2(0.0001),\n", " activation='elu'),\n", " layers.Dropout(0.5),\n", " layers.Dense(512, kernel_regularizer=regularizers.l2(0.0001),\n", " activation='elu'),\n", " layers.Dropout(0.5),\n", " layers.Dense(1)\n", "])\n", "\n", "regularizer_histories['combined'] = compile_and_fit(combined_model, \"regularizers/combined\")" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.5, 0.7)" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotter.plot(regularizer_histories)\n", "plt.ylim([0.5, 0.7])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This model with the `\"Combined\"` regularization is obviously the best one so far." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### View in TensorBoard\n", "\n", "These models also recorded TensorBoard logs.\n", "\n", "To open an embedded tensorboard viewer inside a notebook, copy the following into a code-cell:\n", "\n", "```\n", "%tensorboard --logdir {logdir}/regularizers\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Takeaway" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To recap, here are the most common ways to prevent overfitting in neural networks:\n", "\n", "* Get more training data.\n", "* Reduce the capacity of the network.\n", "* Add weight regularization.\n", "* Add dropout.\n", "\n", "Two important approaches not covered in this guide are:\n", "\n", "* Data augmentation\n", "* Batch normalization (`tf.keras.layers.BatchNormalization`)\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# DO IT YOURSELF 2:\n", "\n", "Modify any example above with a batch normalization layer. Prepare it as a seperate pyton script. Run it in Carbon" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Save and load models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Model progress can be saved during and after training. This means a model can resume where it left off and avoid long training times. Saving also means you can share your model and others can recreate your work. When publishing research models and techniques, most machine learning practitioners share:\n", "\n", "* code to create the model, and\n", "* the trained weights, or parameters, for the model\n", "\n", "Sharing this data helps others understand how the model works and try it themselves with new data.\n", "\n", "Caution: TensorFlow models are code and it is important to be careful with untrusted code. See [Using TensorFlow Securely](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for details.\n", "\n", "### Options\n", "\n", "There are different ways to save TensorFlow models depending on the API you're using. This guide uses [tf.keras](https://www.tensorflow.org/guide/keras)—a high-level API to build and train models in TensorFlow. For other approaches, refer to the [Using the SavedModel format guide](../../guide/saved_model.ipynb) and the [Save and load Keras models guide](https://www.tensorflow.org/guide/keras/save_and_serialize)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup\n", "\n", "### Installs and imports" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Install and import TensorFlow and dependencies:" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/bin/bash: /home/obm/Prog/miniconda3/envs/qml/lib/libtinfo.so.6: no version information available (required by /bin/bash)\n", "Requirement already satisfied: pyyaml in /home/obm/Prog/miniconda3/envs/qml/lib/python3.8/site-packages (6.0)\n", "Requirement already satisfied: h5py in /home/obm/Prog/miniconda3/envs/qml/lib/python3.8/site-packages (3.7.0)\n", "Requirement already satisfied: numpy>=1.14.5 in /home/obm/Prog/miniconda3/envs/qml/lib/python3.8/site-packages (from h5py) (1.23.4)\n" ] } ], "source": [ "!pip install pyyaml h5py # Required to save models in HDF5 format" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.10.0\n" ] } ], "source": [ "import os\n", "\n", "import tensorflow as tf\n", "from tensorflow import keras\n", "\n", "print(tf.version.VERSION)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get an example dataset\n", "\n", "To demonstrate how to save and load weights, you'll use the [MNIST dataset](http://yann.lecun.com/exdb/mnist/). To speed up these runs, use the first 1000 examples:" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n", "11490434/11490434 [==============================] - 1s 0us/step\n" ] } ], "source": [ "(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()\n", "\n", "train_labels = train_labels[:1000]\n", "test_labels = test_labels[:1000]\n", "\n", "train_images = train_images[:1000].reshape(-1, 28 * 28) / 255.0\n", "test_images = test_images[:1000].reshape(-1, 28 * 28) / 255.0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define a model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Start by building a simple sequential model:" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_11\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " dense_37 (Dense) (None, 512) 401920 \n", " \n", " dropout_8 (Dropout) (None, 512) 0 \n", " \n", " dense_38 (Dense) (None, 10) 5130 \n", " \n", "=================================================================\n", "Total params: 407,050\n", "Trainable params: 407,050\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "# Define a simple sequential model\n", "def create_model():\n", " model = tf.keras.Sequential([\n", " keras.layers.Dense(512, activation='relu', input_shape=(784,)),\n", " keras.layers.Dropout(0.2),\n", " keras.layers.Dense(10)\n", " ])\n", "\n", " model.compile(optimizer='adam',\n", " loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n", " metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])\n", "\n", " return model\n", "\n", "# Create a basic model instance\n", "model = create_model()\n", "\n", "# Display the model's architecture\n", "model.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Save checkpoints during training" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can use a trained model without having to retrain it, or pick-up training where you left off in case the training process was interrupted. The `tf.keras.callbacks.ModelCheckpoint` callback allows you to continually save the model both *during* and at *the end* of training.