![]() # Retrieve the pre-trained model tarball for transfer learning Train_source_uri = script_uris.retrieve(model_id=model_id, model_version=model_version, script_scope="training") Train_image_uri = image_uris.retrieve(model_id=model_id,model_version=model_version,image_scope="training",instance_type=training_instance_type,region=None,framework=None) Model_id, model_version = "tensorflow-ic-imagenet-mobilenet-v2-100-224-classification-4", "*" See the following code:įrom sagemaker import image_uris, model_uris, script_urisįrom sagemaker.estimator import Estimator The pre-trained model tarballs have been pre-downloaded from TensorFlow Hub and saved with the appropriate model signature in Amazon Simple Storage Service (Amazon S3) buckets, such that the training job runs in network isolation. The pre-trained model URI is specific to the particular model. Note that the Docker image URI and the training script URI are the same for all the TensorFlow image classification models. The pre-trained model URI contains the pre-trained model architecture definition and the model parameters. The training script URI contains all the necessary code for data processing, loading the pre-trained model, model training, and saving the trained model for inference. For each model_id, in order to launch a SageMaker training job through the Estimator class of the SageMaker Python SDK, you need to fetch the Docker image URI, training script URI, and pre-trained model URI through the utility functions provided in SageMaker. The following code shows how to fine-tune MobileNet V2 1.00 224 identified by model_id tensorflow-ic-imagenet-mobilenet-v2-100-224-classification-4 on a custom training dataset. Each model is identified by a unique model_id. The algorithm supports transfer learning for the pre-trained models listed in TensorFlow Hub Models. For information on how to use it from the Studio UI, see SageMaker JumpStart. This section describes how to use the TensorFlow image classification algorithm with the SageMaker Python SDK. How to use the new TensorFlow image classification algorithm In this transfer learning mode, you can achieve training even with a smaller dataset. Then either the whole network, including the pre-trained model, or only the top classification layer can be fine-tuned on the new training data. The model training has hyperparameters for the dropout rate of the dropout layer and the L2 regularization factor for the dense layer. The classification layer consists of a dropout layer and a dense layer, which is a fully connected layer with 2-norm regularizer that is initialized with random weights. According to the number of class labels in the training data, a classification layer is attached to the pre-trained TensorFlow Hub model. Image classification with TensorFlow in SageMaker provides transfer learning on many pre-trained models available in TensorFlow Hub. For more information, refer to its documentation Image Classification – TensorFlow and the example notebook Introduction to SageMaker TensorFlow – Image Classification. It’s available through the SageMaker built-in algorithms as well as through the SageMaker JumpStart UI inside Amazon SageMaker Studio. You can fine-tune these pre-trained models using transfer learning even when a large number of training images aren’t available. It takes an image as input and outputs probability for each of the class labels. It is a supervised learning algorithm that supports transfer learning for many pre-trained models available in TensorFlow Hub. Starting today, SageMaker provides a new built-in algorithm for image classification: Image Classification – TensorFlow. They can process various types of input data, including tabular, image, and text. You can use these algorithms and models for both supervised and unsupervised learning. Amazon SageMaker provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. ![]()
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