QCNN 0.0.1

Deep Neural Network module for classification/regression

Description

Quantcore's implementation of a deep neural network. Used for classification and regression.

#Usage

The class takes six requirements, # input nodes, # hidden nodes, # layers, # output nodes, #learning rate, output type (specificed with r for regression, c for classification)

The call to train the function is class_.train(input,answer)

The call to test is class_.feed_forward(input)

#Installation

""" pip install QCNN """

#Sample Code

from NeuralNetwork import NeuralNetwork import pandas as pd import numpy as np

data = [] x = np.linspace(-1,1,401) y = np.sin(4*x) for point in range(len(x)): data.append([x[point],y[point]])

def shuffle_data(): train_data = data[:-50] test_data = data np.random.shuffle(train_data) train_data = pd.DataFrame(train_data) test_data = pd.DataFrame(test_data)

train_inputs = train_data[train_data.columns[0]].values
train_answers = train_data[train_data.columns[1]].values
test_inputs = test_data[test_data.columns[0]].values
test_answers = test_data[test_data.columns[1]].values

return train_inputs,train_answers,test_inputs,test_answers

nn = NeuralNetwork(2,2,2,3,.001,'r')

for epoch in range(1000): if epoch %10 ==0: print(epoch/100) normalized_data,inputs,test_data,testing_inputs = shuffle_data()

for i in range(int(len(normalized_data)/10)):

    nn.train(list(normalized_data.iloc[i]),inputs[i],epoch)

num_correct = 0 points = [] correct = [] for test in range(len(test_data)): #inputs = test_data[test] nn_output = nn.feed_forward(list(test_data.iloc[test])) points.append(nn_output[0]) output = np.argmax(nn_output) ti = testing_inputs[test]

correct.append(ti)


answer = np.argmax(ti)

if output == answer:
    num_correct+= 1

print(num_correct/len(testing_inputs)) plt.plot(points,label='Prediction') plt.plot(correct,label='Actual') plt.legend()

plt.show()