\n", "\n", "### Checkpoint callback usage\n", "\n", "Create a `tf.keras.callbacks.ModelCheckpoint` callback that saves weights only during training:" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/10\n", " 1/32 [..............................] - ETA: 5s - loss: 2.2638 - sparse_categorical_accuracy: 0.1562\n", "Epoch 1: saving model to training_1/cp.ckpt\n", "32/32 [==============================] - 0s 7ms/step - loss: 1.1660 - sparse_categorical_accuracy: 0.6560 - val_loss: 0.7017 - val_sparse_categorical_accuracy: 0.7850\n", "Epoch 2/10\n", " 1/32 [..............................] - ETA: 0s - loss: 0.3979 - sparse_categorical_accuracy: 0.9375\n", "Epoch 2: saving model to training_1/cp.ckpt\n", "32/32 [==============================] - 0s 4ms/step - loss: 0.4133 - sparse_categorical_accuracy: 0.8900 - val_loss: 0.5324 - val_sparse_categorical_accuracy: 0.8420\n", "Epoch 3/10\n", "31/32 [============================>.] - ETA: 0s - loss: 0.2830 - sparse_categorical_accuracy: 0.9315\n", "Epoch 3: saving model to training_1/cp.ckpt\n", "32/32 [==============================] - 0s 5ms/step - loss: 0.2845 - sparse_categorical_accuracy: 0.9310 - val_loss: 0.4742 - val_sparse_categorical_accuracy: 0.8480\n", "Epoch 4/10\n", "29/32 [==========================>...] - ETA: 0s - loss: 0.2146 - sparse_categorical_accuracy: 0.9515\n", "Epoch 4: saving model to training_1/cp.ckpt\n", "32/32 [==============================] - 0s 5ms/step - loss: 0.2073 - sparse_categorical_accuracy: 0.9550 - val_loss: 0.4447 - val_sparse_categorical_accuracy: 0.8560\n", "Epoch 5/10\n", "29/32 [==========================>...] - ETA: 0s - loss: 0.1575 - sparse_categorical_accuracy: 0.9644\n", "Epoch 5: saving model to training_1/cp.ckpt\n", "32/32 [==============================] - 0s 5ms/step - loss: 0.1527 - sparse_categorical_accuracy: 0.9670 - val_loss: 0.4213 - val_sparse_categorical_accuracy: 0.8550\n", "Epoch 6/10\n", "29/32 [==========================>...] - ETA: 0s - loss: 0.1125 - sparse_categorical_accuracy: 0.9763\n", "Epoch 6: saving model to training_1/cp.ckpt\n", "32/32 [==============================] - 0s 5ms/step - loss: 0.1115 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.4354 - val_sparse_categorical_accuracy: 0.8660\n", "Epoch 7/10\n", " 1/32 [..............................] - ETA: 0s - loss: 0.0578 - sparse_categorical_accuracy: 1.0000\n", "Epoch 7: saving model to training_1/cp.ckpt\n", "32/32 [==============================] - 0s 4ms/step - loss: 0.0878 - sparse_categorical_accuracy: 0.9860 - val_loss: 0.4152 - val_sparse_categorical_accuracy: 0.8670\n", "Epoch 8/10\n", "28/32 [=========================>....] - ETA: 0s - loss: 0.0557 - sparse_categorical_accuracy: 0.9967\n", "Epoch 8: saving model to training_1/cp.ckpt\n", "32/32 [==============================] - 0s 5ms/step - loss: 0.0570 - sparse_categorical_accuracy: 0.9970 - val_loss: 0.3894 - val_sparse_categorical_accuracy: 0.8690\n", "Epoch 9/10\n", "30/32 [===========================>..] - ETA: 0s - loss: 0.0445 - sparse_categorical_accuracy: 1.0000\n", "Epoch 9: saving model to training_1/cp.ckpt\n", "32/32 [==============================] - 0s 5ms/step - loss: 0.0445 - sparse_categorical_accuracy: 1.0000 - val_loss: 0.3946 - val_sparse_categorical_accuracy: 0.8760\n", "Epoch 10/10\n", "29/32 [==========================>...] - ETA: 0s - loss: 0.0382 - sparse_categorical_accuracy: 0.9968\n", "Epoch 10: saving model to training_1/cp.ckpt\n", "32/32 [==============================] - 0s 5ms/step - loss: 0.0382 - sparse_categorical_accuracy: 0.9970 - val_loss: 0.4046 - val_sparse_categorical_accuracy: 0.8640\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "checkpoint_path = \"training_1/cp.ckpt\"\n", "checkpoint_dir = os.path.dirname(checkpoint_path)\n", "\n", "# Create a callback that saves the model's weights\n", "cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,\n", " save_weights_only=True,\n", " verbose=1)\n", "\n", "# Train the model with the new callback\n", "model.fit(train_images, \n", " train_labels, \n", " epochs=10,\n", " validation_data=(test_images, test_labels),\n", " callbacks=[cp_callback]) # Pass callback to training\n", "\n", "# This may generate warnings related to saving the state of the optimizer.\n", "# These warnings (and similar warnings throughout this notebook)\n", "# are in place to discourage outdated usage, and can be ignored." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This creates a single collection of TensorFlow checkpoint files that are updated at the end of each epoch:" ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['cp.ckpt.index', 'checkpoint', 'cp.ckpt.data-00000-of-00001']" ] }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.listdir(checkpoint_dir)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As long as two models share the same architecture you can share weights between them. So, when restoring a model from weights-only, create a model with the same architecture as the original model and then set its weights. \n", "\n", "Now rebuild a fresh, untrained model and evaluate it on the test set. An untrained model will perform at chance levels (~10% accuracy):" ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "32/32 - 0s - loss: 2.3282 - sparse_categorical_accuracy: 0.1310 - 96ms/epoch - 3ms/step\n", "Untrained model, accuracy: 13.10%\n" ] } ], "source": [ "# Create a basic model instance\n", "model = create_model()\n", "\n", "# Evaluate the model\n", "loss, acc = model.evaluate(test_images, test_labels, verbose=2)\n", "print(\"Untrained model, accuracy: {:5.2f}%\".format(100 * acc))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then load the weights from the checkpoint and re-evaluate:" ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "32/32 - 0s - loss: 0.4046 - sparse_categorical_accuracy: 0.8640 - 40ms/epoch - 1ms/step\n", "Restored model, accuracy: 86.40%\n" ] } ], "source": [ "# Loads the weights\n", "model.load_weights(checkpoint_path)\n", "\n", "# Re-evaluate the model\n", "loss, acc = model.evaluate(test_images, test_labels, verbose=2)\n", "print(\"Restored model, accuracy: {:5.2f}%\".format(100 * acc))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Checkpoint callback options\n", "\n", "The callback provides several options to provide unique names for checkpoints and adjust the checkpointing frequency.