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Demonstrating Deep Learning Models Apache, Spark, and TensorFlow
Neural networks have seen spectacular progress during the last few years and they are now the state of the art in image recognition and automated translation. TensorFlow is a new framework released by Google for numerical computations and neural networks. In this blog post, we are going to demonstrate how to use TensorFlow and Spark together to train and apply deep learning models.
- A number of neurons in each layer: Too few neurons will reduce the expression power of the network, but too many will substantially increase the running time and return noisy estimates.
- Learning rate: If it is too high, the neural network will only focus on the last few samples seen and disregard all the experience accumulated before. If it is too low, it will take too long to reach a good state.
- The learning rate is critical: if it is too low, the neural network does not learn anything (high test error). If it is too high, the training process may oscillate randomly and even diverge in some configurations.
- The number of neurons is not as important for getting a good performance, and networks with many neurons are much more sensitive to the learning rate. This is Occam's Razor principle: simpler model tend to be "good enough" for most purposes. If you have the time and resource to go after the missing 1% test error, you must be willing to invest a lot of resources in training and to find the proper hyperparameters that will make the difference.
- Distributed processing of images using TensorFlow
- Testing the distribution processing of images using TensorFlow