{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "DweYe9FcbMK_" }, "source": [ "##### Copyright 2019 The TensorFlow Authors.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "form", "execution": { "iopub.execute_input": "2024-08-16T07:14:22.923004Z", "iopub.status.busy": "2024-08-16T07:14:22.922763Z", "iopub.status.idle": "2024-08-16T07:14:22.926619Z", "shell.execute_reply": "2024-08-16T07:14:22.925899Z" }, "id": "AVV2e0XKbJeX" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "sUtoed20cRJJ" }, "source": [ "# Load CSV data" ] }, { "cell_type": "markdown", "metadata": { "id": "1ap_W4aQcgNT" }, "source": [ "\n", " \n", " \n", " \n", " \n", "
\n", " View on TensorFlow.org\n", " \n", " Run in Google Colab\n", " \n", " View source on GitHub\n", " \n", " Download notebook\n", "
" ] }, { "cell_type": "markdown", "metadata": { "id": "C-3Xbt0FfGfs" }, "source": [ "This tutorial provides examples of how to use CSV data with TensorFlow.\n", "\n", "There are two main parts to this:\n", "\n", "1. **Loading the data off disk**\n", "2. **Pre-processing it into a form suitable for training.**\n", "\n", "This tutorial focuses on the loading, and gives some quick examples of preprocessing. To learn more about the preprocessing aspect, check out 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.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "fgZ9gjmPfSnK" }, "source": [ "## Setup" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:22.930357Z", "iopub.status.busy": "2024-08-16T07:14:22.929977Z", "iopub.status.idle": "2024-08-16T07:14:25.332422Z", "shell.execute_reply": "2024-08-16T07:14:25.331708Z" }, "id": "baYFZMW_bJHh" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2024-08-16 07:14:23.551186: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "2024-08-16 07:14:23.572583: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", "2024-08-16 07:14:23.579075: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "# Make numpy values easier to read.\n", "np.set_printoptions(precision=3, suppress=True)\n", "\n", "import tensorflow as tf\n", "from tensorflow.keras import layers" ] }, { "cell_type": "markdown", "metadata": { "id": "1ZhJYbJxHNGJ" }, "source": [ "## In memory data" ] }, { "cell_type": "markdown", "metadata": { "id": "ny5TEgcmHjVx" }, "source": [ "For any small CSV dataset the simplest way to train a TensorFlow model on it is to load it into memory as a [pandas `DataFrame`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html) or a NumPy array.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "LgpBOuU8PGFf" }, "source": [ "A relatively simple example is the [abalone dataset](https://archive.ics.uci.edu/ml/datasets/abalone).\n", "\n", "* The dataset is small.\n", "* All the input features are limited-range floating point values.\n", "\n", "Here is how to download the data into a `DataFrame`:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:25.337422Z", "iopub.status.busy": "2024-08-16T07:14:25.336714Z", "iopub.status.idle": "2024-08-16T07:14:25.463882Z", "shell.execute_reply": "2024-08-16T07:14:25.463294Z" }, "id": "IZVExo9DKoNz" }, "outputs": [ { "data": { "text/html": [ "
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LengthDiameterHeightWhole weightShucked weightViscera weightShell weightAge
00.4350.3350.1100.3340.13550.07750.09657
10.5850.4500.1250.8740.35450.20750.22506
20.6550.5100.1601.0920.39600.28250.370014
30.5450.4250.1250.7680.29400.14950.260016
40.5450.4200.1300.8790.37400.16950.230013
\n", "
" ], "text/plain": [ " Length Diameter Height Whole weight Shucked weight Viscera weight \\\n", "0 0.435 0.335 0.110 0.334 0.1355 0.0775 \n", "1 0.585 0.450 0.125 0.874 0.3545 0.2075 \n", "2 0.655 0.510 0.160 1.092 0.3960 0.2825 \n", "3 0.545 0.425 0.125 0.768 0.2940 0.1495 \n", "4 0.545 0.420 0.130 0.879 0.3740 0.1695 \n", "\n", " Shell weight Age \n", "0 0.0965 7 \n", "1 0.2250 6 \n", "2 0.3700 14 \n", "3 0.2600 16 \n", "4 0.2300 13 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "abalone_train = pd.read_csv(\n", " \"https://storage.googleapis.com/download.tensorflow.org/data/abalone_train.csv\",\n", " names=[\"Length\", \"Diameter\", \"Height\", \"Whole weight\", \"Shucked weight\",\n", " \"Viscera weight\", \"Shell weight\", \"Age\"])\n", "\n", "abalone_train.head()" ] }, { "cell_type": "markdown", "metadata": { "id": "hP22mdyPQ1_t" }, "source": [ "The dataset contains a set of measurements of [abalone](https://en.wikipedia.org/wiki/Abalone), a type of sea snail.\n", "\n", "![an abalone shell](https://tensorflow.org/images/abalone_shell.jpg)\n", "\n", " [“Abalone shell”](https://www.flickr.com/photos/thenickster/16641048623/) (by [Nicki Dugan Pogue](https://www.flickr.com/photos/thenickster/), CC BY-SA 2.0)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "vlfGrk_9N-wf" }, "source": [ "The nominal task for this dataset is to predict the age from the other measurements, so separate the features and labels for training:\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:25.467531Z", "iopub.status.busy": "2024-08-16T07:14:25.466980Z", "iopub.status.idle": "2024-08-16T07:14:25.470708Z", "shell.execute_reply": "2024-08-16T07:14:25.470075Z" }, "id": "udOnDJOxNi7p" }, "outputs": [], "source": [ "abalone_features = abalone_train.copy()\n", "abalone_labels = abalone_features.pop('Age')" ] }, { "cell_type": "markdown", "metadata": { "id": "seK9n71-UBfT" }, "source": [ "For this dataset you will treat all features identically. Pack the features into a single NumPy array.:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:25.474039Z", "iopub.status.busy": "2024-08-16T07:14:25.473473Z", "iopub.status.idle": "2024-08-16T07:14:25.478966Z", "shell.execute_reply": "2024-08-16T07:14:25.478340Z" }, "id": "Dp3N5McbUMwb" }, "outputs": [ { "data": { "text/plain": [ "array([[0.435, 0.335, 0.11 , ..., 0.136, 0.077, 0.097],\n", " [0.585, 0.45 , 0.125, ..., 0.354, 0.207, 0.225],\n", " [0.655, 0.51 , 0.16 , ..., 0.396, 0.282, 0.37 ],\n", " ...,\n", " [0.53 , 0.42 , 0.13 , ..., 0.374, 0.167, 0.249],\n", " [0.395, 0.315, 0.105, ..., 0.118, 0.091, 0.119],\n", " [0.45 , 0.355, 0.12 , ..., 0.115, 0.067, 0.16 ]])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "abalone_features = np.array(abalone_features)\n", "abalone_features" ] }, { "cell_type": "markdown", "metadata": { "id": "1C1yFOxLOdxh" }, "source": [ "Next make a regression model predict the age. Since there is only a single input tensor, a `tf.keras.Sequential` model is sufficient here." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:25.482142Z", "iopub.status.busy": "2024-08-16T07:14:25.481769Z", "iopub.status.idle": "2024-08-16T07:14:27.696026Z", "shell.execute_reply": "2024-08-16T07:14:27.695274Z" }, "id": "d8zzNrZqOmfB" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1723792465.996743 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792466.000553 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792466.003815 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792466.007504 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792466.019051 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792466.022520 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792466.025442 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792466.028861 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792466.032242 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792466.035760 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792466.038693 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792466.042130 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792467.267390 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792467.269524 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792467.271597 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792467.273646 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792467.275655 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792467.277618 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792467.279574 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792467.281561 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792467.283463 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792467.285432 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792467.287397 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792467.289455 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792467.328726 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792467.330789 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792467.332799 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792467.334808 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792467.336761 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792467.338745 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792467.340723 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792467.342691 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792467.344638 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792467.347233 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792467.349687 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", "I0000 00:00:1723792467.352112 222190 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n" ] } ], "source": [ "abalone_model = tf.keras.Sequential([\n", " layers.Dense(64, activation='relu'),\n", " layers.Dense(1)\n", "])\n", "\n", "abalone_model.compile(loss = tf.keras.losses.MeanSquaredError(),\n", " optimizer = tf.keras.optimizers.Adam())" ] }, { "cell_type": "markdown", "metadata": { "id": "j6IWeP78O2wE" }, "source": [ "To train that model, pass the features and labels to `Model.fit`:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:27.699602Z", "iopub.status.busy": "2024-08-16T07:14:27.699342Z", "iopub.status.idle": "2024-08-16T07:14:30.736886Z", "shell.execute_reply": "2024-08-16T07:14:30.736185Z" }, "id": "uZdpCD92SN3Z" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/10\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1723792468.428118 222359 service.cc:146] XLA service 0x7f100c01c790 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1723792468.428162 222359 service.cc:154] StreamExecutor device (0): Tesla T4, Compute Capability 7.5\n", "I0000 00:00:1723792468.428167 222359 service.cc:154] StreamExecutor device (1): Tesla T4, Compute Capability 7.5\n", "I0000 00:00:1723792468.428169 222359 service.cc:154] StreamExecutor device (2): Tesla T4, Compute Capability 7.5\n", "I0000 00:00:1723792468.428172 222359 service.cc:154] StreamExecutor device (3): Tesla T4, Compute Capability 7.5\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "\u001b[1m 1/104\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:57\u001b[0m 1s/step - loss: 101.7966" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m 43/104\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 103.8427 " ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m 92/104\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 96.5295 " ] }, { "name": "stderr", "output_type": "stream", "text": [ "I0000 00:00:1723792468.891864 222359 device_compiler.h:188] Compiled cluster using XLA! 