\n", "\n", "Train a new model, and save uniquely named checkpoints once every five epochs:" ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:Detecting that an object or model or tf.train.Checkpoint is being deleted with unrestored values. See the following logs for the specific values in question. To silence these warnings, use `status.expect_partial()`. See https://www.tensorflow.org/api_docs/python/tf/train/Checkpoint#restorefor details about the status object returned by the restore function.\n", "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.iter\n", "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.beta_1\n", "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.beta_2\n", "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.decay\n", "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.learning_rate\n", "\n", "Epoch 5: saving model to training_2/cp-0005.ckpt\n", "\n", "Epoch 10: saving model to training_2/cp-0010.ckpt\n", "\n", "Epoch 15: saving model to training_2/cp-0015.ckpt\n", "\n", "Epoch 20: saving model to training_2/cp-0020.ckpt\n", "\n", "Epoch 25: saving model to training_2/cp-0025.ckpt\n", "\n", "Epoch 30: saving model to training_2/cp-0030.ckpt\n", "\n", "Epoch 35: saving model to training_2/cp-0035.ckpt\n", "\n", "Epoch 40: saving model to training_2/cp-0040.ckpt\n", "\n", "Epoch 45: saving model to training_2/cp-0045.ckpt\n", "\n", "Epoch 50: saving model to training_2/cp-0050.ckpt\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Include the epoch in the file name (uses `str.format`)\n", "checkpoint_path = \"training_2/cp-{epoch:04d}.ckpt\"\n", "checkpoint_dir = os.path.dirname(checkpoint_path)\n", "\n", "batch_size = 32\n", "\n", "# Create a callback that saves the model's weights every 5 epochs\n", "cp_callback = tf.keras.callbacks.ModelCheckpoint(\n", " filepath=checkpoint_path, \n", " verbose=1, \n", " save_weights_only=True,\n", " save_freq=5*batch_size)\n", "\n", "# Create a new model instance\n", "model = create_model()\n", "\n", "# Save the weights using the `checkpoint_path` format\n", "model.save_weights(checkpoint_path.format(epoch=0))\n", "\n", "# Train the model with the new callback\n", "model.fit(train_images, \n", " train_labels,\n", " epochs=50, \n", " batch_size=batch_size, \n", " callbacks=[cp_callback],\n", " validation_data=(test_images, test_labels),\n", " verbose=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, review the resulting checkpoints and choose the latest one:" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['cp-0035.ckpt.index',\n", " 'cp-0050.ckpt.index',\n", " 'cp-0000.ckpt.index',\n", " 'cp-0025.ckpt.data-00000-of-00001',\n", " 'cp-0030.ckpt.index',\n", " 'cp-0000.ckpt.data-00000-of-00001',\n", " 'cp-0025.ckpt.index',\n", " 'cp-0045.ckpt.data-00000-of-00001',\n", " 'checkpoint',\n", " 'cp-0015.ckpt.data-00000-of-00001',\n", " 'cp-0030.ckpt.data-00000-of-00001',\n", " 'cp-0015.ckpt.index',\n", " 'cp-0020.ckpt.data-00000-of-00001',\n", " 'cp-0045.ckpt.index',\n", " 'cp-0020.ckpt.index',\n", " 'cp-0050.ckpt.data-00000-of-00001',\n", " 'cp-0035.ckpt.data-00000-of-00001',\n", " 'cp-0005.ckpt.index',\n", " 'cp-0010.ckpt.index',\n", " 'cp-0040.ckpt.index',\n", " 'cp-0040.ckpt.data-00000-of-00001',\n", " 'cp-0010.ckpt.data-00000-of-00001',\n", " 'cp-0005.ckpt.data-00000-of-00001']" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.listdir(checkpoint_dir)" ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'training_2/cp-0050.ckpt'" ] }, "execution_count": 106, "metadata": {}, "output_type": "execute_result" } ], "source": [ "latest = tf.train.latest_checkpoint(checkpoint_dir)\n", "latest" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: The default TensorFlow format only saves the 5 most recent checkpoints.\n", "\n", "To test, reset the model, and load the latest checkpoint:" ] }, { "cell_type": "code", "execution_count": 107, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "32/32 - 0s - loss: 0.4808 - sparse_categorical_accuracy: 0.8720 - 96ms/epoch - 3ms/step\n", "Restored model, accuracy: 87.20%\n" ] } ], "source": [ "# Create a new model instance\n", "model = create_model()\n", "\n", "# Load the previously saved weights\n", "model.load_weights(latest)\n", "\n", "# Re-evaluate the model\n", "loss, acc = model.evaluate(test_images, test_labels, verbose=2)\n", "print(\"Restored model, accuracy: {:5.2f}%\".format(100 * acc))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What are these files?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above code stores the weights to a collection of [checkpoint](../../guide/checkpoint.ipynb)-formatted files that contain only the trained weights in a binary format. Checkpoints contain:\n", "* One or more shards that contain your model's weights.\n", "* An index file that indicates which weights are stored in which shard.\n", "\n", "If you are training a model on a single machine, you'll have one shard with the suffix: `.data-00000-of-00001`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Manually save weights\n", "\n", "To save weights manually, use `tf.keras.Model.save_weights`. By default, `tf.keras`—and the `Model.save_weights` method in particular—uses the TensorFlow [Checkpoint](../../guide/checkpoint.ipynb) format with a `.ckpt` extension. To save in the HDF5 format with a `.h5` extension, refer to the [Save and load models](https://www.tensorflow.org/guide/keras/save_and_serialize) guide." ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "32/32 - 0s - loss: 0.4808 - sparse_categorical_accuracy: 0.8720 - 95ms/epoch - 3ms/step\n", "Restored model, accuracy: 87.20%\n" ] } ], "source": [ "# Save the weights\n", "model.save_weights('./checkpoints/my_checkpoint')\n", "\n", "# Create a new model instance\n", "model = create_model()\n", "\n", "# Restore the weights\n", "model.load_weights('./checkpoints/my_checkpoint')\n", "\n", "# Evaluate the model\n", "loss, acc = model.evaluate(test_images, test_labels, verbose=2)\n", "print(\"Restored model, accuracy: {:5.2f}%\".format(100 * acc))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Save the entire model\n", "\n", "Call `tf.keras.Model.save` to save a model's architecture, weights, and training configuration in a single `file/folder`. This allows you to export a model so it can be used without access to the original Python code*. Since the optimizer-state is recovered, you can resume training from exactly where you left off.