✳flâneur — a map of the web's best reading

Convolutional Neural Network (CNN)  |  TensorFlow Core

tensorflow.org · 2,477 words · saved by 1 readers

This tutorial demonstrates training a simple Convolutional Neural Network (CNN) to classify CIFAR images. Because this tutorial uses the Keras Sequential API, creating and training your model will take just a few lines of code. The CIFAR10 dataset contains 60,000 color images in 10 classes, with 6,000 images in each class. The dataset is divided into 50,000 training images and 10,000 testing images. The classes are mutually exclusive and there is no overlap between them. To verify that the dataset looks correct, let's plot the first 25 images from the training set and display the class name below each image: The 6 lines of code below define the convolutional base using a common pattern: a stack of Conv2D and MaxPooling2D layers. As input, a CNN takes tensors of shape (image_height, image_width, color_channels), ignoring the batch size. If you are new to these dimensions, color_channels refers to (R,G,B). In this example, you will configure your CNN to process inputs of shape (32, 32, 3

TensorFlow Learn TensorFlow Core Convolutional Neural Network (CNN) Stay organized with collections Save and categorize content based on your preferences. View on TensorFlow.org Run in Google Colab View source on GitHub Download notebook This tutorial demonstrates training a simple Convolutional Neural Network (CNN) to classify CIFAR images . Because this tutorial uses the Keras Sequential API , creating and training your model will take just a few lines of code. Import TensorFlow import tensorflow as tf from tensorflow.keras import datasets , layers , models import matplotlib.pyplot as plt 20

Explore this link on the map →

saved by

related reading