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loss: 6.8608 " ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m 98/104\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 6.8908" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m104/104\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 6.9013\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 9/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "\u001b[1m 1/104\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 43ms/step - loss: 5.2294" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m 49/104\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 6.8765 " ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m100/104\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 6.9685" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m104/104\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 6.9626\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 10/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "\u001b[1m 1/104\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 43ms/step - loss: 7.6975" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m 48/104\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 7.1371 " ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m 96/104\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 7.1340" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m104/104\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 7.0985\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "abalone_model.fit(abalone_features, abalone_labels, epochs=10)" ] }, { "cell_type": "markdown", "metadata": { "id": "GapLOj1OOTQH" }, "source": [ "You have just seen the most basic way to train a model using CSV data. Next, you will learn how to apply preprocessing to normalize numeric columns." ] }, { "cell_type": "markdown", "metadata": { "id": "B87Rd1SOUv02" }, "source": [ "## Basic preprocessing" ] }, { "cell_type": "markdown", "metadata": { "id": "yCrB2Jd-U0Vt" }, "source": [ "It's good practice to normalize the inputs to your model. The Keras preprocessing layers provide a convenient way to build this normalization into your model.\n", "\n", "The `tf.keras.layers.Normalization` layer precomputes the mean and variance of each column, and uses these to normalize the data.\n", "\n", "First, create the layer:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:30.740616Z", "iopub.status.busy": "2024-08-16T07:14:30.740371Z", "iopub.status.idle": "2024-08-16T07:14:30.744251Z", "shell.execute_reply": "2024-08-16T07:14:30.743644Z" }, "id": "H2WQpDU5VRk7" }, "outputs": [], "source": [ "normalize = layers.Normalization()" ] }, { "cell_type": "markdown", "metadata": { "id": "hGgEZE-7Vpt6" }, "source": [ "Then, use the `Normalization.adapt` method to adapt the normalization layer to your data.\n", "\n", "Note: Only use your training data with the `PreprocessingLayer.adapt` method. Do not use your validation or test data." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:30.747781Z", "iopub.status.busy": "2024-08-16T07:14:30.747272Z", "iopub.status.idle": "2024-08-16T07:14:30.763518Z", "shell.execute_reply": "2024-08-16T07:14:30.762920Z" }, "id": "2WgOPIiOVpLg" }, "outputs": [], "source": [ "normalize.adapt(abalone_features)" ] }, { "cell_type": "markdown", "metadata": { "id": "rE6vh0byV7cE" }, "source": [ "Then, use the normalization layer in your model:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:30.766746Z", "iopub.status.busy": "2024-08-16T07:14:30.766485Z", "iopub.status.idle": "2024-08-16T07:14:33.554875Z", "shell.execute_reply": "2024-08-16T07:14:33.554047Z" }, "id": "quPcZ9dTWA9A" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "\u001b[1m 1/104\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:39\u001b[0m 964ms/step - loss: 84.7515" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m 48/104\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 93.7718 " ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m 97/104\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 87.0590" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m104/104\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 86.1396" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m104/104\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - 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loss: 5.8023\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "norm_abalone_model = tf.keras.Sequential([\n", " normalize,\n", " layers.Dense(64, activation='relu'),\n", " layers.Dense(1)\n", "])\n", "\n", "norm_abalone_model.compile(loss = tf.keras.losses.MeanSquaredError(),\n", " optimizer = tf.keras.optimizers.Adam())\n", "\n", "norm_abalone_model.fit(abalone_features, abalone_labels, epochs=10)" ] }, { "cell_type": "markdown", "metadata": { "id": "Wuqj601Qw0Ml" }, "source": [ "## Mixed data types\n", "\n", "In the previous sections, you worked with a dataset where all the features were limited-range floating point values. But not all datasets are limited to a single data type.\n", "\n", "The \"Titanic\" dataset contains information about the passengers on the Titanic. The nominal task on this dataset is to predict who survived.\n", "\n", "![The Titanic](images/csv/Titanic.jpg)\n", "\n", "Image [from Wikimedia](https://commons.wikimedia.org/wiki/File:RMS_Titanic_3.jpg)\n", "\n", "The raw data can easily be loaded as a Pandas `DataFrame`, but is not immediately usable as input to a TensorFlow model.\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:33.558437Z", "iopub.status.busy": "2024-08-16T07:14:33.557831Z", "iopub.status.idle": "2024-08-16T07:14:33.694069Z", "shell.execute_reply": "2024-08-16T07:14:33.693422Z" }, "id": "GS-dBMpuYMnz" }, "outputs": [ { "data": { "text/html": [ "
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survivedsexagen_siblings_spousesparchfareclassdeckembark_townalone
00male22.0107.2500ThirdunknownSouthamptonn
11female38.01071.2833FirstCCherbourgn
21female26.0007.9250ThirdunknownSouthamptony
31female35.01053.1000FirstCSouthamptonn
40male28.0008.4583ThirdunknownQueenstowny
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" ], "text/plain": [ " survived sex age n_siblings_spouses parch fare class deck \\\n", "0 0 male 22.0 1 0 7.2500 Third unknown \n", "1 1 female 38.0 1 0 71.2833 First C \n", "2 1 female 26.0 0 0 7.9250 Third unknown \n", "3 1 female 35.0 1 0 53.1000 First C \n", "4 0 male 28.0 0 0 8.4583 Third unknown \n", "\n", " embark_town alone \n", "0 Southampton n \n", "1 Cherbourg n \n", "2 Southampton y \n", "3 Southampton n \n", "4 Queenstown y " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic = pd.read_csv(\"https://storage.googleapis.com/tf-datasets/titanic/train.csv\")\n", "titanic.head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:33.697732Z", "iopub.status.busy": "2024-08-16T07:14:33.697038Z", "iopub.status.idle": "2024-08-16T07:14:33.700926Z", "shell.execute_reply": "2024-08-16T07:14:33.700337Z" }, "id": "D8rCGIK1ZzKx" }, "outputs": [], "source": [ "titanic_features = titanic.copy()\n", "titanic_labels = titanic_features.pop('survived')" ] }, { "cell_type": "markdown", "metadata": { "id": "urHOwpCDYtcI" }, "source": [ "Because of the different data types and ranges, you can't simply stack the features into a NumPy array and pass it to a `tf.keras.Sequential` model. Each column needs to be handled individually.\n", "\n", "As one option, you could preprocess your data offline (using any tool you like) to convert categorical columns to numeric columns, then pass the processed output to your TensorFlow model. The disadvantage to that approach is that if you save and export your model the preprocessing is not saved with it. The Keras preprocessing layers avoid this problem because they're part of the model.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "Bta4Sx0Zau5v" }, "source": [ "In this example, you'll build a model that implements the preprocessing logic using [Keras functional API](https://www.tensorflow.org/guide/keras/functional). You could also do it by [subclassing](https://www.tensorflow.org/guide/keras/custom_layers_and_models).\n", "\n", "The functional API operates on \"symbolic\" tensors. Normal \"eager\" tensors have a value. In contrast these \"symbolic\" tensors do not. Instead they keep track of which operations are run on them, and build a representation of the calculation, that you can run later. Here's a quick example:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:33.704758Z", "iopub.status.busy": "2024-08-16T07:14:33.704088Z", "iopub.status.idle": "2024-08-16T07:14:33.709851Z", "shell.execute_reply": "2024-08-16T07:14:33.709287Z" }, "id": "730F16_97D-3" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create a symbolic input\n", "input = tf.keras.Input(shape=(), dtype=tf.float32)\n", "\n", "# Perform a calculation using the input\n", "result = 2*input + 1\n", "\n", "# the result doesn't have a value\n", "result" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:33.712975Z", "iopub.status.busy": "2024-08-16T07:14:33.712472Z", "iopub.status.idle": "2024-08-16T07:14:33.717209Z", "shell.execute_reply": "2024-08-16T07:14:33.716573Z" }, "id": "RtcNXWB18kMJ" }, "outputs": [], "source": [ "calc = tf.keras.Model(inputs=input, outputs=result)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:33.720553Z", "iopub.status.busy": "2024-08-16T07:14:33.719896Z", "iopub.status.idle": "2024-08-16T07:14:33.729572Z", "shell.execute_reply": "2024-08-16T07:14:33.728968Z" }, "id": "fUGQOUqZ8sa-" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[3.]\n", "[5.]\n" ] } ], "source": [ "print(calc(np.array([1])).numpy())\n", "print(calc(np.array([2])).numpy())" ] }, { "cell_type": "markdown", "metadata": { "id": "rNS9lT7f6_U2" }, "source": [ "To build the preprocessing model, start by building a set of symbolic `tf.keras.Input` objects, matching the names and data-types of the CSV columns." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:33.732848Z", "iopub.status.busy": "2024-08-16T07:14:33.732385Z", "iopub.status.idle": "2024-08-16T07:14:33.743171Z", "shell.execute_reply": "2024-08-16T07:14:33.742575Z" }, "id": "5WODe_1da3yw" }, "outputs": [ { "data": { "text/plain": [ "{'sex': ,\n", " 'age': ,\n", " 'n_siblings_spouses': ,\n", " 'parch': ,\n", " 'fare': ,\n", " 'class': ,\n", " 'deck': ,\n", " 'embark_town': ,\n", " 'alone': }" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "inputs = {}\n", "\n", "for name, column in titanic_features.items():\n", " dtype = column.dtype\n", " if dtype == object:\n", " dtype = tf.string\n", " else:\n", " dtype = tf.float32\n", "\n", " inputs[name] = tf.keras.Input(shape=(1,), name=name, dtype=dtype)\n", "\n", "inputs" ] }, { "cell_type": "markdown", "metadata": { "id": "aaheJFmymq8l" }, "source": [ "The first step in your preprocessing logic is to concatenate the numeric inputs together, and run them through a normalization layer:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:33.746398Z", "iopub.status.busy": "2024-08-16T07:14:33.746110Z", "iopub.status.idle": "2024-08-16T07:14:33.764973Z", "shell.execute_reply": "2024-08-16T07:14:33.764345Z" }, "id": "wPRC_E6rkp8D" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numeric_inputs = {name:input for name,input in inputs.items()\n", " if input.dtype==tf.float32}\n", "\n", "x = layers.Concatenate()(list(numeric_inputs.values()))\n", "norm = layers.Normalization()\n", "norm.adapt(np.array(titanic[numeric_inputs.keys()]))\n", "all_numeric_inputs = norm(x)\n", "\n", "all_numeric_inputs" ] }, { "cell_type": "markdown", "metadata": { "id": "-JoR45Uj712l" }, "source": [ "Collect all the symbolic preprocessing results, to concatenate them later:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:33.768312Z", "iopub.status.busy": "2024-08-16T07:14:33.767811Z", "iopub.status.idle": "2024-08-16T07:14:33.770861Z", "shell.execute_reply": "2024-08-16T07:14:33.770242Z" }, "id": "M7jIJw5XntdN" }, "outputs": [], "source": [ "preprocessed_inputs = [all_numeric_inputs]" ] }, { "cell_type": "markdown", "metadata": { "id": "r0Hryylyosfm" }, "source": [ "For the string inputs use the `tf.keras.layers.StringLookup` function to map from strings to integer indices in a vocabulary. Next, use `tf.keras.layers.CategoryEncoding` to convert the indexes into `float32` data appropriate for the model.