Multi-class metrics for Tensorflow
-
Updated
Sep 20, 2022 - Python
Multi-class metrics for Tensorflow
Try to use tf.estimator and tf.data together to train a cnn model.
Train, predict, export and reload a tf.estimator for inference
Weakly supervised 3D classification of multi-disease chest CT scans using multi-resolution deep segmentation features via dual-stage CNN architecture (DenseVNet, 3D Residual U-Net).
Gradient accumulation on tf.estimator
Distributed Deep Learning Framework on Ray, including tensorflow/pytorch/mxnet
Tensorflow estimator implementation of the C3D network
OpenAI Glow implementation for TPU/GPU
Fully supervised, healthy/malignant prostate detection in multi-parametric MRI (T2W, DWI, ADC), using a modified 2D RetinaNet model for medical object detection, built upon a shallow SEResNet backbone.
Fully supervised, multi-class 3D brain segmentation in T1 MRI using an ensemble of diverse CNN architectures (3D FCN, 3D U-Net) with multi-scale input.
TensorFlow practice using the higher-level APIs
Scripts to practice the basics of TF and Keras while building networks for image classification (CIFAR, MNIST).
ResNet for CIFAR with Estimator API and tf.keras.Model class
Add a description, image, and links to the tensorflow-estimator topic page so that developers can more easily learn about it.
To associate your repository with the tensorflow-estimator topic, visit your repo's landing page and select "manage topics."