Deep Neural Network module for classification/regression
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Description
Quantcore's implementation of a deep neural network. Used for classification and regression.
#Usage
The class takes a few requirements, # input nodes, # hidden nodes, # layers, # output nodes, # learning rate, # number of epochs, # output type (specified with r for regression, c for classification), # train test split = 0.8 (percentage you would like to train on), # test = True (turn it off if you don't want to test), split = True (turn it off if you don't want the data split)
The train function takes one input, which is a DATAFRAME with the inputs and the outputs on the right side, e.g.
inputs = dataframe[:-1]
outputs = dataframe[-1]
Do not separate inputs from outputs, just have the outputs as the righter most column in your df
The call to train the function is class_.train(input)
The call to test is class_.feed_forward(input)
#Installation
pip install QCNN
#Sample Code
from QCNN import NeuralNetwork
import pandas as pd
nn= NeuralNetwork(2,2,3,1,.1,100000, train_test_split =1, split=False)
data_dict = {0:{'input1':0,'input2':0,'output':0},
1:{'input1':1,'input2':0,'output':1},
2:{'input1':0,'input2':1,'output':1},
3:{'input1':1,'input2':1,'output':0}
}
df = pd.DataFrame.from_dict(data_dict, orient = 'index')
nn.train(df)
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