\n", "\n", "The default settings for the `tf.keras.layers.CategoryEncoding` layer create a one-hot vector for each input. A `tf.keras.layers.Embedding` would also work. Check out 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 for more on this topic." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:33.774336Z", "iopub.status.busy": "2024-08-16T07:14:33.773767Z", "iopub.status.idle": "2024-08-16T07:14:33.808573Z", "shell.execute_reply": "2024-08-16T07:14:33.807888Z" }, "id": "79fi1Cgan2YV" }, "outputs": [], "source": [ "for name, input in inputs.items():\n", " if input.dtype == tf.float32:\n", " continue\n", " \n", " lookup = layers.StringLookup(vocabulary=np.unique(titanic_features[name]))\n", " one_hot = layers.CategoryEncoding(num_tokens=lookup.vocabulary_size())\n", "\n", " x = lookup(input)\n", " x = one_hot(x)\n", " preprocessed_inputs.append(x)" ] }, { "cell_type": "markdown", "metadata": { "id": "Wnhv0T7itnc7" }, "source": [ "With the collection of `inputs` and `preprocessed_inputs`, you can concatenate all the preprocessed inputs together, and build a model that handles the preprocessing:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:33.812022Z", "iopub.status.busy": "2024-08-16T07:14:33.811624Z", "iopub.status.idle": "2024-08-16T07:14:34.012933Z", "shell.execute_reply": "2024-08-16T07:14:34.012126Z" }, "id": "XJRzUTe8ukXc" }, "outputs": [ { "data": { "image/png": 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Xrl2/+eab119/vays7K233lq+fHnXrl2Li4t/3PrqnjVr1vz5z38OIeTk5Oy4NT8/f/Xq1fu9qF16/vnnZ8+e/cQTT+x0a4cOHU499dR169ZNnDhxPxcGAAAAAAB8X/sYC1133XVpaWkvv/xy586d69evf8wxx7z00kvp6enXXHNNNKBLly7VV5YMHjx42LBhIYShQ4dOnz790UcfjVafLFq0KITQp0+fYcOGDRo0qHXr1i1atLj55psrKyv3YZ4qb7zxxkknnXTQQQdlZ2fn5+dXVFRUbVq7du0VV1xxyCGHtGrV6tprr63alJubG03VokWLc845Z/HixVH/bmqLx+MPPfTQUUcd1bBhw06dOj377LN789FNmzZty5Yt7du3P/bYY3fcWlpaevfdd+90x4qKivz8/E6dOjVo0KBp06Z9+vR59913q7Z27tw5FotdeOGFv/71r9u1a9e0adOzzjpr6dKlIYTKyspHHnmkc+fODRs2bNOmzfnnn//pp5/uTakhhJycnIKCghNPPHFXA/r37x9C+Mtf/rKXEwIAAAAAAImyL7FQaWnpK6+8MnLkyAYNGlR1pqamXn311dOnT1+/fv1u9n3uuefOOeec6667Lh6Px+Pxjh07Rv0FBQUnnXRSUVHRtGnTJk6cuMcnp+1qnhBCWVnZBRdccOWVV65evfqtt95at25d9RRk4sSJvXv3Xrx48fTp0ydNmjRp0qSo/6mnnoqmWrBgQWZm5oABA7Zv37772u6+++6CgoIpU6b861//Gj9+/G233TZ16tQQwuTJk2Ox2K7eGxRlOV27dt1x01FHHdW0adOnnnrqq6++2nHrVVdddfPNNzdq1GjJkiUvvvhiYWHhGWec8fbbb1cf87e//e2MM874z//8zxYtWrz++us33nhjCCE3N/f6668/9NBDS0pKJk2aNH369G7dun344Ye7/4T30sknnxxCmD9/fmlp6Y8yIQAAAAAAUEv2JRb6+uuvKyoqqnGRCb4AACAASURBVCcxkaOPPrqiomKnkcYenXzyyaNGjTr44IN79ep18803P/TQQ/swSWTVqlWbN28+77zz0tPT27Vrd++993bp0qVq6/nnnz906NAmTZp069bt3HPPnTVrVo3dMzIyHnvssS+++GLBggW7qW3Lli35+fmPPvpot27d0tPTe/XqdfXVVxcUFOyxvJKSkhDCoYceuuOmFi1ajBo1qry8PHpnT3VFRUVRgnXTTTe1adOmX79+vXv3rqioGD16dPVhPXr0GDhw4KGHHnraaaeFED7++OMvv/wyWsY0evToVq1a9e3bt3v37lu2bPn973+/x1L3xiGHHBJCqKys/Pbbb3+UCQEAAAAAgFqyjw+R241YLLYPe1VfPXPyySd/++23GzZs2LcC2rdvf9ZZZ/Xs2fN3v/vdq6++unnz5upbs7Ozq9o/+9nPvvvuu6j9+eefn3feea1bt65Xr17Dhg0rKiqq3pO009oWLly4cePGfv36paampqSk1KtXb8yYMV9//XUI4Ze//GU8Hm/ZsuVOy4vH47spPlrW8+KLL37wwQfV+6t+7NChQ/UTqTHsiCOOiBoNGzYMIWzdunXu3LnREU877bToKXmzZ88OIXz55Ze7KQMAAAAAAKh79iUWOuKII1JSUmq8zieE8MUXX6SkpFQlE9VVvY9nV/YyTNrjPNFU06dPnzJlSqNGjW6//fajjjqqat3Prg4Uj8fPPvvsww47bO7cuVu3bq2oqEhLS6t6iNxOd4kq+eSTT7Zv315RUVFZWRmPx/dmpVTbtm1DCCtWrNjp1vT09Ly8vHg8fsstt+xxqh2lpqbWqLkqhfrss8/i1dTIk/ZZdCKxWKxNmzY/yoQAAAAAAEAt2ZdYqEmTJueee+748eO3bt1a1bl9+/Y//OEP55xzTrNmzcL/XIgTQli8eHFVOy0tbcd0Z+7cudXbbdq0adq06T7ME4nFYr169Ro3btz8+fPbtGkzceLE3Z/Rt99+W1xc/Lvf/e7www9PS0ubN29eeXn57ms75phjGjVq9Nprr+1+5h1Fj3erPmcNV111VceOHWu8+6dqxVLVJxA1dvqOouqid/+EEKJFQju6+uqrY7FYZmbmXlW/gyheOv7446N/LwAAAAAA4IC1jw+Re+yxx8rLywcMGPD555+Xl5cvWrTowgsv3LRp0+OPPx4N+MUvfjFlypRFixatX7/+3nvvnTdvXtW+hx9++Lx588rKyqpPOHfu3Pz8/PXr1xcWFj7wwAPXX3/9vs0TQpgzZ86IESPmz5+/ZcuWefPmLV269Mgjj9z96WRkZPzsZz+bOHHixo0bP/3006uuumqPtaWnp48aNequu+56/vnnN2zY8M9//nP8+PHjxo0LIUyePDkWi61Zs2anx7rwwgsbNGiwZMmS6muYqktJSbnvvvtqdHbo0GHIkCEhhAcffHD58uVvvvnmO++8k5KSctddd+3+1LKzs3NyckII48aN++ijj0pLS99///1rr732qaee2v2Oe+mVV14JIVx++eU/ymwAAAAAAEDt2cdYKCsr64MPPsjKyjrzzDPT09PPOOOMjIyMuXPnZmVlRQNGjRr185//vGfPnscee+zy5cv79+9fte91111XWVmZkZERi8WqnkSXk5PzwQcfZGdnX3DBBUOHDq2Khb7vPCGEk08++aSTThoyZEjz5s0vuOCC4cOHjxgxYvenk5aW9uKLL7788svNmzfv37//v//7vx900EFVW3dVW15e3n333Tdu3LhWrVqdfvrpCxYsGD58+B4/ulatWkUBz27WMA0YMOCUU06p0VlQUPDAAw9s3LixXbt2AwcOPO20095+++3evXvv8YjPPPPMQw891KxZs1NOOaVdu3bXX399x44dr7jiimhrtBjruOOO2+m+UcTVqFGj6MebbropFov16NEj+rGoqOgf//jHwQcfXCNIAwAAAAAADkCxqnfP5OXljR07NoQwZsyYvLy8/VlEnz59unbtev/99+/Pg+6l2qht5cqV2dnZ8Xh8yZIlLVu2/BFn3gdZWVkrV6789NNPO3To8H33HTp06KRJkx566KGqqGz3qq6xyP6/0uDAEb0ArOoODAcClyUAAABAnZea6AKSUUZGxoYNGxJdRQghLFmypKSk5M4779yHTCiE8Nxzzz333HM/dlEAAAAAAECtEAsltfbt2/tWOAAAAAAAJIkDIhaaMWNGokvYpQO5NgAAAAAAgL1XL9EFAAAAAAAAsD+IhQAAAAAAAJKCWAgAAAAAACApiIUAAAAAAACSglgIAAAAAAAgKYiFAAAAAAAAkoJYCAAAAAAAICmIhQAAAAAAAJKCWAgAAAAAACApiIUAAAAAAACSglgIAAAAAAAgKaTu2DVz5sy8vLz9Xgl138yZMxNdAgAAAAAAJK+dxEKFhYWFhYX7vxQAAAAAAABqj4fIAQAAAAAAJIVYPB5PdA0ASScWi4UQ3IE5oLgsAQAAAOo8q4UAAAAAAACSglgIAAAAAAAgKYiFAAAAAAAAkoJYCAAAAAAAICmIhQAAAAAAAJKCWAgAAAAAACAppCa6AIBkceWVV3766afVe7p27Ro1jjvuuD/+8Y+JKIpk57IEAAAASCpiIYD95Jhjjnnuueeq93z44YdR45JLLklAQRBCx44dd3VZXnzxxQkoCAAAAIDaFIvH44muASApFBcXt2vXbse7biwW++abb7KyshJSFUnOZQkAAACQVLxbCGA/ycrK+vnPf75j/y9+8Qt/fCdRsrKyTj311B37f/7zn7ssAQAAAOoesRDA/nPppZfuZSfsNy5LAAAAgOThIXIA+8+aNWvatGlTXl5e1ZOamvrtt9+2bt06gVWR5FyWAAAAAMnDaiGA/adly5Z9+/at3vNv//Zv/vhOYrVs2bJPnz7Ve84880yXJQAAAECdJBYC2K9qPJvLo7o4ELgsAQAAAJKEh8gB7FelpaWHHHLIpk2bQgjp6ekrVqxo0qRJoosi2ZWWlmZkZGzevDmE0KhRo5UrV7osAQAAAOokq4UA9qsmTZqce+65Ubt///7++M6BoEmTJv3794/a5513nssSAAAAoK4SCwHsb1VP6Bo8eHBiK4EqVVfjoEGDElsJAAAAALXnvx8il5eXN3bs2MRWQ1IZM2ZMXl7efjiQaxsAfrr22y8MAAAAkAysFgIAAAAAAEgKYiEAAAAAAICkIBYCAAAAAABICmIhAAAAAACApCAWAgAAAAAASApiIQAAAAAAgKQgFgIAAAAAAEgKYiEAAAAAAICkIBYCAAAAAABICmIhAAAAAACApCAWAgAAAAAASApiIQAAAAAAgKQgFgIAAAAAAEgKP8lY6P7774/H4/F4/I477kh0LQdWMQC1pH379g8//PDnn39eWlpaVlb2xRdfTJw48dRTT010XQAAAADA9/CTjIUSZdiwYVEClJubm+haAPafQYMGff7557/97W87derUuHHjgw46qEOHDldeeeWdd96Z6NJCSNzN2X8KAAAAAPzkiIUA2J0ePXpMnjy5UaNG8Xj8vvvuy8rKSk9P79y588iRI7/88stEVwcAAAAAfA91JBaaOnVq9JXtnJyc1157bcWKFWvWrJkyZUqLFi1qDBg+fPiMGTNWrVq1YcOGKVOmtGrVqmqSmTNnRmOaNWtWo6dx48Zz5syZMGFC1D9+/Piov1+/fnus7cEHH4z/l23btpWUlEyePDk7OzuE8OSTT0b9J5xwQtX4Tz75JB6Pr1q1qn79+lFPly5dXnjhheXLl2/btm3lypVTp0499thja5zX0UcfnZ+fv3r16oULF/7QTxOgmnvuuSc1NTWE8NRTT912220lJSWbN2/+/PPPn3rqqauvvrpq2EknnfS///f/XrFiRXl5+cqVK//P//k/J510UtXWPd6lQwidO3f+85//XFJSsnXr1sWLF999992NGzeONu3mRrr7m/Pe3D93U1JtHBcAAAAAEig10QX8yAoKCqral156aUpKyqBBg6oPePrpp6sPOOaYY7p3775t27bqY+LxeC2Vl5aWlpmZefnll5955pkdO3Z8/PHHR44cGULIycm55pprQgjZ2dnHHXdcCGHSpElRVf369Xv55ZerIqLWrVsPGjSof//+Z5xxxnvvvVc18z333HPRRReFENauXVtLxQNJqEmTJr169YraDz744K6GnXPOOS+99FJaWlr0Y+vWrS+88ML+/ftfcMEF06dPrz5yV3fpM8888//+3//boEGDaNNRRx11xx13rFy58g9/+EONY9W4ke6m+L28f+7xP45aOi4AAAAA7H91ZLVQlfnz5x955JGdOnX617/+FUK46KKLoi+5V5kzZ05WVlZWVtacOXNCCF26dLnssstqTFJZWbnjzD169PjVr34VtUeOHBmLxWKx2Ouvv77Hkm688cZocL169Q4++OC8vLwQQqtWrQYOHLhw4cIZM2aEEC677LLoj6EXX3xxtFf0JfSUlJQJEybUr1+/qKjo+OOPb9CgQffu3devX5+env7www9XP8pxxx3XrVu31NTU3f+xEuB7adu2bUpKSghhy5YtS5Ys2emY1NTUZ555Ji0tLR6PX3bZZU2aNLn00kvj8XhaWtrTTz9d4ya807t0SkpKQUFBdBv8/e9/f+ihh2ZkZOTk5ERjwm5vpLu6Oe/9/XM3/3HU6nEBAAAAYP+ra7FQXl7e119/vXDhwij1SUlJadOmTfUBY8eOLSkpKSkpGTduXNTTu3fvWi2pbdu2EyZMWLJkyZYtW9atWxf9VTGE0K5duxDCY489FkJo3rz5gAEDQggDBw4MIcycObOoqCiEcOKJJ2ZmZoYQOnToMH/+/K1bt7733nvRM+569OhR9bX6EMJtt902d+7cioqKWj0XgB2dcMIJ0Z323Xffff7558vKyqZOnTpr1qwQwmGHHdalS5fqg3d6l666182bN2/UqFErVqxYtWrVH//4xylTpkR77f5GulN7f//czX8ctXpcAAAAANj/6tpD5L744ouosWXLlqhR9RifSElJSdRYunRp1GjZsuWuZqt6INI+a9CgQWFhYfv27XfcFBU2ffr0JUuWtG/fPicn5/33349eMvTMM89EY1q3br2rmWOxWNVbN0IICxYs+IGlAuyopKSkoqIiJSWlYcOG7du3//rrr3ccU3WnqrrBhhCKi4trbI3s9C5dNWant7I93kh3au/vn7v6j6OWjrt169ZdDQAAAACA2lbXVguVl5dHjV29H6ht27ZRI/o2dwhhzZo1NfZNT08PIaSkpBx55JHV992Hdw717Nkz+pNiYWHhYYcdFovFLrnkkuoDKisrn3jiiRBCnz59brjhhhDC2rVrp02bFm1dtWpV1HjppZdiO6j+GqGq4gF+RKWlpYWFhVH7xhtv3OmY1atXR42qG2wIISsrK2pU3cciO71LV4055phjdpx/jzfSnd6c9+H+WWOe2j4uAAAAAOx/dS0W2qPRo0e3bdu2bdu2d9xxR9TzzjvvRI2q77YPGTKkadOmo0ePzsjIqL7vd999FzU6depU420Zu7J9+/aosXnz5rKysuzs7FtvvbXGmIKCgo0bN9arV+83v/lNCOFPf/pT1XfJP/7442XLloUQzj///Nzc3GbNmjVv3rx79+4PPPBAFCYB1LY777wzupXl5ubee++9mZmZDRs2PProo4cPH/7444+HED766KPoTnXaaaddeumljRs3Hjx48C9+8YsQwrJly+bNm7fHQ3z00UfRCs4TTjghPz8/IyOjZcuWw4YN++Uvfxn24ka605vzD79/Juq4AAAAAFB7ki4W6tmzZ3FxcXFxcY8ePUIIn3zySdW7K1544YWokZ+fv379+tGjR9fY96OPPor+SnjNNdeUl5fH4/HqjyG6++6749X8/e9/DyF8+OGHUdrUr1+/9evXFxUVRY+Jq27dunWTJ08OIcRisVDtCXIhhO3bt48YMaK8vLxevXrjx49ft27d2rVr58yZM2rUqBqRFUAtmT179tChQ7ds2RKLxW699daSkpLNmzcvWrTo6aefzs7ODiFs3749Nzd3+/btsVhsypQppaWlzz//fCwWq+rf4yEqKiqGDRsWJeI33XTTihUrVq9ePWHChIMPPjjsxY10pzfnH37/TNRxAQAAAKD2JF0slJubO3PmzLVr15aVlb3wwgt9+/atWprz5ptvjhw5cvHixZs2bfrss89uv/322bNnV9/3m2++GTZsWFFR0bZt2/bycJs3bz777LPffvvtsrKyDRs2vPHGGzuuFgohRN+4DyHMmjVr0aJF1Te9+uqr3bt3nzp16rJly8rLy5cvXz5r1qwbbrhh5MiR3/vkAfbJX/7yl86dOz/22GMLFy7ctGnTpk2bioqKnnvuuXHjxkUDXnnllVNOOWXatGmrV6/evn376tWr//a3v5166qmvvPLKXh7ijTfe6Nq16+TJk5cuXVpeXv7VV1+NGzfuueeeC3txI93VzfkH3j8TdVwAAAAAqD2xqlcj5OXljR07NrHV1J6pU6cOGjQohJCdnf3ll18mupz/IRaL9ejRo7CwMC0tbfDgwVWLluq8MWPG5OXl7YcD1e1rGwDqtv32CwMAAAAkg6RbLXSgGTZsWGVl5ezZs9PS0hYsWPDXv/410RUBAAAAAAB1k1jogLBp06YZM2acd955FRUVia4FAAAAAACom5IlFho8eHAsFovFYgfaE+SeffbZWCx20EEH9e3b96uvvkp0OQAAAAAAQJ2VLLEQAAAAAABAkhMLAQAAAAAAJAWxEAAAAAAAQFIQCwEAAAAAACQFsRAAAAAAAEBSEAsBAAAAAAAkBbEQAAAAAABAUhAL8ZP3z3/+84ILLnjxxRc3b96c6FoAAAAAAODAJRbiJ2/79u1/+9vfBg0alJGRccUVV7z22mvbt29PdFEAAAAAAHDAEQtRd5SWlk6ePPnss88+9NBDf/3rX8+aNSsejye6KAAAAAAAOFCkJroA+PGtWbNm/Pjx48ePz8zMvPDCC0tLSxNdEQAAAAAAJJ5YiIS5//77x44dW6uHWLp06WOPPVarhwAAAAAAgJ8KD5EDAAAAAABICmIhEuaWW26J/xi+/PLLXR0iMzPz2muvvfLKK/fneQEAAAAAwIFJLEQd1LJly5EjR7777rvFxcWPPvpoVlZWoisCAAAAAIDE28m7hXr16nX66afv90qo+2bOnFlYWFh78zdp0uT888+/7LLL+vbtm5q68/dmjRkzpvYKAEgqVe+Hc2ulltT2OwgBAAAgCe3kT+enn356Xl7efq+Eui8vL682YqHU1NQBAwZceuml/fv3b9So0R5r+NELAEhOVX+yd2ulloiFAAAA4Ee38xUV8BNy+OGHv/TSS4muAgAAAAAADnTeLQQAAAAAAJAUxEIAAAAAAABJQSwEAAAAAACQFLxbCAAAQl5e3tixYxNdBcAejBkzJi8vL9FVAADwE2a1EAAAAAAAQFIQCwEAAAAAACQFsRAAAAAAAEBSSK5YqE+fPrfcckuiqwgrV65s2rRp48aN16xZk+hafpAhQ4bEYrGHH3440YXUcQfIddu1a9fYf3nwwQejzmS+mJ27c6+9wvaDHc+9rKwsVs0HH3yQwPIOEAfI7TfUlQvPrw0AAABwINj3WGjZsmW5ubmZmZn169fPzMwcPnz4smXL9mbHc88997e//e0+H/dHn2f/u/POO0tLS0eMGNGyZcuop1+/ftGf4caNGxf1rFu3Lup55JFHEldp2Lx5c0FBwQknnFAjDIjcfvvtIYS77rrru+++S1CBtWLo0KGnn356LU3+071uIzfffHM8Hr/xxhujH5P5Ynbuzj3qqTPn3rhx43g8vmTJktorKbG/Ofykb781LrwD9qqLxOPxiy66KKrnl7/8ZVV/Xf21AQAAAH5a9jEWKi4u7tq16zfffPP666+XlZW99dZby5cv79q1a3Fx8Y9bX92zZs2aP//5zyGEnJycHbfm5+evXr16vxe1S88///zs2bOfeOKJnW7t0KHDqaeeum7duokTJ+7nwjgQJPPF7NyDc99B3T73H85vDvtsNxfegXbVRe65555p06bt2O/XBgAAADgQ7GMsdN1116Wlpb388sudO3euX7/+Mccc89JLL6Wnp19zzTXRgC5dulT/YvLgwYOHDRsWQhg6dOj06dMfffTR6DukixYtCiH06dNn2LBhgwYNat26dYsWLW6++ebKysp9mKfKG2+8cdJJJx100EHZ2dn5+fkVFRVVm9auXXvFFVcccsghrVq1uvbaa6s25ebmRlO1aNHinHPOWbx4cdS/m9ri8fhDDz101FFHNWzYsFOnTs8+++zefHTTpk3bsmVL+/btjz322B23lpaW3n333TvdsaKiIj8/v1OnTg0aNGjatGmfPn3efffdqq2dO3eOxWIXXnjhr3/963bt2jVt2vSss85aunRpCKGysvKRRx7p3Llzw4YN27Rpc/7553/66ad7U2oIIScnp6Cg4MQTT9zVgP79+4cQ/vKXv+zlhD85ffr0+dWvfrXTa+ZHv27Dri/d73vd7r68fbt0d5TMF7Nz35/nHn7A6Tv3A+cG/iP+5nAg335r4967mwvvQLvqQgj/8R//MWbMmG7duh1xxBE7bq3zvzYAAADAT0D8v4wZMybqGTNmTHy3NmzYkJKSct9999Xof/jhh1NSUtatWxePx48//vjf//73VZsGDRp01VVXRe1zzjnnuuuuq75j7969QwgPPPDAd999N3PmzJYtW/6v//W/ok3fa55IaWlpo0aNHn/88Y0bNy5ZsuTWW2/9+OOPqw5Ur169P/7xjxs2bHjvvfeaNm1aUFBQY/cVK1YMHz68U6dO5eXlu69t7NixnTp1eu+99zZu3Dhz5sxWrVo9//zz8Xg8+krv6tWrd/rpXX755SGEiy++uHrnv/3bv4UQjjrqqKZNm6alpf0/9u48IKpyf/z4cxgQREUTkUQgSEE0vWkgLiWYS1833KLcUhDNpSwzTU2vMiq5kNfK6kJ64UoZLnU1f8p170KWecMFUlFxocAlBRcWWRxgfn+c7z3fuSwzAwIDzPv11+Gc5zznc8bH85w5n3mec+XKFWV+lY8++kguExQUJIR47rnnbty4sX//fktLS5VKdeTIEXmr/KhIkqRvvvnm5s2bbm5uQohx48ZptdrXX39dCDFo0KA7d+4cOnRIpVLZ2NicPHmywvAqVFBQIAej+28hO3r0qBDCwsIiJyfHYD1KGzOypdUU3eMaUz4oKMjf319e1tNmarbdaitvutVot/rDq17T9fb2Fv+ZRE5mzo2Zc6/Lc3/80+fcjT93ZRK5xMREg5Ubf2mt2TuH+nz5rca1V1v1O4d62+pSU1NbtmxpZ2d39erVTp06CSEmTZqkW6BK/+O0/2ljJrlhAIB6q86uigAAAGisqjNa6Nq1ayUlJV5eXmXWd+rUqaSk5OrVq9Wos2fPngsXLmzVqpW/v/+iRYs2bNhQjUpkd+7cKSgoGDlypK2trZub2+rVq7t3765sHTVqVHBwcIsWLXx9fUeMGHHs2LEyuzs6Om7cuPHSpUspKSl6YissLAwPD//kk098fX1tbW39/f3nzJkTFRVlMLyMjAwhRLt27cpvsre3X7hwoUajkSff15WamhoTEyOEeO+995ycnIYMGTJw4MCSkpLly5frFuvdu3dgYGC7du38/PyEEGfOnLly5Yr8e+Tly5c7ODgMHjy4V69ehYWFH374ocFQjfHkk08KIUpLS2/cuFEjFdZDetpMDbZbobfpVqPdVhZetZtueebcmDn3Ojt3IUStnj7nXjcX8Bq/c6jPl98av/ZW1vDqW6vLzc0dPXp0dnZ2REREhUOFhHncNgAAAAAAUM9VcxI5PSRJqsZePj4+ynLPnj1v3LiRk5NTvQDc3d2HDh3ap0+f+fPn79u3T/mhtMzDw0NZfuKJJ5Rf154/f37kyJFt27a1sLCwsbEpKSlR3nZQYWwXLlx4+PDhkCFD5J/fWlhYhIaGXrt2TQjx2muvabVa5W3kZWh1fl5d3rx589q1a7dz586TJ0/qrlf+9PT01D2RMsWUpzA2NjZCiKKiIvnn3kIIPz8/ebqb48ePCyGuXLmiJwzoqqzNiBptt0Jv061Gu60svGo33fLMuTFz7pWp8XMX/xm2IurB6Zvzudeqatw51OfLb1WvveIx7hzqVavbvXt3SkpKUFDQxIkT9ZcEAAAAAAAmVJ200NNPP61SqcrPy3/p0iWVSlXh70OVifUrY+QjIYP1yFXFxcXFxsY2bdp06dKlHTt21B0/UeGBtFrtsGHD2rdvn5iYWFRUVFJSYmVlVVxcrGcXOZJff/21uLi4pKSktLRUq9Ua83tnFxcXIcQff/xR4VZbW1u1Wq3VahcvXmywqvIsLS3LxKw8Szp37pzuMLEyD4aqTT4RSZKcnJxqpMJ6SE/jrMF2K/Q23Wq028r2qnbTLc+cGzPnXmfnLmr59Dn3urmA1/idQ32+/Nb4tVdPw6tXrU4+x5iYGDmZdOnSJSHE119/LUmSErxpbxvy8/NPnTpV98cFAAAAAKBeqU5aqEWLFiNGjIiIiJB/VSorLi7+7LPPhg8f3rJlS1FuUIXyKmYhhJWVVfnHNImJibrLTk5OdnZ21ahHJkmSv79/WFhYcnKyk5NTdHS0/jO6ceNGenr6/Pnzn3rqKSsrq6SkJI1Goz+2zp07N23adP/+/fprLk+ep0W3zjKmTZvm5eVV5rGF8tNj5ROQF3R/klyhnj17ygvyT33LmzNnjiRJzs7ORkVfjvyQ6Nlnn5X/vcxNzbZbUcWmq7/dVhZetZtueY27MeuvrXGfu/4K6/jchaHT59wbxAW8xu8c6vPlt8avvfobXh23OlF5wwsODtZNI+m+W0ieO06Y+rbhwYMHPj4+7u7uixcvlrNWAAAARc0A8AAAIABJREFUAACYoWpOIrdx40aNRjN69Ojz589rNJqLFy+OHTs2Pz//008/lQv069cvNjb24sWL2dnZq1evTkpKUvZ96qmnkpKS8vLydCtMTEwMDw/Pzs5OSEhYt27dvHnzqlePEOLEiRMzZ85MTk4uLCxMSkq6fv16hw4d9J+Oo6PjE088ER0d/fDhw7Nnz06bNs1gbLa2tgsXLly5cuW2bdtycnJ+//33iIiIsLAwIcTWrVslScrKyqrwWGPHjrW2tk5LS9Mdw6RLfil3mZWenp7yu6PXr19/69atQ4cOff/99yqVauXKlfpPzcPDIyQkRAgRFhZ2+vTp3NzcX3755e23346MjNS/o5H27t0rhJDfhm2GarDdiqo3Xf3ttrLwqt10yzPnxsy519m5i1o+fc69zi7gNXvnUJ8vv1W99orHu3Og1VXVb7/9tm7dOi8vLx8fn7/85S+85QgAAAAAYG6qmRZydXU9efKkq6vrSy+9ZGtrO2DAAEdHx8TERFdXV7nAwoULX3jhhT59+jzzzDO3bt0KCAhQ9p07d25paamjo6MkScp8MiEhISdPnvTw8BgzZkxwcLDyfKeq9Qghevbs6e3tHRQU1Lp16zFjxsyYMWPmzJn6T8fKymrnzp179uxp3bp1QEDAlClTmjVrpmytLDa1Wr1mzZqwsDAHB4f+/funpKTMmDHD4Efn4OAgP6nR80vk0aNH9+3bt8zKqKiodevWPXz40M3NLTAw0M/P7+jRowMHDjR4xE2bNm3YsKFly5Z9+/Z1c3ObN2+el5fX5MmT5a3yr6q7detW4b7yg6qmTZvKf7733nuSJPXu3Vv+MzU19aeffmrVqlX5hISZqMF2K6redPW3Wz3hVa/plte4G7P+2hr3ueuvsO7PXf/pc+4N5QJes3cO9fnyW+PXXoMNry5bnTDU8PSob7cNp06dWrBggaur64svvrhp06Z79+6ZOiLos3btWnkgWmFhoTyzoiwrK0ur1Rr/o5a6tH37djnmjh07lv+zDo4IAAAAABWSlBnk1Wr1ihUrhBChoaFqtbougxg0aJCPj8/atWvr8qBGqo3Ybt++7eHhodVq09LSKnu/dJ1xdXW9ffv22bNnlbdSGy84ODgmJmbDhg3KMy/9lDYmq7OWpntc/W+Mr5L63G5FLYTn4+OjTFL04YcfLliwQDTqxmywtkZ87gYr5Nwb5bnn5eW1aNFCKZCYmGhwwrHyr7+qG/X58ltLsTWOhlfV2wbxnzY2dOhQf3//KgdaTk5OzurVqyvb2qRJEzc3t9TU1Mc/EGrD2rVrFy1aJC9v3rxZyapmZWXZ29vfvXvX5P81ytu+ffu4ceOEEB4eHleuXCnz5+PUPH369M2bNwshZs+erTuSrwYPgfqs7r+wAwAAoJGxNHUA5sjR0TEnJ8fUUQghRFpaWkZGxrJly6rxSFEIsWXLli1bttR0UKinKnzZeGNtzMbU1ljP3ZgKOfdqHKvG1fi5N2/evI6zOzBe42h41b5t+OGHH2rklXj6PXr0iJxQQzF16tS1a9deu3bN1IFUzfjx48ePH9/QDwEAAACgESAtZNbc3d15CIjGoWYbc8P6r1Hj0Tag0+fcTR0FzI5JGp6vr6+vr+/j15OXl/f5559XtpXRQg1FSUmJpaWlWq2eMmVKZWW8vb0XL17cr18/e3v7e/fu/fjjj6tXr1YGPStDary8vKZNmzZ16tSsrKzOnTt/++23L7/8shAiODh46tSpPXr0KCwsjI2NDQ0NXbJkyWuvvdaqVavTp0/PmTPn119/latav379/Pnz5WWNRnP79u2EhIQVK1Zcvny5fFRlhvK0adMmMzOzfLG//OUvCxYs0FPziRMnevXqJW+KiIiIiIgQQgwdOvTAgQPlRwsZ+VFMmzbtlVde6dGjh6Wl5aFDh9566627d+9W6d8FAAAAQANSL9JCR44cMXUIlarPscG06nnbqOfhAUC11efrW32OreHy8/OrkemSbt68WT4tZGFh4efnN2HChMDAwI0bN+rOdov6aevWrUFBQZMmTVqzZs2FCxfKFxg+fPju3butrKzkP9u2bTt27NiAgIAxY8bExcXplvzggw/kPFCZFIgyrM3Ozu6dd96ZPHmyvb29vKZfv3779u3z9PQsLCwsc1wrKytnZ+dJkya99NJLXl5eNfi2qjI1G7+j8R9FVFSUsjxhwgSVSiWniwAAAAA0ShamDgAAAACoa97e3uvXr09PT//Xv/41Y8aM1q1bmzoiGGXfvn0nTpywsLCoMIdnaWm5adMmKysrrVY7ceLEFi1aTJgwQavVWllZffHFF5aW//WTuG7duvn6+lpaWpbJtfz444+Ojo4TJ06U/7S3t3/vvfdatmwpp1JcXFxeeOEFedOCBQskSZIkycLColWrVnL+0sHBITAw0OCJZGVlSf/x3HPPyRNF3r9/PyYmRn/NvXv3fv311+VKZs+eLRc7cODA43wUycnJHTp06NKli5zNevnll8sUAAAAANCYkBYCAACAuXBzc1u0aNHFixdPnjw5f/789u3bmzoiVNmyZcuEEIGBgc8++2yZTT169HBychJC/PDDD9u2bcvLy9u+ffuxY8eEEO3bt+/evbtu4SVLliQmJpaUlJSpZPXq1Xfu3FESLVlZWRs2bMjJyVHeceXq6iovuLi4bN68OS0trbCw8MGDB8qwNjc3N+NPx9PT88CBA3Z2dvn5+cOHDz979myN1Fylj0KtVl+7du3ChQsnTpwQQqhUKnlfAAAAAI0SvwIDAABA49eqVauTJ096e3ubOhA8riNHjiQkJPj7+69atarMprZt28oLGRkZysr09PQyW2UpKSkV1p+WliaEKCgokP/8/fffS0tLhRDFxcXymiZNmgghrK2tExIS3N3dy9cgFzCGs7Pz4cOH27Zt++jRo7Fjx/788881VXOVPopLly7JC8rkeMYfCAAAAECDw2ghAAAANH62trbkhBoNecBQQEBAq1atdNdnZmbKCy4uLspKZXDPnTt3dAtrNJoKK1fSP2X+lCRJd32fPn3kzE1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"text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "preprocessed_inputs_cat = layers.Concatenate()(preprocessed_inputs)\n", "\n", "titanic_preprocessing = tf.keras.Model(inputs, preprocessed_inputs_cat)\n", "\n", "tf.keras.utils.plot_model(model = titanic_preprocessing , rankdir=\"LR\", dpi=72, show_shapes=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "PNHxrNW8vdda" }, "source": [ "This model just contains the input preprocessing. You can run it to see what it does to your data. Keras models don't automatically convert pandas `DataFrame`s because it's not clear if it should be converted to one tensor or to a dictionary of tensors. So, convert it to a dictionary of tensors:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:34.017926Z", "iopub.status.busy": "2024-08-16T07:14:34.017126Z", "iopub.status.idle": "2024-08-16T07:14:34.021771Z", "shell.execute_reply": "2024-08-16T07:14:34.021171Z" }, "id": "5YjdYyMEacwQ" }, "outputs": [], "source": [ "titanic_features_dict = {name: np.array(value) \n", " for name, value in titanic_features.items()}" ] }, { "cell_type": "markdown", "metadata": { "id": "0nKJYoPByada" }, "source": [ "Slice out the first training example and pass it to this preprocessing model, you see the numeric features and string one-hots all concatenated together:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:34.025228Z", "iopub.status.busy": "2024-08-16T07:14:34.024747Z", "iopub.status.idle": "2024-08-16T07:14:34.449309Z", "shell.execute_reply": "2024-08-16T07:14:34.448618Z" }, "id": "SjnmU8PSv8T3" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "features_dict = {name:values[:1] for name, values in titanic_features_dict.items()}\n", "titanic_preprocessing(features_dict)" ] }, { "cell_type": "markdown", "metadata": { "id": "qkBf4LvmzMDp" }, "source": [ "Now, build the model on top of this:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:34.453184Z", "iopub.status.busy": "2024-08-16T07:14:34.452526Z", "iopub.status.idle": "2024-08-16T07:14:35.101265Z", "shell.execute_reply": "2024-08-16T07:14:35.100528Z" }, "id": "coIPtGaCzUV7" }, "outputs": [], "source": [ "def titanic_model(preprocessing_head, inputs):\n", " body = tf.keras.Sequential([\n", " layers.Dense(64, activation='relu'),\n", " layers.Dense(1)\n", " ])\n", "\n", " preprocessed_inputs = preprocessing_head(inputs)\n", " result = body(preprocessed_inputs)\n", " model = tf.keras.Model(inputs, result)\n", "\n", " model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n", " optimizer=tf.keras.optimizers.Adam())\n", " return model\n", "\n", "titanic_model = titanic_model(titanic_preprocessing, inputs)" ] }, { "cell_type": "markdown", "metadata": { "id": "LK5uBQQF2KbZ" }, "source": [ "When you train the model, pass the dictionary of features as `x`, and the label as `y`." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:35.105340Z", "iopub.status.busy": "2024-08-16T07:14:35.105080Z", "iopub.status.idle": "2024-08-16T07:14:37.935422Z", "shell.execute_reply": "2024-08-16T07:14:37.934649Z" }, "id": "D1gVfwJ61ejz" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "\u001b[1m 1/20\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m32s\u001b[0m 2s/step - loss: 0.6629" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m18/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.6849" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 0.6818\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 2/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "\u001b[1m 1/20\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 53ms/step - loss: 0.6230" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m19/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - 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loss: 0.3964 " ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3980\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic_model.fit(x=titanic_features_dict, y=titanic_labels, epochs=10)" ] }, { "cell_type": "markdown", "metadata": { "id": "LxgJarZk3bfH" }, "source": [ "Since the preprocessing is part of the model, you can save the model and reload it somewhere else and get identical results:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:37.939425Z", "iopub.status.busy": "2024-08-16T07:14:37.938719Z", "iopub.status.idle": "2024-08-16T07:14:38.098585Z", "shell.execute_reply": "2024-08-16T07:14:38.097807Z" }, "id": "Ay-8ymNA2ZCh" }, "outputs": [], "source": [ "titanic_model.save('test.keras')\n", "reloaded = tf.keras.models.load_model('test.keras')" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:38.102283Z", "iopub.status.busy": "2024-08-16T07:14:38.101636Z", "iopub.status.idle": "2024-08-16T07:14:38.139127Z", "shell.execute_reply": "2024-08-16T07:14:38.138472Z" }, "id": "Qm6jMTpD20lK" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tf.Tensor([[-1.882]], shape=(1, 1), dtype=float32)\n", "tf.Tensor([[-1.882]], shape=(1, 1), dtype=float32)\n" ] } ], "source": [ "features_dict = {name:values[:1] for name, values in titanic_features_dict.items()}\n", "\n", "before = titanic_model(features_dict)\n", "after = reloaded(features_dict)\n", "assert (before-after)<1e-3\n", "print(before)\n", "print(after)" ] }, { "cell_type": "markdown", "metadata": { "id": "7VsPlxIRZpXf" }, "source": [ "## Using tf.data\n" ] }, { "cell_type": "markdown", "metadata": { "id": "NyVDCwGzR5HW" }, "source": [ "In the previous section you relied on the model's built-in data shuffling and batching while training the model.