\n", "\n", "An entire model can be saved in two different file formats (`SavedModel` and `HDF5`). The TensorFlow `SavedModel` format is the default file format in TF2.x. However, models can be saved in `HDF5` format. More details on saving entire models in the two file formats is described below.\n", "\n", "Saving a fully-functional model is very useful—you can load them in TensorFlow.js ([Saved Model](https://www.tensorflow.org/js/tutorials/conversion/import_saved_model), [HDF5](https://www.tensorflow.org/js/tutorials/conversion/import_keras)) and then train and run them in web browsers, or convert them to run on mobile devices using TensorFlow Lite ([Saved Model](https://www.tensorflow.org/lite/models/convert/#convert_a_savedmodel_recommended_), [HDF5](https://www.tensorflow.org/lite/models/convert/#convert_a_keras_model_))\n", "\n", "\\*Custom objects (for example, subclassed models or layers) require special attention when saving and loading. Refer to the **Saving custom objects** section below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SavedModel format" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The SavedModel format is another way to serialize models. Models saved in this format can be restored using `tf.keras.models.load_model` and are compatible with TensorFlow Serving. The [SavedModel guide](../../guide/saved_model.ipynb) goes into detail about how to `serve/inspect` the SavedModel. The section below illustrates the steps to save and restore the model." ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/5\n", "32/32 [==============================] - 0s 1ms/step - loss: 1.1146 - sparse_categorical_accuracy: 0.6860\n", "Epoch 2/5\n", "32/32 [==============================] - 0s 2ms/step - loss: 0.4161 - sparse_categorical_accuracy: 0.8870\n", "Epoch 3/5\n", "32/32 [==============================] - 0s 2ms/step - loss: 0.2854 - sparse_categorical_accuracy: 0.9250\n", "Epoch 4/5\n", "32/32 [==============================] - 0s 2ms/step - loss: 0.1986 - sparse_categorical_accuracy: 0.9510\n", "Epoch 5/5\n", "32/32 [==============================] - 0s 2ms/step - loss: 0.1484 - sparse_categorical_accuracy: 0.9680\n", "/bin/bash: /home/obm/Prog/miniconda3/envs/qml/lib/libtinfo.so.6: no version information available (required by /bin/bash)\n", "INFO:tensorflow:Assets written to: saved_model/my_model/assets\n" ] } ], "source": [ "# Create and train a new model instance.\n", "model = create_model()\n", "model.fit(train_images, train_labels, epochs=5)\n", "\n", "# Save the entire model as a SavedModel.\n", "!mkdir -p saved_model\n", "model.save('saved_model/my_model') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The SavedModel format is a directory containing a protobuf binary and a TensorFlow checkpoint. Inspect the saved model directory:" ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/bin/bash: /home/obm/Prog/miniconda3/envs/qml/lib/libtinfo.so.6: no version information available (required by /bin/bash)\n", "my_model\n", "/bin/bash: /home/obm/Prog/miniconda3/envs/qml/lib/libtinfo.so.6: no version information available (required by /bin/bash)\n", "assets\tkeras_metadata.pb saved_model.pb variables\n" ] } ], "source": [ "# my_model directory\n", "!ls saved_model\n", "\n", "# Contains an assets folder, saved_model.pb, and variables folder.\n", "!ls saved_model/my_model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reload a fresh Keras model from the saved model:" ] }, { "cell_type": "code", "execution_count": 111, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_16\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " dense_47 (Dense) (None, 512) 401920 \n", " \n", " dropout_13 (Dropout) (None, 512) 0 \n", " \n", " dense_48 (Dense) (None, 10) 5130 \n", " \n", "=================================================================\n", "Total params: 407,050\n", "Trainable params: 407,050\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "new_model = tf.keras.models.load_model('saved_model/my_model')\n", "\n", "# Check its architecture\n", "new_model.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The restored model is compiled with the same arguments as the original model. Try running evaluate and predict with the loaded model:" ] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "32/32 - 0s - loss: 0.4154 - sparse_categorical_accuracy: 0.8690 - 97ms/epoch - 3ms/step\n", "Restored model, accuracy: 86.90%\n", "32/32 [==============================] - 0s 641us/step\n", "(1000, 10)\n" ] } ], "source": [ "# Evaluate the restored model\n", "loss, acc = new_model.evaluate(test_images, test_labels, verbose=2)\n", "print('Restored model, accuracy: {:5.2f}%'.format(100 * acc))\n", "\n", "print(new_model.predict(test_images).shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### HDF5 format\n", "\n", "Keras provides a basic save format using the [HDF5](https://en.wikipedia.org/wiki/Hierarchical_Data_Format) standard. " ] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/5\n", "32/32 [==============================] - 0s 1ms/step - loss: 1.1581 - sparse_categorical_accuracy: 0.6700\n", "Epoch 2/5\n", "32/32 [==============================] - 0s 2ms/step - loss: 0.4105 - sparse_categorical_accuracy: 0.8830\n", "Epoch 3/5\n", "32/32 [==============================] - 0s 2ms/step - loss: 0.2735 - sparse_categorical_accuracy: 0.9250\n", "Epoch 4/5\n", "32/32 [==============================] - 0s 2ms/step - loss: 0.1999 - sparse_categorical_accuracy: 0.9530\n", "Epoch 5/5\n", "32/32 [==============================] - 0s 2ms/step - loss: 0.1456 - sparse_categorical_accuracy: 0.9690\n" ] } ], "source": [ "# Create and train a new model instance.\n", "model = create_model()\n", "model.fit(train_images, train_labels, epochs=5)\n", "\n", "# Save the entire model to a HDF5 file.\n", "# The '.h5' extension indicates that the model should be saved to HDF5.