<jupyter_start><jupyter_text>Create a new representation of a deep neural network  Using the example of VGG-16 (<NAME>, <NAME>, Very Deep Convolutional Networks for Large-Scale Image Recognition)In this tutorial we build a representation of the VGG-16 convolutional neural network that can be used to predict the execution time of the model on different hardware.<jupyter_code>import mlpredict<jupyter_output><empty_output><jupyter_text>Create a new instance of the dnn class and add layers to it<jupyter_code>VGG16 = mlpredict.dnn.create_model('VGG16')<jupyter_output><empty_output><jupyter_text>Display model architecture<jupyter_code>VGG16.describe()<jupyter_output><empty_output><jupyter_text>Save the representation of the VGG-16 deep neural network as json file.<jupyter_code>VGG16.save('../dnn_architecture/VGG16.json')<jupyter_output><empty_output>
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