\n", "\n", "If you need more control over the input data pipeline or need to use data that doesn't easily fit into memory: use `tf.data`.\n", "\n", "For more examples, refer to the [`tf.data`: Build TensorFlow input pipelines](../../guide/data.ipynb) guide." ] }, { "cell_type": "markdown", "metadata": { "id": "gP5Y1jM2Sor0" }, "source": [ "### On in memory data\n", "\n", "As a first example of applying `tf.data` to CSV data, consider the following code to manually slice up the dictionary of features from the previous section. For each index, it takes that index for each feature:\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:38.142882Z", "iopub.status.busy": "2024-08-16T07:14:38.142258Z", "iopub.status.idle": "2024-08-16T07:14:38.145946Z", "shell.execute_reply": "2024-08-16T07:14:38.145362Z" }, "id": "i8wE-MVuVu7_" }, "outputs": [], "source": [ "import itertools\n", "\n", "def slices(features):\n", " for i in itertools.count():\n", " # For each feature take index `i`\n", " example = {name:values[i] for name, values in features.items()}\n", " yield example" ] }, { "cell_type": "markdown", "metadata": { "id": "cQ3RTbS9YEal" }, "source": [ "Run this and print the first example:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:38.149340Z", "iopub.status.busy": "2024-08-16T07:14:38.148750Z", "iopub.status.idle": "2024-08-16T07:14:38.152541Z", "shell.execute_reply": "2024-08-16T07:14:38.151930Z" }, "id": "Wwq8XK88WwFk" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sex : male\n", "age : 22.0\n", "n_siblings_spouses : 1\n", "parch : 0\n", "fare : 7.25\n", "class : Third\n", "deck : unknown\n", "embark_town : Southampton\n", "alone : n\n" ] } ], "source": [ "for example in slices(titanic_features_dict):\n", " for name, value in example.items():\n", " print(f\"{name:19s}: {value}\")\n", " break" ] }, { "cell_type": "markdown", "metadata": { "id": "vvp8Dct6YOIE" }, "source": [ "The most basic `tf.data.Dataset` in memory data loader is the `Dataset.from_tensor_slices` constructor. This returns a `tf.data.Dataset` that implements a generalized version of the above `slices` function, in TensorFlow." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:38.155828Z", "iopub.status.busy": "2024-08-16T07:14:38.155254Z", "iopub.status.idle": "2024-08-16T07:14:38.162752Z", "shell.execute_reply": "2024-08-16T07:14:38.162153Z" }, "id": "2gEJthslYxeV" }, "outputs": [], "source": [ "features_ds = tf.data.Dataset.from_tensor_slices(titanic_features_dict)" ] }, { "cell_type": "markdown", "metadata": { "id": "-ZC0rTpMZMZK" }, "source": [ "You can iterate over a `tf.data.Dataset` like any other python iterable:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:38.166068Z", "iopub.status.busy": "2024-08-16T07:14:38.165522Z", "iopub.status.idle": "2024-08-16T07:14:38.178245Z", "shell.execute_reply": "2024-08-16T07:14:38.177569Z" }, "id": "gOHbiefaY4ag" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sex : b'male'\n", "age : 22.0\n", "n_siblings_spouses : 1\n", "parch : 0\n", "fare : 7.25\n", "class : b'Third'\n", "deck : b'unknown'\n", "embark_town : b'Southampton'\n", "alone : b'n'\n" ] } ], "source": [ "for example in features_ds:\n", " for name, value in example.items():\n", " print(f\"{name:19s}: {value}\")\n", " break" ] }, { "cell_type": "markdown", "metadata": { "id": "uwcFoVJWZY5F" }, "source": [ "The `from_tensor_slices` function can handle any structure of nested dictionaries or tuples. The following code makes a dataset of `(features_dict, labels)` pairs:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:38.181682Z", "iopub.status.busy": "2024-08-16T07:14:38.181212Z", "iopub.status.idle": "2024-08-16T07:14:38.190933Z", "shell.execute_reply": "2024-08-16T07:14:38.190264Z" }, "id": "xIHGBy76Zcrx" }, "outputs": [], "source": [ "titanic_ds = tf.data.Dataset.from_tensor_slices((titanic_features_dict, titanic_labels))" ] }, { "cell_type": "markdown", "metadata": { "id": "gQwxitt8c2GK" }, "source": [ "To train a model using this `Dataset`, you'll need to at least `shuffle` and `batch` the data." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:38.194281Z", "iopub.status.busy": "2024-08-16T07:14:38.193623Z", "iopub.status.idle": "2024-08-16T07:14:38.201949Z", "shell.execute_reply": "2024-08-16T07:14:38.201387Z" }, "id": "SbJcbldhddeC" }, "outputs": [], "source": [ "titanic_batches = titanic_ds.shuffle(len(titanic_labels)).batch(32)" ] }, { "cell_type": "markdown", "metadata": { "id": "-4FRqhRFuoJx" }, "source": [ "Instead of passing `features` and `labels` to `Model.fit`, you pass the dataset:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:38.205374Z", "iopub.status.busy": "2024-08-16T07:14:38.204780Z", "iopub.status.idle": "2024-08-16T07:14:38.962086Z", "shell.execute_reply": "2024-08-16T07:14:38.961373Z" }, "id": "8yXkNPumdBtB" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/5\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "\u001b[1m 1/20\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m8s\u001b[0m 421ms/step - 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loss: 0.3606" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m19/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3672" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.3703\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic_model.fit(titanic_batches, epochs=5)" ] }, { "cell_type": "markdown", "metadata": { "id": "qXuibiv9exT7" }, "source": [ "### From a single file\n", "\n", "So far this tutorial has worked with in-memory data. `tf.data` is a highly scalable toolkit for building data pipelines, and provides a few functions for loading CSV files. " ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:38.965738Z", "iopub.status.busy": "2024-08-16T07:14:38.965226Z", "iopub.status.idle": "2024-08-16T07:14:39.004624Z", "shell.execute_reply": "2024-08-16T07:14:39.004035Z" }, "id": "Ncf5t6tgL5ZI" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://storage.googleapis.com/tf-datasets/titanic/train.csv\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "\u001b[1m 0/30874\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 0s/step" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m30874/30874\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n" ] } ], "source": [ "titanic_file_path = tf.keras.utils.get_file(\"train.csv\", \"https://storage.googleapis.com/tf-datasets/titanic/train.csv\")" ] }, { "cell_type": "markdown", "metadata": { "id": "t4N-plO4tDXd" }, "source": [ "Now read the CSV data from the file and create a `tf.data.Dataset`.\n", "\n", "(For the full documentation, see `tf.data.experimental.make_csv_dataset`)\n" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:39.008129Z", "iopub.status.busy": "2024-08-16T07:14:39.007517Z", "iopub.status.idle": "2024-08-16T07:14:39.062521Z", "shell.execute_reply": "2024-08-16T07:14:39.061908Z" }, "id": "yIbUscB9sqha" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/data/experimental/ops/readers.py:572: ignore_errors (from tensorflow.python.data.experimental.ops.error_ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use `tf.data.Dataset.ignore_errors` instead.\n" ] } ], "source": [ "titanic_csv_ds = tf.data.experimental.make_csv_dataset(\n", " titanic_file_path,\n", " batch_size=5, # Artificially small to make examples easier to show.\n", " label_name='survived',\n", " num_epochs=1,\n", " ignore_errors=True,)" ] }, { "cell_type": "markdown", "metadata": { "id": "Sf3v3BKgy4AG" }, "source": [ "This function includes many convenient features, so the data is easy to work with. This includes:\n", "\n", "* Using the column headers as dictionary keys.\n", "* Automatically determining the type of each column.\n", "\n", "Caution: Make sure to set the `num_epochs` argument in `tf.data.experimental.make_csv_dataset`, otherwise the default behavior for `tf.data.Dataset` is to loop endlessly." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:39.065904Z", "iopub.status.busy": "2024-08-16T07:14:39.065498Z", "iopub.status.idle": "2024-08-16T07:14:39.126772Z", "shell.execute_reply": "2024-08-16T07:14:39.126029Z" }, "id": "v4oMO9MIxgTG" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sex : [b'male' b'male' b'male' b'male' b'female']\n", "age : [28. 28. 48. 61. 28.]\n", "n_siblings_spouses : [0 0 0 0 1]\n", "parch : [0 0 0 0 0]\n", "fare : [ 7.75 7.896 26.55 32.321 51.862]\n", "class : [b'Third' b'Third' b'First' b'First' b'First']\n", "deck : [b'unknown' b'unknown' b'E' b'D' b'D']\n", "embark_town : [b'Queenstown' b'Southampton' b'Southampton' b'Southampton' b'Southampton']\n", "alone : [b'y' b'y' b'y' b'y' b'n']\n", "\n", "label : [0 0 1 0 1]\n" ] } ], "source": [ "for batch, label in titanic_csv_ds.take(1):\n", " for key, value in batch.items():\n", " print(f\"{key:20s}: {value}\")\n", " print()\n", " print(f\"{'label':20s}: {label}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "k-TgA6o2Ja6U" }, "source": [ "Note: If you run the above cell twice it will produce different results. The default settings for `tf.data.experimental.make_csv_dataset` include `shuffle_buffer_size=1000`, which is more than sufficient for this small dataset, but may not be for a real-world dataset." ] }, { "cell_type": "markdown", "metadata": { "id": "d6uviU_KCCWD" }, "source": [ "It can also decompress the data on the fly. Here's a gzipped CSV file containing the [metro interstate traffic dataset](https://archive.ics.uci.edu/ml/datasets/Metro+Interstate+Traffic+Volume).\n", "\n", "![A traffic jam.](images/csv/traffic.jpg)\n", "\n", "Image [from Wikimedia](https://commons.wikimedia.org/wiki/File:Trafficjam.jpg)\n" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:39.130518Z", "iopub.status.busy": "2024-08-16T07:14:39.129827Z", "iopub.status.idle": "2024-08-16T07:14:41.458469Z", "shell.execute_reply": "2024-08-16T07:14:41.457725Z" }, "id": "kT7oZI2E46Q8" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://archive.ics.uci.edu/ml/machine-learning-databases/00492/Metro_Interstate_Traffic_Volume.csv.gz\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 0/Unknown \u001b[1m0s\u001b[0m 0s/step" ] }, { "name": "stdout", "output_type": "stream", "text": [ 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"output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 393216/Unknown \u001b[1m2s\u001b[0m 5us/step" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 401408/Unknown \u001b[1m2s\u001b[0m 5us/step" ] } ], "source": [ "traffic_volume_csv_gz = tf.keras.utils.get_file(\n", " 'Metro_Interstate_Traffic_Volume.csv.gz', \n", " \"https://archive.ics.uci.edu/ml/machine-learning-databases/00492/Metro_Interstate_Traffic_Volume.csv.gz\",\n", " cache_dir='.', cache_subdir='traffic')" ] }, { "cell_type": "markdown", "metadata": { "id": "F-IOsFHbCw0i" }, "source": [ "Set the `compression_type` argument to read directly from the compressed file:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:41.462348Z", "iopub.status.busy": "2024-08-16T07:14:41.461764Z", "iopub.status.idle": "2024-08-16T07:14:41.708853Z", "shell.execute_reply": "2024-08-16T07:14:41.708127Z" }, "id": "ar0MPEVJ5NeA" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "holiday : [b'None' b'None' b'None' b'None' b'None']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "temp : [249.51 288.47 254.48 275.54 274.24]\n", "rain_1h : [0. 