\n", "model.save('my_model.h5') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, recreate the model from that file:" ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_17\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " dense_49 (Dense) (None, 512) 401920 \n", " \n", " dropout_14 (Dropout) (None, 512) 0 \n", " \n", " dense_50 (Dense) (None, 10) 5130 \n", " \n", "=================================================================\n", "Total params: 407,050\n", "Trainable params: 407,050\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "# Recreate the exact same model, including its weights and the optimizer\n", "new_model = tf.keras.models.load_model('my_model.h5')\n", "\n", "# Show the model architecture\n", "new_model.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check its accuracy:" ] }, { "cell_type": "code", "execution_count": 115, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "32/32 - 0s - loss: 0.4171 - sparse_categorical_accuracy: 0.8640 - 95ms/epoch - 3ms/step\n", "Restored model, accuracy: 86.40%\n" ] } ], "source": [ "loss, acc = new_model.evaluate(test_images, test_labels, verbose=2)\n", "print('Restored model, accuracy: {:5.2f}%'.format(100 * acc))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Keras saves models by inspecting their architectures. This technique saves everything:\n", "\n", "* The weight values\n", "* The model's architecture\n", "* The model's training configuration (what you pass to the `.compile()` method)\n", "* The optimizer and its state, if any (this enables you to restart training where you left off)\n", "\n", "Keras is not able to save the `v1.x` optimizers (from `tf.compat.v1.train`) since they aren't compatible with checkpoints. For v1.x optimizers, you need to re-compile the model after loading—losing the state of the optimizer.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Saving custom objects\n", "\n", "If you are using the SavedModel format, you can skip this section. The key difference between HDF5 and SavedModel is that HDF5 uses object configs to save the model architecture, while SavedModel saves the execution graph. Thus, SavedModels are able to save custom objects like subclassed models and custom layers without requiring the original code.\n", "\n", "To save custom objects to HDF5, you must do the following:\n", "\n", "1. Define a `get_config` method in your object, and optionally a `from_config` classmethod.\n", " * `get_config(self)` returns a JSON-serializable dictionary of parameters needed to recreate the object.\n", " * `from_config(cls, config)` uses the returned config from `get_config` to create a new object. By default, this function will use the config as initialization kwargs (`return cls(**config)`).\n", "2. Pass the object to the `custom_objects` argument when loading the model. The argument must be a dictionary mapping the string class name to the Python class. E.g. `tf.keras.models.load_model(path, custom_objects={'CustomLayer': CustomLayer})`\n", "\n", "Refer to the [Writing layers and models from scratch](https://www.tensorflow.org/guide/keras/custom_layers_and_models) tutorial for examples of custom objects and `get_config`.\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "ltPJCG6pAUoc" }, "source": [ "# TFP Probabilistic Layers: Regression\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "WRVR-tGTR31S" }, "source": [ "In this example we show how to fit regression models using TFP's \"probabilistic layers.\"" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "uiR4-VOt9NFX" }, "source": [ "### Dependencies & Prerequisites\n" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "colab": {}, "colab_type": "code", "id": "kZ0MdF1j8WJf" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:Detecting that an object or model or tf.train.Checkpoint is being deleted with unrestored values. See the following logs for the specific values in question. To silence these warnings, use `status.expect_partial()`. See https://www.tensorflow.org/api_docs/python/tf/train/Checkpoint#restorefor details about the status object returned by the restore function.\n", "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.iter\n", "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.beta_1\n", "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.beta_2\n", "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.decay\n", "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.learning_rate\n" ] } ], "source": [ "from pprint import pprint\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import seaborn as sns\n", "\n", "import tensorflow.compat.v2 as tf\n", "tf.enable_v2_behavior()\n", "\n", "import tensorflow_probability as tfp\n", "\n", "sns.reset_defaults()\n", "#sns.set_style('whitegrid')\n", "#sns.set_context('talk')\n", "sns.set_context(context='talk',font_scale=0.7)\n", "\n", "%matplotlib inline\n", "\n", "tfd = tfp.distributions" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "7nnwjUdVoWN2" }, "source": [ "### Make things Fast!" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "2CK9RaDcoYPG" }, "source": [ "Before we dive in, let's make sure we're using a GPU for this demo. \n", "\n", "To do this, select \"Runtime\" -> \"Change runtime type\" -> \"Hardware accelerator\" -> \"GPU\".\n", "\n", "The following snippet will verify that we have access to a GPU." ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "colab": { "height": 35 }, "colab_type": "code", "id": "qP_4Xr8vpA42", "outputId": "1dfdce37-0963-49fc-a044-f9c11b507309" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SUCCESS: Found GPU: /device:GPU:0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2022-10-20 12:31:01.971602: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-10-20 12:31:01.971821: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-10-20 12:31:01.972042: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-10-20 12:31:01.972243: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-10-20 12:31:01.972395: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-10-20 12:31:01.972519: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /device:GPU:0 with 5481 MB memory: -> device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:21:00.0, compute capability: 7.5\n", "2022-10-20 12:31:01.972645: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-10-20 12:31:01.972796: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-10-20 12:31:01.972939: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-10-20 12:31:01.973101: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-10-20 12:31:01.973248: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", "2022-10-20 12:31:01.973368: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /device:GPU:0 with 5481 MB memory: -> device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:21:00.0, compute capability: 7.5\n" ] } ], "source": [ "if tf.test.gpu_device_name() != '/device:GPU:0':\n", " print('WARNING: GPU device not found.')