UI/UX demo
UI/UX demo
think.py sample output
breast cancer data fed through neural network, graph is of accuracy of neural network over the number of inputs that were received.
diabetes data fed through neural network, graph is of accuracy of neural network over the number of inputs that were received.
heart disease data fed through neural network, graph is of accuracy of neural network over the number of inputs that were received.
cervical cancer data fed through neural network, graph is of accuracy of neural network over the number of inputs that were received.

This is the main driver of the project. It's written in Python and calls on Tensor Flow for machine learning through a neural network. All data, code, and outputs are available at the link provided.

```from keras.models import Sequential from keras.layers import Dense import numpy import csv import matplotlib.pyplot as plt

fix random seed for reproducibility

numpy.random.seed(7)

load pima indians dataset

dataset = numpy.loadtxt("./data/new.data.csv", delimiter=",")

split into input (X) and output (Y) variables

X = dataset[:,0:13] Y = dataset[:,14]

create model

model = Sequential() model.add(Dense(17, input_dim=13, activation='relu')) model.add(Dense(13, activation='relu')) model.add(Dense(1, activation='sigmoid'))

Compile model

optimizer = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)

model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])

epochs = [150]

batch_sizes = [10]

losses = ['mean_squared_error', 'mean_absolute_error', 'mean_absolute_percentage_error', 'mean_squared_logarithmic_error', 'squared_hinge', 'hinge', 'categorical_hinge', 'logcosh', #'categorical_crossentropy', #'sparse_categorical_crossentropy', 'binary_crossentropy', 'kullback_leibler_divergence', 'poisson', 'cosine_proximity']

optimizers = ['SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Adamax', 'Nadam'] #'TFOptimizer']

with open('./data/accuracy.csv','a') as f1: writer=csv.writer(f1, delimiter=',',lineterminator='\n',) writer.writerow(["Run Start"]) writer.writerow(['loss', 'optimizer', 'epoch', 'batch_size', 'accuracy'])

Iterator used to test accuracies of different losses and optimizers

for i in range(len(losses)):

for j in range(len(optimizers)):

for k in range(len(epochs)):

for l in range(len(batch_sizes)):

print(losses[i])

print(optimizers[j])

model.compile(loss=losses[i], optimizer=optimizers[j], metrics=['accuracy'])

results = model.fit(X, Y, epochs=epochs[k], batch_size=batch_sizes[l])

# evaluate the model

scores = model.evaluate(X, Y)

print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

with open('./data/accuracy.csv','a') as f1:

writer=csv.writer(f1, delimiter=',',lineterminator='\n',)

writer.writerow([losses[i], optimizers[j], epochs[k], batch_sizes[l], scores[1]*100])

model.compile(loss='binary_crossentropy', optimizer='Adam', metrics=['accuracy'])

results = model.fit(X, Y, epochs=1500, batch_size=50, verbose=2)

evaluate the model

scores = model.evaluate(X, Y) print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

calculate predictions

predictions = model.predict(X)

print(predictions)

round predictions

rounded = [round(x[0]) for x in predictions]

print(rounded)

with open('./data/output.csv','w') as f1: writer=csv.writer(f1, delimiter=',',lineterminator='\n',) for i in range(len(predictions)): writer.writerow([str(predictions[i]).replace('[','').replace(']',''), rounded[i]])

with open('./data/accuracy2.csv','w') as f1: writer=csv.writer(f1, delimiter=',',lineterminator='\n',) for i in range(len(results.history['acc'])): writer.writerow([results.history['acc'][i]])