0. 0. 0. 0.]\n", "snow_1h : [0. 0. 0. 0. 0.]\n", "clouds_all : [88 44 1 90 20]\n", "weather_main : [b'Clouds' b'Clouds' b'Clear' b'Mist' b'Clouds']\n", "weather_description : [b'overcast clouds' b'scattered clouds' b'sky is clear' b'mist'\n", " b'few clouds']\n", "date_time : [b'2013-02-03 03:00:00' b'2013-08-07 08:00:00' b'2013-01-01 23:00:00'\n", " b'2013-01-12 11:00:00' b'2012-10-30 01:00:00']\n", "\n", "label : [ 338 5990 845 4017 393]\n" ] } ], "source": [ "traffic_volume_csv_gz_ds = tf.data.experimental.make_csv_dataset(\n", " traffic_volume_csv_gz,\n", " batch_size=256,\n", " label_name='traffic_volume',\n", " num_epochs=1,\n", " compression_type=\"GZIP\")\n", "\n", "for batch, label in traffic_volume_csv_gz_ds.take(1):\n", " for key, value in batch.items():\n", " print(f\"{key:20s}: {value[:5]}\")\n", " print()\n", " print(f\"{'label':20s}: {label[:5]}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "p12Y6tGq8D6M" }, "source": [ "Note: If you need to parse those date-time strings in the `tf.data` pipeline, you can use `tfa.text.parse_time`." ] }, { "cell_type": "markdown", "metadata": { "id": "EtrAXzYGP3l0" }, "source": [ "### Caching" ] }, { "cell_type": "markdown", "metadata": { "id": "fN2dL_LRP83r" }, "source": [ "There is some overhead to parsing the CSV data. For small models this can be the bottleneck in training.\n", "\n", "Depending on your use case, it may be a good idea to use `Dataset.cache` or `tf.data.Dataset.snapshot`, so that the CSV data is only parsed on the first epoch.\n", "\n", "The main difference between the `cache` and `snapshot` methods is that `cache` files can only be used by the TensorFlow process that created them, but `snapshot` files can be read by other processes.\n", "\n", "For example, iterating over the `traffic_volume_csv_gz_ds` 20 times may take around 15 seconds without caching, or about two seconds with caching." ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:41.713008Z", "iopub.status.busy": "2024-08-16T07:14:41.712385Z", "iopub.status.idle": "2024-08-16T07:14:51.127767Z", "shell.execute_reply": "2024-08-16T07:14:51.126968Z" }, "id": "Qk38Sw4MO4eh" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ ".." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ ".." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ ".." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ ".." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ ".." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ ".." ] }, { "name": "stdout", "output_type": "stream", "text": [ "..." ] }, { "name": "stdout", "output_type": "stream", "text": [ ".." ] }, { "name": "stdout", "output_type": "stream", "text": [ "..." ] }, { "name": "stdout", "output_type": "stream", "text": [ "..." ] }, { "name": "stdout", "output_type": "stream", "text": [ "..." ] }, { "name": "stdout", "output_type": "stream", "text": [ ".." ] }, { "name": "stdout", "output_type": "stream", "text": [ "..." ] }, { "name": "stdout", "output_type": "stream", "text": [ "..." ] }, { "name": "stdout", "output_type": "stream", "text": [ "..." ] }, { "name": "stdout", "output_type": "stream", "text": [ "..." ] }, { "name": "stdout", "output_type": "stream", "text": [ "..." ] }, { "name": "stdout", "output_type": "stream", "text": [ ".." ] }, { "name": "stdout", "output_type": "stream", "text": [ "..." ] }, { "name": "stdout", "output_type": "stream", "text": [ "..." ] }, { "name": "stdout", "output_type": "stream", "text": [ "..." ] }, { "name": "stdout", "output_type": "stream", "text": [ ".." ] }, { "name": "stdout", "output_type": "stream", "text": [ "..." ] }, { "name": "stdout", "output_type": "stream", "text": [ "..." ] }, { "name": "stdout", "output_type": "stream", "text": [ ".." ] }, { "name": "stdout", "output_type": "stream", "text": [ "..." ] }, { "name": "stdout", "output_type": "stream", "text": [ ".." ] }, { "name": "stdout", "output_type": "stream", "text": [ "..." ] }, { "name": "stdout", "output_type": "stream", "text": [ "..." ] }, { "name": "stdout", "output_type": "stream", "text": [ "..." ] }, { "name": "stdout", "output_type": "stream", "text": [ "..." ] }, { "name": "stdout", "output_type": "stream", "text": [ "..\n", "CPU times: user 12.9 s, sys: 2.09 s, total: 15 s\n", "Wall time: 9.41 s\n" ] } ], "source": [ "%%time\n", "for i, (batch, label) in enumerate(traffic_volume_csv_gz_ds.repeat(20)):\n", " if i % 40 == 0:\n", " print('.', end='')\n", "print()" ] }, { "cell_type": "markdown", "metadata": { "id": "pN3HtDONh5TX" }, "source": [ "Note: `Dataset.cache` stores the data from the first epoch and replays it in order. So, using the `cache` method disables any shuffles earlier in the pipeline. Below, `Dataset.shuffle` is added back in after `Dataset.cache`." ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:51.131492Z", "iopub.status.busy": "2024-08-16T07:14:51.131211Z", "iopub.status.idle": "2024-08-16T07:14:52.865880Z", "shell.execute_reply": "2024-08-16T07:14:52.865109Z" }, "id": "r5Jj72MrPbnh" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "................" ] }, { "name": "stdout", "output_type": "stream", "text": [ "................." ] }, { "name": "stdout", "output_type": "stream", "text": [ "................." ] }, { "name": "stdout", "output_type": "stream", "text": [ "................." ] }, { "name": "stdout", "output_type": "stream", "text": [ "................." ] }, { "name": "stdout", "output_type": "stream", "text": [ "...........\n", "CPU times: user 1.83 s, sys: 197 ms, total: 2.03 s\n", "Wall time: 1.73 s\n" ] } ], "source": [ "%%time\n", "caching = traffic_volume_csv_gz_ds.cache().shuffle(1000)\n", "\n", "for i, (batch, label) in enumerate(caching.shuffle(1000).repeat(20)):\n", " if i % 40 == 0:\n", " print('.', end='')\n", "print()" ] }, { "cell_type": "markdown", "metadata": { "id": "wN7uUBjmgNZ9" }, "source": [ "Note: The `tf.data.Dataset.snapshot` files are meant for *temporary* storage of a dataset while in use. This is *not* a format for long term storage. The file format is considered an internal detail, and not guaranteed between TensorFlow versions." ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:52.869542Z", "iopub.status.busy": "2024-08-16T07:14:52.869254Z", "iopub.status.idle": "2024-08-16T07:14:54.953044Z", "shell.execute_reply": "2024-08-16T07:14:54.952381Z" }, "id": "PHGD1E8ktUvW" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "............" ] }, { "name": "stdout", "output_type": "stream", "text": [ "............" ] }, { "name": "stdout", "output_type": "stream", "text": [ "..............." ] }, { "name": "stdout", "output_type": "stream", "text": [ "..............." ] }, { "name": "stdout", "output_type": "stream", "text": [ "..............." ] }, { "name": "stdout", "output_type": "stream", "text": [ "................" ] }, { "name": "stdout", "output_type": "stream", "text": [ "..........\n", "CPU times: user 2.71 s, sys: 686 ms, total: 3.39 s\n", "Wall time: 2.08 s\n" ] } ], "source": [ "%%time\n", "snapshotting = traffic_volume_csv_gz_ds.snapshot('titanic.tfsnap').shuffle(1000)\n", "\n", "for i, (batch, label) in enumerate(snapshotting.shuffle(1000).repeat(20)):\n", " if i % 40 == 0:\n", " print('.', end='')\n", "print()" ] }, { "cell_type": "markdown", "metadata": { "id": "fUSSegnMCGRz" }, "source": [ "If your data loading is slowed by loading CSV files, and `Dataset.cache` and `tf.data.Dataset.snapshot` are insufficient for your use case, consider re-encoding your data into a more streamlined format." ] }, { "cell_type": "markdown", "metadata": { "id": "M0iGXv9pC5kr" }, "source": [ "### Multiple files" ] }, { "cell_type": "markdown", "metadata": { "id": "9FFzHQrCDH4w" }, "source": [ "All the examples so far in this section could easily be done without `tf.data`. One place where `tf.data` can really simplify things is when dealing with collections of files.\n", "\n", "For example, the [character font images](https://archive.ics.uci.edu/ml/datasets/Character+Font+Images) dataset is distributed as a collection of csv files, one per font.\n", "\n", "![Fonts](images/csv/fonts.jpg)\n", "\n", "Image by Willi Heidelbach from Pixabay\n", "\n", "Download the dataset, and review the files inside:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:14:54.957197Z", "iopub.status.busy": "2024-08-16T07:14:54.956644Z", "iopub.status.idle": "2024-08-16T07:33:15.844686Z", "shell.execute_reply": "2024-08-16T07:33:15.843860Z" }, "id": "RmVknMdJh5ks" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://archive.ics.uci.edu/ml/machine-learning-databases/00417/fonts.zip\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 0/Unknown 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "160276480/Unknown \u001b[1m1096s\u001b[0m 7us/step" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "160301056/Unknown \u001b[1m1096s\u001b[0m 7us/step" ] } ], "source": [ "fonts_zip = tf.keras.utils.get_file(\n", " 'fonts.zip', \"https://archive.ics.uci.edu/ml/machine-learning-databases/00417/fonts.zip\",\n", " cache_dir='.', cache_subdir='fonts',\n", " extract=True)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:15.849093Z", "iopub.status.busy": "2024-08-16T07:33:15.848528Z", "iopub.status.idle": "2024-08-16T07:33:15.855186Z", "shell.execute_reply": "2024-08-16T07:33:15.854550Z" }, "id": "xsDlMCnyi55e" }, "outputs": [ { "data": { "text/plain": [ "['fonts/AGENCY.csv',\n", " 'fonts/ARIAL.csv',\n", " 'fonts/BAITI.csv',\n", " 'fonts/BANKGOTHIC.csv',\n", " 'fonts/BASKERVILLE.csv',\n", " 'fonts/BAUHAUS.csv',\n", " 'fonts/BELL.csv',\n", " 'fonts/BERLIN.csv',\n", " 'fonts/BERNARD.csv',\n", " 'fonts/BITSTREAMVERA.csv']" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pathlib\n", "font_csvs = sorted(str(p) for p in pathlib.Path('fonts').glob(\"*.csv\"))\n", "\n", "font_csvs[:10]" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:15.857757Z", "iopub.status.busy": "2024-08-16T07:33:15.857535Z", "iopub.status.idle": "2024-08-16T07:33:15.861504Z", "shell.execute_reply": "2024-08-16T07:33:15.860952Z" }, "id": "lRAEJx9ROAGl" }, "outputs": [ { "data": { "text/plain": [ "153" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(font_csvs)" ] }, { "cell_type": "markdown", "metadata": { "id": "19Udrw9iG-FS" }, "source": [ "When dealing with a bunch of files, you can pass a glob-style `file_pattern` to the `tf.data.experimental.make_csv_dataset` function. The order of the files is shuffled each iteration.\n", "\n", "Use the `num_parallel_reads` argument to set how many files are read in parallel and interleaved together." ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:15.864858Z", "iopub.status.busy": "2024-08-16T07:33:15.864331Z", "iopub.status.idle": "2024-08-16T07:33:16.851274Z", "shell.execute_reply": "2024-08-16T07:33:16.850528Z" }, "id": "6TSUNdT6iG58" }, "outputs": [], "source": [ "fonts_ds = tf.data.experimental.make_csv_dataset(\n", " file_pattern = \"fonts/*.csv\",\n", " batch_size=10, num_epochs=1,\n", " num_parallel_reads=20,\n", " shuffle_buffer_size=10000)" ] }, { "cell_type": "markdown", "metadata": { "id": "XMoexinLHYFa" }, "source": [ "These CSV files have the images flattened out into a single row. The column names are formatted `r{row}c{column}`. Here's the first batch:" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:16.855331Z", "iopub.status.busy": "2024-08-16T07:33:16.855072Z", "iopub.status.idle": "2024-08-16T07:33:18.818570Z", "shell.execute_reply": "2024-08-16T07:33:18.817630Z" }, "id": "RmFvBWxxi3pq" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "font : [b'CONSOLAS' b'BITSTREAMVERA' b'CORBEL' b'BRUSH' b'ROMAN' b'KUNSTLER'\n", " b'OCRA' b'BITSTREAMVERA' b'BITSTREAMVERA' b'TW']\n", "fontVariant : [b'CONSOLAS' b'scanned' b'CORBEL' b'BRUSH SCRIPT MT' b'ROMANT'\n", " b'KUNSTLER SCRIPT' b'scanned' b'scanned' b'scanned'\n", " b'TW CEN MT CONDENSED EXTRA BOLD']\n", "m_label : [9520 57 733 305 88 173 84 79 82 8212]\n", "strength : [0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4]\n", "italic : [0 0 0 1 0 0 0 0 0 0]\n", "orientation : [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", "m_top : [57 0 35 57 40 67 0 0 0 60]\n", "m_left : [20 0 22 20 23 21 0 0 0 20]\n", "originalH : [42 20 11 23 50 1 14 21 20 8]\n", "originalW : [37 69 19 16 42 9 8 55 32 67]\n", "h : [20 20 20 20 20 20 20 20 20 20]\n", "w : [20 20 20 20 20 20 20 20 20 20]\n", "r0c0 : [255 1 1 1 107 255 255 1 1 255]\n", "r0c1 : [255 1 1 1 209 255 255 1 1 255]\n", "r0c2 : [255 1 1 1 255 255 255 1 1 255]\n", "r0c3 : [255 1 1 1 255 255 255 5 1 255]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "...\n", "[total: 412 features]\n" ] } ], "source": [ "for features in fonts_ds.take(1):\n", " for i, (name, value) in enumerate(features.items()):\n", " if i>15:\n", " break\n", " print(f\"{name:20s}: {value}\")\n", "print('...')\n", "print(f\"[total: {len(features)} features]\")" ] }, { "cell_type": "markdown", "metadata": { "id": "xrC3sKdeOhb5" }, "source": [ "#### Optional: Packing fields\n", "\n", "You probably don't want to work with each pixel in separate columns like this. Before trying to use this dataset be sure to pack the pixels into an image-tensor.\n", "\n", "Here is code that parses the column names to build images for each example:" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:18.822250Z", "iopub.status.busy": "2024-08-16T07:33:18.821590Z", "iopub.status.idle": "2024-08-16T07:33:18.826943Z", "shell.execute_reply": "2024-08-16T07:33:18.826226Z" }, "id": "hct5EMEWNyfH" }, "outputs": [], "source": [ "import re\n", "\n", "def make_images(features):\n", " image = [None]*400\n", " new_feats = {}\n", "\n", " for name, value in features.items():\n", " match = re.match('r(\\d+)c(\\d+)', name)\n", " if match:\n", " image[int(match.group(1))*20+int(match.group(2))] = value\n", " else:\n", " new_feats[name] = value\n", "\n", " image = tf.stack(image, axis=0)\n", " image = tf.reshape(image, [20, 20, -1])\n", " new_feats['image'] = image\n", "\n", " return new_feats" ] }, { "cell_type": "markdown", "metadata": { "id": "61qy8utAwARP" }, "source": [ "Apply that function to each batch in the dataset:" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:18.830173Z", "iopub.status.busy": "2024-08-16T07:33:18.829639Z", "iopub.status.idle": "2024-08-16T07:33:21.235533Z", "shell.execute_reply": "2024-08-16T07:33:21.234637Z" }, "id": "DJnnfIW9baE4" }, "outputs": [], "source": [ "fonts_image_ds = fonts_ds.map(make_images)\n", "\n", "for features in fonts_image_ds.take(1):\n", " break" ] }, { "cell_type": "markdown", "metadata": { "id": "_ThqrthGwHSm" }, "source": [ "Plot the resulting images:" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:21.239638Z", "iopub.status.busy": "2024-08-16T07:33:21.239300Z", "iopub.status.idle": "2024-08-16T07:33:21.986413Z", "shell.execute_reply": "2024-08-16T07:33:21.985755Z" }, "id": "I5dcey31T_tk" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/IPython/core/events.py:82: UserWarning: Glyph 65045 (\\N{PRESENTATION FORM FOR VERTICAL EXCLAMATION MARK}) missing from font(s) DejaVu Sans.\n", " func(*args, **kwargs)\n", "/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 65045 (\\N{PRESENTATION FORM FOR VERTICAL EXCLAMATION MARK}) missing from font(s) DejaVu Sans.\n", " fig.canvas.print_figure(bytes_io, **kw)\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import pyplot as plt\n", "\n", "plt.figure(figsize=(6,6), dpi=120)\n", "\n", "for n in range(9):\n", " plt.subplot(3,3,n+1)\n", " plt.imshow(features['image'][..., n])\n", " plt.title(chr(features['m_label'][n]))\n", " plt.axis('off')" ] }, { "cell_type": "markdown", "metadata": { "id": "7-nNR0Nncdd1" }, "source": [ "## Lower level functions" ] }, { "cell_type": "markdown", "metadata": { "id": "3jiGZeUijJNd" }, "source": [ "So far this tutorial has focused on the highest-level utilities for reading csv data. There are two other APIs that may be helpful for advanced users if your use-case doesn't fit the basic patterns.\n", "\n", "* `tf.io.decode_csv`: a function for parsing lines of text into a list of CSV column tensors.\n", "* `tf.data.experimental.CsvDataset`: a lower-level CSV dataset constructor.\n", "\n", "This section recreates functionality provided by `tf.data.experimental.make_csv_dataset`, to demonstrate how this lower-level functionality can be used.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "LL_ixywomOHW" }, "source": [ "### `tf.io.decode_csv`\n", "\n", "This function decodes a string, or list of strings into a list of columns.\n", "\n", "Unlike `tf.data.experimental.make_csv_dataset` this function does not try to guess column data-types. You specify the column types by providing a list of `record_defaults` containing a value of the correct type, for each column.\n", "\n", "To read the Titanic data **as strings** using `tf.io.decode_csv` you would say:" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:21.989634Z", "iopub.status.busy": "2024-08-16T07:33:21.989387Z", "iopub.status.idle": "2024-08-16T07:33:21.995009Z", "shell.execute_reply": "2024-08-16T07:33:21.994372Z" }, "id": "m1D2C-qdlqeW" }, "outputs": [ { "data": { "text/plain": [ "['', '', '', '', '', '', '', '', '', '']" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "text = pathlib.Path(titanic_file_path).read_text()\n", "lines = text.split('\\n')[1:-1]\n", "\n", "all_strings = [str()]*10\n", "all_strings" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:21.997761Z", "iopub.status.busy": "2024-08-16T07:33:21.997536Z", "iopub.status.idle": "2024-08-16T07:33:22.004234Z", "shell.execute_reply": "2024-08-16T07:33:22.003554Z" }, "id": "9W4UeJYyHPx5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "type: string, shape: (627,)\n", "type: string, shape: (627,)\n", "type: string, shape: (627,)\n", "type: string, shape: (627,)\n", "type: string, shape: (627,)\n", "type: string, shape: (627,)\n", "type: string, shape: (627,)\n", "type: string, shape: (627,)\n", "type: string, shape: (627,)\n", "type: string, shape: (627,)\n" ] } ], "source": [ "features = tf.io.decode_csv(lines, record_defaults=all_strings) \n", "\n", "for f in features:\n", " print(f\"type: {f.dtype.name}, shape: {f.shape}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "j8TaHSQFoQL4" }, "source": [ "To parse them with their actual types, create a list of `record_defaults` of the corresponding types: " ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:22.007209Z", "iopub.status.busy": "2024-08-16T07:33:22.006969Z", "iopub.status.idle": "2024-08-16T07:33:22.010331Z", "shell.execute_reply": "2024-08-16T07:33:22.009824Z" }, "id": "rzUjR59yoUe1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0,male,22.0,1,0,7.25,Third,unknown,Southampton,n\n" ] } ], "source": [ "print(lines[0])" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:22.013292Z", "iopub.status.busy": "2024-08-16T07:33:22.012700Z", "iopub.status.idle": "2024-08-16T07:33:22.017523Z", "shell.execute_reply": "2024-08-16T07:33:22.016952Z" }, "id": "7sPTunxwoeWU" }, "outputs": [ { "data": { "text/plain": [ "[0, '', 0.0, 0, 0, 0.0, '', '', '', '']" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic_types = [int(), str(), float(), int(), int(), float(), str(), str(), str(), str()]\n", "titanic_types" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:22.020124Z", "iopub.status.busy": "2024-08-16T07:33:22.019886Z", "iopub.status.idle": "2024-08-16T07:33:22.027008Z", "shell.execute_reply": "2024-08-16T07:33:22.026418Z" }, "id": "n3NlViCzoB7F" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "type: int32, shape: (627,)\n", "type: string, shape: (627,)\n", "type: float32, shape: (627,)\n", "type: int32, shape: (627,)\n", "type: int32, shape: (627,)\n", "type: float32, shape: (627,)\n", "type: string, shape: (627,)\n", "type: string, shape: (627,)\n", "type: string, shape: (627,)\n", "type: string, shape: (627,)\n" ] } ], "source": [ "features = tf.io.decode_csv(lines, record_defaults=titanic_types) \n", "\n", "for f in features:\n", " print(f\"type: {f.dtype.name}, shape: {f.shape}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "m-LkTUTnpn2P" }, "source": [ "Note: It is more efficient to call `tf.io.decode_csv` on large batches of lines than on individual lines of CSV text." ] }, { "cell_type": "markdown", "metadata": { "id": "Yp1UItJmqGqw" }, "source": [ "### `tf.data.experimental.CsvDataset`\n", "\n", "The `tf.data.experimental.CsvDataset` class provides a minimal CSV `Dataset` interface without the convenience features of the `tf.data.experimental.make_csv_dataset` function: column header parsing, column type-inference, automatic shuffling, file interleaving.\n", "\n", "This constructor uses `record_defaults` the same way as `tf.io.decode_csv`:\n" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:22.029985Z", "iopub.status.busy": "2024-08-16T07:33:22.029738Z", "iopub.status.idle": "2024-08-16T07:33:22.047192Z", "shell.execute_reply": "2024-08-16T07:33:22.046507Z" }, "id": "9OzZLp3krP-t" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, b'male', 22.0, 1, 0, 7.25, b'Third', b'unknown', b'Southampton', b'n']\n" ] } ], "source": [ "simple_titanic = tf.data.experimental.CsvDataset(titanic_file_path, record_defaults=titanic_types, header=True)\n", "\n", "for example in simple_titanic.take(1):\n", " print([e.numpy() for e in example])" ] }, { "cell_type": "markdown", "metadata": { "id": "_HBmfI-Ks7dw" }, "source": [ "The above code is basically equivalent to:" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:22.050454Z", "iopub.status.busy": "2024-08-16T07:33:22.049817Z", "iopub.status.idle": "2024-08-16T07:33:22.140716Z", "shell.execute_reply": "2024-08-16T07:33:22.140051Z" }, "id": "E5O5d69Yq7gG" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, b'male', 22.0, 1, 0, 7.25, b'Third', b'unknown', b'Southampton', b'n']\n" ] } ], "source": [ "def decode_titanic_line(line):\n", " return tf.io.decode_csv(line, titanic_types)\n", "\n", "manual_titanic = (\n", " # Load the lines of text\n", " tf.data.TextLineDataset(titanic_file_path)\n", " # Skip the header row.\n", " .skip(1)\n", " # Decode the line.