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Tensorflow and Keras with R

#artificialintelligence

The Keras package already provides some datasets and pre-trained networks to serve as a learning base, the MNIST dataset is one of them, let's use it. We can see that we get in reply a three-dimensional array, in the first dimension we have the index of the case, and for each one of them, we have a matrix of 28x28 that corresponds to a image of a number. To use with "tensorflow/keras" it is necessary to convert the matrix into a Tensor (generalization of a vector), in this case we have to convert to 4D-Tensor, with dimensions of "n x 28 x 28 x 1", where: The channel in the image stands for the "color encoding". In color images, usually the channel will be a 3-dimensional vector, for RGB values. In the MNIST database, the images are im grey scale, in integers from 0 to 255.


Transfer Learning -- Part -- 4.1!! Implementing VGG-16 and VGG-19 in Keras

#artificialintelligence

Now after loading the model and setting up the parameters it is the time for predicting the image as demonstrated below. Line 3: This snippets send the pre-processed image to the VGG-16 network for getting prediction. Line 4 and Line 5: These two line accept the prediction from the model and output the top 5 prediction probabilities which is shown below.


Will Google drop TensorFlow?

#artificialintelligence

The open-source Python library is mainly used for the training and inference of deep neural networks. For example, GE Healthcare uses TensorFlow to increase the speed and accuracy of MRIs in identifying specific body parts. Meta AI released PyTorch, an open-source machine learning platform, in 2016. PyTorch allows quicker prototyping than TensorFlow. In addition, it is more tightly integrated with the Python ecosystem than TensorFlow, and the debugging experience is much simpler.


Automatic Cross-Replica Sharding of Weight Update in Data-Parallel Training

arXiv.org Machine Learning

In data-parallel synchronous training of deep neural networks, different devices (replicas) run the same program with different partitions of the training batch, but weight update computation is repeated on all replicas, because the weights do not have a batch dimension to partition. This can be a bottleneck for performance and scalability in typical language models with large weights, and models with small per-replica batch size which is typical in large-scale training. This paper presents an approach to automatically shard the weight update computation across replicas with efficient communication primitives and data formatting, using static analysis and transformations on the training computation graph. We show this technique achieves substantial speedups on typical image and language models on Cloud TPUs, requiring no change to model code. This technique helps close the gap between traditionally expensive (ADAM) and cheap (SGD) optimizers, as they will only take a small part of training step time and have similar peak memory usage. It helped us to achieve state-of-the-art training performance in Google's MLPerf 0.6 submission.


How can Federated learning be used for speech emotion recognition?

#artificialintelligence

In the field of machine learning, federated learning can be considered as an approach to make algorithms learned using multiple decentralized devices and data samples. Because of the nature of learning from a decentralized environment, we can also call federated learning collaborative learning. One of the major features of federated learning is that it enables us to use common machine learning without sharing the data in a robust mechanism. This feature also enhances data security and data privacy. Since there is always a need for data privacy and data security in the fields like defence, telecommunications, IoT, and pharmaceutics, this machine learning technique is emerging very rapidly. This learning aims at training machine learning algorithms using the data which is located at different locations without exchanging it with each other. We can think of this learning machine as a process training model by exchanging the parameters in different locations without exchanging the data. The main difference between distributed learning and federated learning is that federated learning aims at learning using heterogeneous data while distributed learning aims at training the models at parallelizing computing settings.