Datasets: Banknote Authentication | Pima Indians Diabetes Database .
____________________________________________________________________________"""
np.random.seed(1)
"""_________________________________________________________________________"""
if __name__ == '__main__':
ds_id = int(input('Enter 0 for Banknote and 1 for Diabetes: '))
if not ds_id:
''' Banknote '''
features_train, labels_train, features_test, labels_test,\
yes_examples, no_examples = pp.banknote('banknote.csv')
h = 7
else:
''' Diabetes '''
features_train, labels_train, features_test, labels_test,\
yes_examples, no_examples = pp.diabetes('diabetes.csv')
h = 15
# OPT: h = int(input('Enter the number of the hidden layer neurons: '))
# OPT: epochs = int(input('Enter the number of SGD epochs: '))
# OPT: learning_rate = int(input('Enter the SGD learning rate: '))
nn = ann.Network((features_train.shape[1], h + 1, 1),
(af.Sigmoid, af.Sigmoid))
nn.train(features_train,
labels_train,
loss=lf.CE,
epochs=5,
learning_rate=1e-2)
status = confusion_matrix(yes_examples, no_examples, nn, features_test,
labels_test, ds_id)
def main():
train_label_data = read_file('NeuralNetwork/train-labels.idx1-ubyte')
magic_number = int.from_bytes(train_label_data[0:4], 'big')
number_of_items = int.from_bytes(train_label_data[4:8], 'big')
train_label_data = train_label_data[8:]
print('magic_number:',magic_number)
print('number_of_items:',number_of_items)
print('--------------')
train_activation_data = read_file('NeuralNetwork/train-images.idx3-ubyte')
magic_number = int.from_bytes(train_activation_data[0:4], 'big')
number_of_items = int.from_bytes(train_activation_data[4:8], 'big')
number_of_rows = int.from_bytes(train_activation_data[8:12], 'big')
number_of_columns = int.from_bytes(train_activation_data[12:16], 'big')
train_activation_data = train_activation_data[16:]
print('magic_number:', magic_number)
print('number_of_items:', number_of_items)
print('number_of_rows:', number_of_rows)
print('number_of_columns:', number_of_columns)
test_label_data = read_file('NeuralNetwork/train-labels.idx1-ubyte')
test_label_data = test_label_data[8:]
test_activation_data = read_file('NeuralNetwork/train-images.idx3-ubyte')
test_activation_data = test_activation_data[16:]
offset = 0
image_size = 784
image_count = 20
# for image_index in range(image_count):
# image = Image.new('L', (28, 28), 'white')
# image.frombytes(activation_data[offset:offset+image_size])
# offset += image_size
# image_name = 'image_' + str(image_index) + '.bmp'
# image.save('NeuralNetwork/' + image_name)
# runs = 3
lrate = 0.001
batch_size = 32
epochs = 30
train_activation_data = convert_activation_data(train_activation_data, image_size)
train_label_data = convert_label_data(train_label_data)
test_activation_data = convert_activation_data(test_activation_data, image_size)
test_label_data = convert_label_data(test_label_data)
#f = open("NeuralNetwork/mnist_model_784_16_16_10.tsv", "w")
#f.write("epoch\tcost\tlrate\tbatchsize\trun\n")
#lrates = [float(x)/200 for x in range(2,21)]
#batch_sizes = [32,64]
network = ANN.Network([784,16,16,10]) #for each run, initialize the network
#load_model('NeuralNetwork/model.txt', network)
for epoch in range(epochs):
cost = 0
for i in range(100):
cost += network.cost(test_activation_data[i], test_label_data[i])
cost /= 100
print('epoch:', epoch, 'cost:', cost, 'lrate:', lrate, 'batch-size:', batch_size)
mini_batch_vectors = create_mini_batch_vectors(number_of_items, batch_size)
for mini_batch_vector in mini_batch_vectors:
activations, labels = create_mini_batch(train_activation_data, train_label_data, mini_batch_vector)
for a, y in zip(activations, labels):
network.forward_pass(a)
network.backward_pass(y)
network.update_weights(lrate, len(mini_batch_vector))
network.update_biases(lrate, len(mini_batch_vector))
#f.write(str(epoch+1)+"\t"+str(cost)+"\t"+str(lrate)+"\t"+str(batch_size)+"\t"+str(run+1)+"\n")
#f.close()
#save_model('NeuralNetwork/model.txt', network.weights, network.biases)
for i in range(3):
print(network.forward_pass(test_activation_data[i]))
print(network.layers[0].update_activation(test_activation_data[i]))
print(test_label_data[i])
print(network.cost(test_activation_data[i], test_label_data[i]))
print("----------------")
from __future__ import division
import os, wave, struct
import lib
import numpy as np
import ANN
if __name__ == '__main__':
index = 0
chunk = 256
n = ANN.Network(14, 30)
mp = ANN.SelfOrganizingMap(n)
try:
files = os.listdir("../cis 830/wav")
for filename in files:
try:
wav = wave.open("../cis 830/wav/" + filename)
bin = wav.readframes(wav.getnframes())
data = []
for i in range(0, len(bin)):
if i % 2 != 0:
continue
data.append(struct.unpack("<h", bin[i:i + 2])[0])
rate, chunk = (wav.getframerate(), 256)
duration = (len(data) / rate) * 1000
featureVector = []
for i in range(0, int(duration / 10)):
frame = data[i * 10:(i * 10) + 30]
