Neural Network Regression model in hebel

Neural Network Regression model in hebel

Recipe Objective - Neural Network Regression model in Hebel?

A neural network is a circuit of neurons or in a broader sense, an Artificial Neural Network composes of nodes that are the basis of deep learning inspired by the human brain in the same way biologically created neurons signal each other, used in solving artificial intelligence problems. Neural networks comprise node layers, with an input layer, one or more hidden layers, and finally an output layer. Node assigns a number to its incoming connection known as 'weight'. It also contains an associated threshold value. If the output of the individual node is above the specified threshold value then that node is activated thereby sending data to the next layer of the network. No data is passed to the next layer of the network if the output of the individual node is below the specified threshold value.

class hebel.models.NeuralNetRegression(layers, top_layer=None, activation_function='sigmoid', dropout=0.0,
input_dropout=0.0, n_in=None, n_out=None, l1_penalty_weight=0.0, l2_penalty_weight=0.0)

A neural network for regression using the squared error loss function.

Getting Started with Image Segmentation using Mask R-CNN

Links for the more related projects:-

https://www.dezyre.com/projects/data-science-projects/deep-learning-projects
https://www.dezyre.com/projects/data-science-projects/neural-network-projects

Example:-

# Simple form
model = NeuralNet(layers=[1000, 1000],
     activation_function='relu',
     dropout=0.5,
     n_in=784, n_out=10,
     l1_penalty_weight=.1)

# Extended form, initializing with "HiddenLayer" and "TopLayer" objects
hidden_layers = [HiddenLayer(784, 1000, 'relu', dropout=0.5,
     l1_penalty_weight=.2),
    HiddenLayer(1000, 1000, 'relu', dropout=0.5,
     l1_penalty_weight=.1)]
softmax_layer = LogisticLayer(1000, 10, l1_penalty_weight=.1)

model = NeuralNet(hidden_layers, softmax_layer)

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