\n", " .map(decode_titanic_line)\n", ")\n", "\n", "for example in manual_titanic.take(1):\n", " print([e.numpy() for e in example])" ] }, { "cell_type": "markdown", "metadata": { "id": "5R3ralsnt2AC" }, "source": [ "#### Multiple files\n", "\n", "To parse the fonts dataset using `tf.data.experimental.CsvDataset`, you first need to determine the column types for the `record_defaults`. Start by inspecting the first row of one file:" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:22.144187Z", "iopub.status.busy": "2024-08-16T07:33:22.143609Z", "iopub.status.idle": "2024-08-16T07:33:22.151352Z", "shell.execute_reply": "2024-08-16T07:33:22.150653Z" }, "id": "3tlFOTjCvAI5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AGENCY,AGENCY FB,64258,0.400000,0,0.000000,35,21,51,22,20,20,1,1,1,21,101,210,255,255,255,255,255,255,255,255,255,255,255,255,255,255,1,1,1,93,255,255,255,176,146,146,146,146,146,146,146,146,216,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,141,141,141,182,255,255,255,172,141,141,141,115,1,1,1,1,163,255,255,255,255,255,255,255,255,255,255,255,255,255,255,209,1,1,1,1,163,255,255,255,6,6,6,96,255,255,255,74,6,6,6,5,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255,1,1,1,93,255,255,255,70,1,1,1,1,1,1,1,1,163,255,255,255\n" ] } ], "source": [ "font_line = pathlib.Path(font_csvs[0]).read_text().splitlines()[1]\n", "print(font_line)" ] }, { "cell_type": "markdown", "metadata": { "id": "etyGu8K_ySRz" }, "source": [ "Only the first two fields are strings, the rest are integers or floats, and you can get the total number of features by counting the commas:" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:22.154283Z", "iopub.status.busy": "2024-08-16T07:33:22.153979Z", "iopub.status.idle": "2024-08-16T07:33:22.157705Z", "shell.execute_reply": "2024-08-16T07:33:22.157123Z" }, "id": "crgZZn0BzkSB" }, "outputs": [], "source": [ "num_font_features = font_line.count(',')+1\n", "font_column_types = [str(), str()] + [float()]*(num_font_features-2)" ] }, { "cell_type": "markdown", "metadata": { "id": "YeK2Pw540RNj" }, "source": [ "The `tf.data.experimental.CsvDataset` constructor can take a list of input files, but reads them sequentially. The first file in the list of CSVs is `AGENCY.csv`:" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:22.160577Z", "iopub.status.busy": "2024-08-16T07:33:22.160353Z", "iopub.status.idle": "2024-08-16T07:33:22.164574Z", "shell.execute_reply": "2024-08-16T07:33:22.163921Z" }, "id": "_SvL5Uvl0r0N" }, "outputs": [ { "data": { "text/plain": [ "'fonts/AGENCY.csv'" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "font_csvs[0]" ] }, { "cell_type": "markdown", "metadata": { "id": "EfAX3G8Xywy6" }, "source": [ "So, when you pass the list of files to `CsvDataset`, the records from `AGENCY.csv` are read first:" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:22.167326Z", "iopub.status.busy": "2024-08-16T07:33:22.167096Z", "iopub.status.idle": "2024-08-16T07:33:22.200319Z", "shell.execute_reply": "2024-08-16T07:33:22.199735Z" }, "id": "Gtr1E66VmBqj" }, "outputs": [], "source": [ "simple_font_ds = tf.data.experimental.CsvDataset(\n", " font_csvs, \n", " record_defaults=font_column_types, \n", " header=True)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:22.203012Z", "iopub.status.busy": "2024-08-16T07:33:22.202792Z", "iopub.status.idle": "2024-08-16T07:33:22.275205Z", "shell.execute_reply": "2024-08-16T07:33:22.274500Z" }, "id": "k750Mgq4yt_o" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "b'AGENCY'" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "b'AGENCY'\n", "b'AGENCY'\n", "b'AGENCY'\n", "b'AGENCY'\n", "b'AGENCY'\n", "b'AGENCY'\n", "b'AGENCY'\n", "b'AGENCY'\n", "b'AGENCY'\n" ] } ], "source": [ "for row in simple_font_ds.take(10):\n", " print(row[0].numpy())" ] }, { "cell_type": "markdown", "metadata": { "id": "NiqWKQV21FrE" }, "source": [ "To interleave multiple files, use `Dataset.interleave`.\n", "\n", "Here's an initial dataset that contains the CSV file names: " ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:22.277962Z", "iopub.status.busy": "2024-08-16T07:33:22.277723Z", "iopub.status.idle": "2024-08-16T07:33:22.621274Z", "shell.execute_reply": "2024-08-16T07:33:22.620508Z" }, "id": "t9dS3SNb23W8" }, "outputs": [], "source": [ "font_files = tf.data.Dataset.list_files(\"fonts/*.csv\")" ] }, { "cell_type": "markdown", "metadata": { "id": "TNiLHMXpzHy5" }, "source": [ "This shuffles the file names each epoch:" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:22.625431Z", "iopub.status.busy": "2024-08-16T07:33:22.625172Z", "iopub.status.idle": "2024-08-16T07:33:22.665332Z", "shell.execute_reply": "2024-08-16T07:33:22.664699Z" }, "id": "zNd-TYyNzIgg" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1:\n", " b'fonts/YI BAITI.csv'\n", " b'fonts/HAETTENSCHWEILER.csv'\n", " b'fonts/SKETCHFLOW.csv'\n", " b'fonts/JUICE.csv'\n", " b'fonts/CONSOLAS.csv'\n", " ...\n", "\n", "Epoch 2:\n", " b'fonts/PHAGSPA.csv'\n", " b'fonts/GILL.csv'\n", " b'fonts/ROCKWELL.csv'\n", " b'fonts/JUICE.csv'\n", " b'fonts/SUPERFRENCH.csv'\n", " ...\n" ] } ], "source": [ "print('Epoch 1:')\n", "for f in list(font_files)[:5]:\n", " print(\" \", f.numpy())\n", "print(' ...')\n", "print()\n", "\n", "print('Epoch 2:')\n", "for f in list(font_files)[:5]:\n", " print(\" \", f.numpy())\n", "print(' ...')" ] }, { "cell_type": "markdown", "metadata": { "id": "B0QB1PtU3WAN" }, "source": [ "The `interleave` method takes a `map_func` that creates a child-`Dataset` for each element of the parent-`Dataset`.\n", "\n", "Here, you want to create a `tf.data.experimental.CsvDataset` from each element of the dataset of files:" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:22.668417Z", "iopub.status.busy": "2024-08-16T07:33:22.668182Z", "iopub.status.idle": "2024-08-16T07:33:22.671719Z", "shell.execute_reply": "2024-08-16T07:33:22.671052Z" }, "id": "QWp4rH0Q4uPh" }, "outputs": [], "source": [ "def make_font_csv_ds(path):\n", " return tf.data.experimental.CsvDataset(\n", " path, \n", " record_defaults=font_column_types, \n", " header=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "VxRGdLMB5nRF" }, "source": [ "The `Dataset` returned by interleave returns elements by cycling over a number of the child-`Dataset`s. Note, below, how the dataset cycles over `cycle_length=3` three font files:" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:22.674640Z", "iopub.status.busy": "2024-08-16T07:33:22.674373Z", "iopub.status.idle": "2024-08-16T07:33:22.883357Z", "shell.execute_reply": "2024-08-16T07:33:22.882683Z" }, "id": "OePMNF_x1_Cc" }, "outputs": [], "source": [ "font_rows = font_files.interleave(make_font_csv_ds,\n", " cycle_length=3)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:22.886464Z", "iopub.status.busy": "2024-08-16T07:33:22.886232Z", "iopub.status.idle": "2024-08-16T07:33:23.033258Z", "shell.execute_reply": "2024-08-16T07:33:23.032589Z" }, "id": "UORIGWLy54-E" }, "outputs": [ { "data": { "text/html": [ "
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font_namecharacter
0STENCIL◊
1EDWARDIAN
2SITKAffl
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4EDWARDIAN
5SITKAffi
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\n", "
" ], "text/plain": [ " font_name character\n", "0 STENCIL ◊\n", "1 EDWARDIAN \n", "2 SITKA ffl\n", "3 STENCIL ≥\n", "4 EDWARDIAN \n", "5 SITKA ffi\n", "6 STENCIL ≤\n", "7 EDWARDIAN ◊\n", "8 SITKA fl\n", "9 STENCIL ≠" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fonts_dict = {'font_name':[], 'character':[]}\n", "\n", "for row in font_rows.take(10):\n", " fonts_dict['font_name'].append(row[0].numpy().decode())\n", " fonts_dict['character'].append(chr(int(row[2].numpy())))\n", "\n", "pd.DataFrame(fonts_dict)" ] }, { "cell_type": "markdown", "metadata": { "id": "mkKZa_HX8zAm" }, "source": [ "#### Performance\n" ] }, { "cell_type": "markdown", "metadata": { "id": "8BtGHraUApdJ" }, "source": [ "Earlier, it was noted that `tf.io.decode_csv` is more efficient when run on a batch of strings.\n", "\n", "It is possible to take advantage of this fact, when using large batch sizes, to improve CSV loading performance (but try [caching](#caching) first)." ] }, { "cell_type": "markdown", "metadata": { "id": "d35zWMH7MDL1" }, "source": [ "With the built-in loader 20, 2048-example batches take about 17s. " ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:23.037014Z", "iopub.status.busy": "2024-08-16T07:33:23.036762Z", "iopub.status.idle": "2024-08-16T07:33:23.840652Z", "shell.execute_reply": "2024-08-16T07:33:23.839943Z" }, "id": "ieUVAPryjpJS" }, "outputs": [], "source": [ "BATCH_SIZE=2048\n", "fonts_ds = tf.data.experimental.make_csv_dataset(\n", " file_pattern = \"fonts/*.csv\",\n", " batch_size=BATCH_SIZE, num_epochs=1,\n", " num_parallel_reads=100)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:23.844392Z", "iopub.status.busy": "2024-08-16T07:33:23.844108Z", "iopub.status.idle": "2024-08-16T07:33:44.282492Z", "shell.execute_reply": "2024-08-16T07:33:44.281321Z" }, "id": "MUC2KW4LkQIz" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ "." ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "CPU times: user 48.7 s, sys: 3.96 s, total: 52.7 s\n", "Wall time: 20.4 s\n" ] } ], "source": [ "%%time\n", "for i,batch in enumerate(fonts_ds.take(20)):\n", " print('.',end='')\n", "\n", "print()" ] }, { "cell_type": "markdown", "metadata": { "id": "5lhnh6rZEDS2" }, "source": [ "Passing **batches of text lines** to`decode_csv` runs faster, in about 5s:" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:44.286503Z", "iopub.status.busy": "2024-08-16T07:33:44.286090Z", "iopub.status.idle": "2024-08-16T07:33:44.741378Z", "shell.execute_reply": "2024-08-16T07:33:44.740659Z" }, "id": "4XbPZV1okVF9" }, "outputs": [], "source": [ "fonts_files = tf.data.Dataset.list_files(\"fonts/*.csv\")\n", "fonts_lines = fonts_files.interleave(\n", " lambda fname:tf.data.TextLineDataset(fname).skip(1), \n", " cycle_length=100).batch(BATCH_SIZE)\n", "\n", "fonts_fast = fonts_lines.map(lambda x: tf.io.decode_csv(x, record_defaults=font_column_types))" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:33:44.745216Z", "iopub.status.busy": "2024-08-16T07:33:44.744785Z", "iopub.status.idle": "2024-08-16T07:33:45.681024Z", "shell.execute_reply": "2024-08-16T07:33:45.680298Z" }, "id": "te9C2km-qO8W" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "...................." ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "CPU times: user 3.93 s, sys: 125 ms, total: 4.06 s\n", "Wall time: 931 ms\n" ] } ], "source": [ "%%time\n", "for i,batch in enumerate(fonts_fast.take(20)):\n", " print('.',end='')\n", "\n", "print()" ] }, { "cell_type": "markdown", "metadata": { "id": "aebC1plsMeOi" }, "source": [ "For another example of increasing CSV performance by using large batches, refer to the [Overfit and underfit tutorial](../keras/overfit_and_underfit.ipynb).\n", "\n", "This sort of approach may work, but consider other options like `Dataset.cache` and `tf.data.Dataset.snapshot`, or re-encoding your data into a more streamlined format." ] } ], "metadata": { "colab": { "name": "csv.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "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": "3.9.19" } }, "nbformat": 4, "nbformat_minor": 0 }