\n", "else:\n", " print('SUCCESS: Found GPU: {}'.format(tf.test.gpu_device_name()))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "FJRBc_S0ppfE" }, "source": [ "Note: if for some reason you cannot access a GPU, this colab will still work. (Training will just take longer.)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "xuqxMmryiduM" }, "source": [ "## Motivation" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "RtBLNF-tin2L" }, "source": [ "Wouldn't it be great if we could use TFP to specify a probabilistic model then simply minimize the negative log-likelihood, i.e.," ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "colab": {}, "colab_type": "code", "id": "3PFfNeJzifo7" }, "outputs": [], "source": [ "negloglik = lambda y, rv_y: -rv_y.log_prob(y)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "cN4IP8n_jIvT" }, "source": [ "Well not only is it possible, but this colab shows how! (In context of linear regression problems.)" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "cellView": "form", "colab": {}, "colab_type": "code", "id": "5zCEYpzu7bDX" }, "outputs": [], "source": [ "#@title Synthesize dataset.\n", "w0 = 0.125\n", "b0 = 5.\n", "x_range = [-20, 60]\n", "\n", "def load_dataset(n=150, n_tst=150):\n", " np.random.seed(43)\n", " def s(x):\n", " g = (x - x_range[0]) / (x_range[1] - x_range[0])\n", " return 3 * (0.25 + g**2.)\n", " x = (x_range[1] - x_range[0]) * np.random.rand(n) + x_range[0]\n", " eps = np.random.randn(n) * s(x)\n", " y = (w0 * x * (1. + np.sin(x)) + b0) + eps\n", " x = x[..., np.newaxis]\n", " x_tst = np.linspace(*x_range, num=n_tst).astype(np.float32)\n", " x_tst = x_tst[..., np.newaxis]\n", " return y, x, x_tst\n", "\n", "y, x, x_tst = load_dataset()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "N8Shtn_e99XC" }, "source": [ "### Case 1: No Uncertainty" ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "colab": { "height": 52 }, "colab_type": "code", "id": "RxKJ_RPI0K4N", "outputId": "24684193-e6b1-4139-e0d7-fe4d502e245c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.13626648\n", "5.1208844\n" ] } ], "source": [ "# Build model.\n", "model = tf.keras.Sequential([\n", " tf.keras.layers.Dense(1),\n", " tfp.layers.DistributionLambda(lambda t: tfd.Normal(loc=t, scale=1)),\n", "])\n", "\n", "# Do inference.\n", "model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.01), loss=negloglik)\n", "model.fit(x, y, epochs=1000, verbose=False);\n", "\n", "# Profit.\n", "[print(np.squeeze(w.numpy())) for w in model.weights];\n", "yhat = model(x_tst)\n", "assert isinstance(yhat, tfd.Distribution)" ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "cellView": "form", "colab": { "height": 147 }, "colab_type": "code", "id": "1AE9ElaKI6Er", "outputId": "5cb67b1e-5431-40ef-c010-19989702cbea" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#@title Figure 1: No uncertainty.\n", "w = np.squeeze(model.layers[-2].kernel.numpy())\n", "b = np.squeeze(model.layers[-2].bias.numpy())\n", "\n", "plt.figure(figsize=[6, 1.5]) # inches\n", "#plt.figure(figsize=[8, 5]) # inches\n", "plt.plot(x, y, 'b.', label='observed');\n", "plt.plot(x_tst, yhat.mean(),'r', label='mean', linewidth=4);\n", "plt.ylim(-0.,17);\n", "plt.yticks(np.linspace(0, 15, 4)[1:]);\n", "plt.xticks(np.linspace(*x_range, num=9));\n", "\n", "ax=plt.gca();\n", "ax.xaxis.set_ticks_position('bottom')\n", "ax.yaxis.set_ticks_position('left')\n", "ax.spines['left'].set_position(('data', 0))\n", "ax.spines['top'].set_visible(False)\n", "ax.spines['right'].set_visible(False)\n", "#ax.spines['left'].set_smart_bounds(True)\n", "#ax.spines['bottom'].set_smart_bounds(True)\n", "plt.legend(loc='center left', fancybox=True, framealpha=0., bbox_to_anchor=(1.05, 0.5))\n", "\n", "plt.savefig('/tmp/fig1.png', bbox_inches='tight', dpi=300)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "91kwRqs4O5Yv" }, "source": [ "### Case 2: Aleatoric Uncertainty" ] }, { "cell_type": "code", "execution_count": 122, "metadata": { "colab": { "height": 52 }, "colab_type": "code", "id": "TLZ97_V4PP-f", "outputId": "7a263b5d-c9de-4865-d374-fc9272738962" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.123 0.979]\n", "[5.208 9.202]\n" ] } ], "source": [ "# Build model.\n", "model = tf.keras.Sequential([\n", " tf.keras.layers.Dense(1 + 1),\n", " tfp.layers.DistributionLambda(\n", " lambda t: tfd.Normal(loc=t[..., :1],\n", " scale=1e-3 + tf.math.softplus(0.05 * t[...,1:]))),\n", "])\n", "\n", "# Do inference.\n", "model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.01), loss=negloglik)\n", "model.fit(x, y, epochs=1000, verbose=False);\n", "\n", "# Profit.\n", "[print(np.squeeze(w.numpy())) for w in model.weights];\n", "yhat = model(x_tst)\n", "assert isinstance(yhat, tfd.Distribution)" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "cellView": "form", "colab": { "height": 147 }, "colab_type": "code", "id": "JSSWw2-FPCiG", "outputId": "db8baddb-ae41-415b-a71e-545a60f4a546" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#@title Figure 2: Aleatoric Uncertainty\n", "plt.figure(figsize=[6, 1.5]) # inches\n", "plt.plot(x, y, 'b.', label='observed');\n", "\n", "m = yhat.mean()\n", "s = yhat.stddev()\n", "\n", "plt.plot(x_tst, m, 'r', linewidth=4, label='mean');\n", "plt.plot(x_tst, m + 2 * s, 'g', linewidth=2, label=r'mean + 2 stddev');\n", "plt.plot(x_tst, m - 2 * s, 'g', linewidth=2, label=r'mean - 2 stddev');\n", "\n", "plt.ylim(-0.,17);\n", "plt.yticks(np.linspace(0, 15, 4)[1:]);\n", "plt.xticks(np.linspace(*x_range, num=9));\n", "\n", "ax=plt.gca();\n", "ax.xaxis.set_ticks_position('bottom')\n", "ax.yaxis.set_ticks_position('left')\n", "ax.spines['left'].set_position(('data', 0))\n", "ax.spines['top'].set_visible(False)\n", "ax.spines['right'].set_visible(False)\n", "#ax.spines['left'].set_smart_bounds(True)\n", "#ax.spines['bottom'].set_smart_bounds(True)\n", "plt.legend(loc='center left', fancybox=True, framealpha=0., bbox_to_anchor=(1.05, 0.5))\n", "\n", "plt.savefig('/tmp/fig2.png', bbox_inches='tight', dpi=300)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "xEvTd7ZJYvDx" }, "source": [ "### Case 3: Epistemic Uncertainty" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "cellView": "both", "colab": {}, "colab_type": "code", "id": "VwzbWw3_CQ2z" }, "outputs": [], "source": [ "# Specify the surrogate posterior over `keras.layers.Dense` `kernel` and `bias`.