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    Explain Keras

    Here understand that why you should use a deep learning library called Keras to build the first deep neural networks and compare it to other options.

    – Then you use Keras to build an app that generates text in the style of any given author. Deep learning only started getting really popular.

    – Couple of years ago, when Hinton’s team submitted a model that blew away the competition for the large scale visual recognition.

    – Challenge deep neural network was significantly better than all benchmarks because it used lots of GPU computation and data.

    – Other began to take notice and implemented own deep neural networks for different tasks resulting in a deep learning.

    – Impossible similar improvements were made in fields like vision text and speech recognition.

    – Wave net for example was a model that massively speeds up improvements to speech-to-text and text-to-speech. Resulting in lifelike generated audio.

    – Giotto was really the first widely adopted deep learning library. It was maintained by the University of Montreal.

    – Different open-source Python deep learning frameworks have been introduced the past couple of years.

    – Tensorflow seems to be most used deep learning library based on the number github stars and forks as well as stack overflow activity.

    But there are other libraries that are growing passionate user bases as well.

    Pi torch is great example.

    – It is developed in 2017 by Face book. They basically ported the popular torch framework which was written in Lua to Python. The main driver behind pi torches popularity because it is used dynamic computation graphs that means they are defined by run instead of the traditional define and run when inputs can vary .

    – If you are using unstructured data with text, this is super useful and efficient when it comes to static graphs.

    You first draw the graph then inject the data to run it that’s defining the aedra. For dynamic graphs, the graph is defined on the fly via the forward computation of the data.

    – But in addition to tensor flows main framework several companions libraries were released including the tensorflow fold for dynamic computation graphs and tensorflow transform for data input pipelines. The temperature flow team also announced a new eager execution mode which works similar to pi torches dynamic computation graph.

    Best way to learn how some AI concept works is to start building it and figure it out as you go and the best way to do that is by first using a high level library called keras.

    Keras is effectively an interface that wraps multiple frameworks. You can use it as an interface tensor flow; it works the same no matter what back-end you use.

    – Keras is definitely the fastest track when you need to quickly train and test model built from standard layers. Using keras the pipeline for building a deep network looks like this, you define it, compile it, fit it, evaluate it and make predicts as-:

    Consider a three layer neural network with an input layer, hidden layer and output layer.

    – Each of these layers is just a matrix operation input times await and activate the result, repeat that twice and get a prediction.

    – Deep networks have multiple layers, that’s why they are called deep and these layers don’t have to use just one type of operation. These are all sorts of layers out there for different types of networks.

    – Convolution layers drop out layers or current layers. The list goes on but the basic idea of a deep neural network is applying a series of math operations. In order to some input data each layer represents a different operation that then passes the result on to next layer.

    So in a way you can think of these layers as building blocks. If you can list out all the different types of layers you can wrap them into their own classes and then reuse them as modular building blocks.

    That’s exactly what Keras does. It also abstracts away a lot of the magic numbers.

    – You have to input into a deep neural network written in i.e. pure tensorflow. When you define a network, they are defined as a sequence of layers using the sequential class.

    Model = Sequential ()

    – Once you create an instance of the sequential class you can add new layers where each new line is a new layer.

    – You could do this in just two steps or you could do this in one step by creating an array of layers beforehand and pasting it to the constructor of the sequential model.

    The first Layer in the network must define the number of inputs to expect the way, specified can differ depending on the network type.

    – Think of sequential model as a pipeline with your raw data fed in at the bottom and predictions that come out at top.

    Model.add (LSTM (HIDDEN_DIM, input_shape = (None, VOCAB_SIZE), return_sequences = True))……

    It is helpful in Keras as concept that were traditionally associated with the layer can also be split out and added as separate layers clearly, showing the role in then transform of data from input to prediction. For example “activation functions”- transform some signal from each neuron in a layer can be extracted and added to the sequential class as a Layer like object called activation. The choice of activation function is most important for the output layer as it will define the format that predictions will take. Once you defined the network, you will compile it that means it transforms a simple sequence of Layers into a highly efficient series of matrix transforms. As-:

    model.add (Activation (‘softmax’))

    – Intended to be executed on a GPU or CPU depending on your configuration setting, it’s a pre-compute step for the network. It’s required after defining a model.

    Model.compile (loss=”categorical_crossentropy”, optimizer = “rmsprop”)

    – It requires after defining a model.

    Compilation requires a number of parameters to be specified specifically tailored to training the network.

    – Optimization algorithm you use to train the network and the loss function used to evaluate it are things that you can decide. This is the art of deep learning once the network is compiled it can be fit, which means adapting the weights on a training data set fitting. The network requires a training data to be specified both a matrix of input patterns X and an array of matching output patterns. Why the network is trained using the back propagation algorithm and optimized according to the optimized algorithm and loss function specified when compiling the model.

    – Finally once you are satisfied with the performance of your fit model. You can use it to make predictions on new data. This is easy as calling the predict function on the model with an array of new input patterns.

     

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