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What does inception V3 do?
Inception v3 is a convolutional neural network for assisting in image analysis and object detection, and got its start as a module for Googlenet. It is the third edition of Google’s Inception Convolutional Neural Network, originally introduced during the ImageNet Recognition Challenge.
What is TensorFlow inception?
Inception in TensorFlow. ImageNet is a common academic data set in machine learning for training an image recognition system. Code in this directory demonstrates how to use TensorFlow to train and evaluate a type of convolutional neural network (CNN) on this academic data set.
How was inception V3 trained?
Inception V3 was trained using a dataset of 1,000 classes (See the list of classes here) from the original ImageNet dataset which was trained with over 1 million training images, the Tensorflow version has 1,001 classes which is due to an additional “background’ class not used in the original ImageNet.
How to create an output tensor for inception V3?
This place can be specified by an environment variable TFHUB_CACHE_DIR . By passing tensor for input images, you can have an output tensor of Inception-v3. For Inception-v3, the input needs to be 299×299 RGB images, and the output is a 2048 dimensional vector.
Do you need TensorFlow hub to use TensorFlow?
In order to use TensorFlow Hub, the version of TensorFlow has to be greater or equal to 1.7, and you need to install an additional package for TensorFlow Hub. Pre-tr a ined models are managed as module in TensorFlow Hub.
How to preprocess input in keras inception V3?
For InceptionV3, call tf.keras.applications.inception_v3.preprocess_input on your inputs before passing them to the model. inception_v3.preprocess_input will scale input pixels between -1 and 1. include_top: Boolean, whether to include the fully-connected layer at the top, as the last layer of the network.
When to use inception V3 in MNIST?
For example, if the problem is to classify images into 10 categories, you may want to continue fully connected-layer with 10 nodes, and apply the softmax function to have the probabilities. Now we would like to arrange the MNIST tutorial code to use Inception-v3.