\n", "def posterior_mean_field(kernel_size, bias_size=0, dtype=None):\n", " n = kernel_size + bias_size\n", " c = np.log(np.expm1(1.))\n", " return tf.keras.Sequential([\n", " tfp.layers.VariableLayer(2 * n, dtype=dtype),\n", " tfp.layers.DistributionLambda(lambda t: tfd.Independent(\n", " tfd.Normal(loc=t[..., :n],\n", " scale=1e-5 + tf.nn.softplus(c + t[..., n:])),\n", " reinterpreted_batch_ndims=1)),\n", " ])" ] }, { "cell_type": "code", "execution_count": 125, "metadata": { "cellView": "both", "colab": {}, "colab_type": "code", "id": "aAQhyK9Y_lm1" }, "outputs": [], "source": [ "# Specify the prior over `keras.layers.Dense` `kernel` and `bias`.\n", "def prior_trainable(kernel_size, bias_size=0, dtype=None):\n", " n = kernel_size + bias_size\n", " return tf.keras.Sequential([\n", " tfp.layers.VariableLayer(n, dtype=dtype),\n", " tfp.layers.DistributionLambda(lambda t: tfd.Independent(\n", " tfd.Normal(loc=t, scale=1),\n", " reinterpreted_batch_ndims=1)),\n", " ])" ] }, { "cell_type": "code", "execution_count": 126, "metadata": { "colab": { "height": 52 }, "colab_type": "code", "id": "XI7ZCFzSnrWN", "outputId": "d73eed57-94c1-466f-841b-421056c3ec73" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.138 5.156 -3.925 -2.398]\n", "[0.124 5.143]\n" ] } ], "source": [ "# Build model.\n", "model = tf.keras.Sequential([\n", " tfp.layers.DenseVariational(1, posterior_mean_field, prior_trainable, kl_weight=1/x.shape[0]),\n", " tfp.layers.DistributionLambda(lambda t: tfd.Normal(loc=t, scale=1)),\n", "])\n", "\n", "# Do inference.\n", "model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.01), loss=negloglik)\n", "model.fit(x, y, epochs=1000, verbose=False);\n", "\n", "# Profit.\n", "[print(np.squeeze(w.numpy())) for w in model.weights];\n", "yhat = model(x_tst)\n", "assert isinstance(yhat, tfd.Distribution)" ] }, { "cell_type": "code", "execution_count": 127, "metadata": { "cellView": "form", "colab": { "height": 147 }, "colab_type": "code", "id": "Y4Bypix9UvTO", "outputId": "5dbedd20-a914-4a61-beb3-2de1b266e137" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#@title Figure 3: Epistemic Uncertainty\n", "plt.figure(figsize=[6, 1.5]) # inches\n", "plt.clf();\n", "plt.plot(x, y, 'b.', label='observed');\n", "\n", "yhats = [model(x_tst) for _ in range(100)]\n", "avgm = np.zeros_like(x_tst[..., 0])\n", "for i, yhat in enumerate(yhats):\n", " m = np.squeeze(yhat.mean())\n", " s = np.squeeze(yhat.stddev())\n", " if i < 25:\n", " plt.plot(x_tst, m, 'r', label='ensemble means' if i == 0 else None, linewidth=0.5)\n", " avgm += m\n", "plt.plot(x_tst, avgm/len(yhats), 'r', label='overall mean', linewidth=4)\n", "\n", "plt.ylim(-0.,17);\n", "plt.yticks(np.linspace(0, 15, 4)[1:]);\n", "plt.xticks(np.linspace(*x_range, num=9));\n", "\n", "ax=plt.gca();\n", "ax.xaxis.set_ticks_position('bottom')\n", "ax.yaxis.set_ticks_position('left')\n", "ax.spines['left'].set_position(('data', 0))\n", "ax.spines['top'].set_visible(False)\n", "ax.spines['right'].set_visible(False)\n", "#ax.spines['left'].set_smart_bounds(True)\n", "#ax.spines['bottom'].set_smart_bounds(True)\n", "plt.legend(loc='center left', fancybox=True, framealpha=0., bbox_to_anchor=(1.05, 0.5))\n", "\n", "plt.savefig('/tmp/fig3.png', bbox_inches='tight', dpi=300)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "H_3At7s2fel0" }, "source": [ "### Case 4: Aleatoric & Epistemic Uncertainty" ] }, { "cell_type": "code", "execution_count": 128, "metadata": { "colab": { "height": 69 }, "colab_type": "code", "id": "GcRC3uwcft6l", "outputId": "157a96b8-7ffe-44fa-ba96-1cbd0b5abe52" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.136 2.172 5.199 3.019 -3.308 -0.584 -1.905 0.016]\n", "[0.146 2.18 5.153 3.064]\n" ] } ], "source": [ "# Build model.\n", "model = tf.keras.Sequential([\n", " tfp.layers.DenseVariational(1 + 1, posterior_mean_field, prior_trainable, kl_weight=1/x.shape[0]),\n", " tfp.layers.DistributionLambda(\n", " lambda t: tfd.Normal(loc=t[..., :1],\n", " scale=1e-3 + tf.math.softplus(0.01 * t[...,1:]))),\n", "])\n", "\n", "# Do inference.\n", "model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.01), loss=negloglik)\n", "model.fit(x, y, epochs=1000, verbose=False);\n", "\n", "# Profit.\n", "[print(np.squeeze(w.numpy())) for w in model.weights];\n", "yhat = model(x_tst)\n", "assert isinstance(yhat, tfd.Distribution)" ] }, { "cell_type": "code", "execution_count": 129, "metadata": { "cellView": "form", "colab": { "height": 147 }, "colab_type": "code", "id": "cWhfYYzcgFak", "outputId": "40b71fb8-7913-4f52-dad6-180af9df3c54" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#@title Figure 4: Both Aleatoric & Epistemic Uncertainty\n", "plt.figure(figsize=[6, 1.5]) # inches\n", "plt.plot(x, y, 'b.', label='observed');\n", "\n", "yhats = [model(x_tst) for _ in range(100)]\n", "avgm = np.zeros_like(x_tst[..., 0])\n", "for i, yhat in enumerate(yhats):\n", " m = np.squeeze(yhat.mean())\n", " s = np.squeeze(yhat.stddev())\n", " if i < 15:\n", " plt.plot(x_tst, m, 'r', label='ensemble means' if i == 0 else None, linewidth=1.)