Category Archives: machine learning

Sharing Variables in Tensorflow

This article shows how to use sharing variables in Tensroflow. But I still have a question: dose sharing variables have the same value? To answer this question, I write these code below:

Therefore, the “sharing variables” mechanism is made only for convenience of writing short code to create multi-models…. Read more »

Some tips about Tensorflow

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Q: How to fix error report like

A: We can’t feed a value into a variable and optimize it in the same time (So the problem only occurs when using Optimizers). Should using ‘tf.assign()’ in graph to give value to tf.Variable Q: How to get a tensor by name?… Read more »

How to average gradients in Tensorflow

Sometimes, we need to average an array of gradients in deep learning model. Fortunately, Tensorflow divided models into fine-grained tensors and operations, therefore it’s not difficult to implement gradients average by using it. Let’s see the code from github:

We should keep in mind that these codes will only… Read more »

A basic example of using Tensorflow to regress

In theory of Deep Learning, even a network with single hidden layer could represent any function of mathematics. To verify it, I write a Tensorflow example as below:

In this code, it was trying to regress to a number from its own sine-value and cosine-value. At first running, the… Read more »

Fix Resnet-101 model in example of MXNET

SSD(Single Shot MultiBox Detector) is the fastest method in object-detection task (Another detector YOLO, is a little bit slower than SSD). In the source code of MXNET,there is an example for SSD implementation. I test it by using different models: inceptionv3, resnet-50, resnet-101 etc. and find a weird phenomenon: the… Read more »

Use Mxnet To Classify Images Of Birds (Fourth Episode)

More than half a year past since previous article. In this period, Alan Mei (my old ex-colleague) collected more than 1 million pictures of Chinese Avians. And after Alexnet, VGG19, I finally chose Resnet-18 as my DNN model to classify different kinds of Chinese birds. Resnet-18 model has far less… Read more »

Read paper “In-Datacenter Performance Analysis of a Tensor Processing Unit”

Paper reference: In-Datacenter Performance Analysis of a Tensor Processing Unit” Application Using floating point (16bit or 32bit) for NN (Neural Network) training, then a step called quantization transforms floating-point numbers into narrow integers–often just 8 bits–which are usually good enough for inference. MLP(Multi-layer Perceptions), CNN(Convolutional Neural Netowrks), and RNN(Recurrent Neural… Read more »