![]() ![]() compile (loss = 'categorical_crossentropy', optimizer =sgd, metrics = ) Sgd = SGD (lr =lrate, momentum = 0.9, decay =decay, nesterov = False ) add (Dense (num_classes, activation = 'softmax' ) ) add (Dense ( 512, activation = 'relu', kernel_constraint =maxnorm ( 3 ) ) ) add (MaxPooling2D (pool_size = ( 2, 2 ) ) ) add (Conv2D ( 32, ( 3, 3 ), activation = 'relu', padding = 'same', kernel_constraint =maxnorm ( 3 ) ) ) add (Conv2D ( 32, ( 3, 3 ), input_shape =input_shape, padding = 'same', activation = 'relu', kernel_constraint =maxnorm ( 3 ) ) ) However, you can few the workable code from this post. So, you maybe not able to find some definition of some variables in this post. For this exploration, we crop the lines where we use to create CNN(Convolutional Neural Network) model in our previous post. Therefore, in this post we are going to explore all of them. With Keras, deep learning model are very easy to create, but there are 5 key steps you must follow. Then you should see the version of Kerasthat installed on your machine. To verify Keras go into python console and type: import keras Then you can install Keras into your machine via a command: sudo pip install keras Where TensorFlow is the recommend backend engine. Supports both convolutional networks and recurrent networks, as well as combinations of the two.īefore installing Keras, you need to install one of its backend engines: TensorFlow, Theano, or CNTK.Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).There are few resons why you should choose Keras for building your project: It can run on top of TensorFlow, CNTK, or Theano which is the ideal for deep learning beginner to build and explore the power of deep learning with ease. Keras is a python library which use to build a deep learning model with just a few short lines of code. ![]()
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