\n", " plt.plot(x_tst, m + 2 * s, 'g', linewidth=0.5, label='ensemble means + 2 ensemble stdev' if i == 0 else None);\n", " plt.plot(x_tst, m - 2 * s, 'g', linewidth=0.5, label='ensemble means - 2 ensemble stdev' if i == 0 else None);\n", " avgm += m\n", "plt.plot(x_tst, avgm/len(yhats), 'r', label='overall mean', linewidth=4)\n", "\n", "plt.ylim(-0.,17);\n", "plt.yticks(np.linspace(0, 15, 4)[1:]);\n", "plt.xticks(np.linspace(*x_range, num=9));\n", "\n", "ax=plt.gca();\n", "ax.xaxis.set_ticks_position('bottom')\n", "ax.yaxis.set_ticks_position('left')\n", "ax.spines['left'].set_position(('data', 0))\n", "ax.spines['top'].set_visible(False)\n", "ax.spines['right'].set_visible(False)\n", "#ax.spines['left'].set_smart_bounds(True)\n", "#ax.spines['bottom'].set_smart_bounds(True)\n", "plt.legend(loc='center left', fancybox=True, framealpha=0., bbox_to_anchor=(1.05, 0.5))\n", "\n", "plt.savefig('/tmp/fig4.png', bbox_inches='tight', dpi=300)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "qmgmcmMKzOH7" }, "source": [ "### Case 5: Functional Uncertainty" ] }, { "cell_type": "code", "execution_count": 130, "metadata": { "cellView": "form", "colab": {}, "colab_type": "code", "id": "qtXVxLRdzHBn" }, "outputs": [], "source": [ "#@title Custom PSD Kernel\n", "class RBFKernelFn(tf.keras.layers.Layer):\n", " def __init__(self, **kwargs):\n", " super(RBFKernelFn, self).__init__(**kwargs)\n", " dtype = kwargs.get('dtype', None)\n", "\n", " self._amplitude = self.add_variable(\n", " initializer=tf.constant_initializer(0),\n", " dtype=dtype,\n", " name='amplitude')\n", " \n", " self._length_scale = self.add_variable(\n", " initializer=tf.constant_initializer(0),\n", " dtype=dtype,\n", " name='length_scale')\n", "\n", " def call(self, x):\n", " # Never called -- this is just a layer so it can hold variables\n", " # in a way Keras understands.\n", " return x\n", "\n", " @property\n", " def kernel(self):\n", " return tfp.math.psd_kernels.ExponentiatedQuadratic(\n", " amplitude=tf.nn.softplus(0.1 * self._amplitude),\n", " length_scale=tf.nn.softplus(5. * self._length_scale)\n", " )" ] }, { "cell_type": "code", "execution_count": 131, "metadata": { "colab": { "height": 55 }, "colab_type": "code", "id": "_gJJtPMzzDyo", "outputId": "056de545-93f2-41c1-be48-37ef2215cc58" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From /home/obm/Prog/miniconda3/envs/qml/lib/python3.8/site-packages/tensorflow_probability/python/distributions/distribution.py:342: calling GaussianProcess.__init__ (from tensorflow_probability.python.distributions.gaussian_process) with jitter is deprecated and will be removed after 2021-05-10.\n", "Instructions for updating:\n", "`jitter` is deprecated; please use `marginal_fn` directly.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_142225/1709427333.py:7: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use the `layer.add_weight()` method instead.\n", " self._amplitude = self.add_variable(\n", "/tmp/ipykernel_142225/1709427333.py:12: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use the `layer.add_weight()` method instead.\n", " self._length_scale = self.add_variable(\n", "/home/obm/Prog/miniconda3/envs/qml/lib/python3.8/site-packages/tensorflow_probability/python/distributions/gaussian_process.py:402: UserWarning: Unable to detect statically whether the number of index_points is 1. As a result, defaulting to treating the marginal GP at `index_points` as a multivariate Gaussian. This makes some methods, like `cdf` unavailable.\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From /home/obm/Prog/miniconda3/envs/qml/lib/python3.8/site-packages/tensorflow_probability/python/internal/auto_composite_tensor.py:97: GaussianProcess.jitter (from tensorflow_probability.python.distributions.gaussian_process) is deprecated and will be removed after 2022-02-04.\n", "Instructions for updating:\n", "the `jitter` property of `tfd.GaussianProcess` is deprecated; use the `marginal_fn` property instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2022-10-20 12:31:45.260083: I tensorflow/core/util/cuda_solvers.cc:179] Creating GpuSolver handles for stream 0x55e5eec3da80\n" ] } ], "source": [ "# For numeric stability, set the default floating-point dtype to float64\n", "tf.keras.backend.set_floatx('float64')\n", "\n", "# Build model.\n", "num_inducing_points = 40\n", "model = tf.keras.Sequential([\n", " tf.keras.layers.InputLayer(input_shape=[1]),\n", " tf.keras.layers.Dense(1, kernel_initializer='ones', use_bias=False),\n", " tfp.layers.VariationalGaussianProcess(\n", " num_inducing_points=num_inducing_points,\n", " kernel_provider=RBFKernelFn(),\n", " event_shape=[1],\n", " inducing_index_points_initializer=tf.constant_initializer(\n", " np.linspace(*x_range, num=num_inducing_points,\n", " dtype=x.dtype)[..., np.newaxis]),\n", " unconstrained_observation_noise_variance_initializer=(\n", " tf.constant_initializer(np.array(0.54).astype(x.dtype))),\n", " ),\n", "])\n", "\n", "# Do inference.\n", "batch_size = 32\n", "loss = lambda y, rv_y: rv_y.variational_loss(\n", " y, kl_weight=np.array(batch_size, x.dtype) / x.shape[0])\n", "model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.01), loss=loss)\n", "model.fit(x, y, batch_size=batch_size, epochs=1000, verbose=False)\n", "\n", "# Profit.\n", "yhat = model(x_tst)\n", "assert isinstance(yhat, tfd.Distribution)" ] }, { "cell_type": "code", "execution_count": 132, "metadata": { "cellView": "form", "colab": { "height": 147 }, "colab_type": "code", "id": "Fp4qEWSRzc8m", "outputId": "ce1d241c-a2d9-43f8-952b-1c15a2e3ccb8" }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#@title Figure 5: Functional Uncertainty\n", "\n", "y, x, _ = load_dataset()\n", "\n", "plt.figure(figsize=[6, 1.5]) # inches\n", "plt.plot(x, y, 'b.', label='observed');\n", "\n", "num_samples = 7\n", "for i in range(num_samples):\n", " sample_ = yhat.sample().numpy()\n", " plt.plot(x_tst,\n", " sample_[..., 0].T,\n", " 'r',\n", " linewidth=0.9,\n", " label='ensemble means' if i == 0 else None);\n", "\n", "plt.ylim(-0.,17);\n", "plt.yticks(np.linspace(0, 15, 4)[1:]);\n", "plt.xticks(np.linspace(*x_range, num=9));\n", "\n", "ax=plt.gca();\n", "ax.xaxis.set_ticks_position('bottom')\n", "ax.yaxis.set_ticks_position('left')\n", "ax.spines['left'].set_position(('data', 0))\n", "ax.spines['top'].set_visible(False)\n", "ax.spines['right'].set_visible(False)\n", "#ax.spines['left'].set_smart_bounds(True)\n", "#ax.spines['bottom'].set_smart_bounds(True)\n", "plt.legend(loc='center left', fancybox=True, framealpha=0., bbox_to_anchor=(1.05, 0.5))\n", "\n", "plt.savefig('/tmp/fig5.png', bbox_inches='tight', dpi=300)" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "name": "regression.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.8.13" } }, "nbformat": 4, "nbformat_minor": 1 }