def main(argv):
if args.seed:
np.random.seed(args.seed)
map = Map(MAP_WIDTH, MAP_HEIGHT)
net = NeuralNetwork(2, args.layer_neurons, 1, args.hidden_layers, args.bias)
print net
if args.train:
# тренировочные данные
train_d0, train_d1 = map.dataset(0, MAP_WIDTH + MAP_HEIGHT), \
map.dataset(1, MAP_WIDTH + MAP_HEIGHT)
td0 = np.array([[0]] * train_d0.shape[0], dtype=float)
td1 = np.array([[1]] * train_d1.shape[0], dtype=float)
t = np.concatenate((td0, td1), axis=0) # уже нормализован
# вход
x = np.concatenate((train_d0, train_d1), axis=0)
x_normalized = x / np.amax(x, axis=0)
print 'Training...'
if args.logging:
with open('training.log', 'w') as f:
for epoch in xrange(args.epochs):
f.write('Epoch {}\n'.format(epoch))
f.write("Input:\n{}\n".format(x_normalized.T))
f.write("Actual Output:\n{}\n".format(t.T))
f.write("Predicted Output:\n{}\n".format(np.round(net.forward(x_normalized).T)))
f.write("Loss:\n{}\n\n".format(str(np.mean(np.square(t - net.forward(x_normalized))))))
net.train(x_normalized, t)
else:
for epoch in xrange(args.epochs):
net.train(x_normalized, t, args.alpha, args.train_speed)
print "Saving weights..."
net.save_weights(W_PREFIX)
print 'Done.'
else:
train_d0 = train_d1 = np.array([])
if os.path.exists('{}_0.w.txt'.format(W_PREFIX)):
print "Loading weights..."
net.load_weights(W_PREFIX)
print 'Done.'
else:
print "No weights were found!"
if args.seed:
np.random.seed(args.seed + 1)
# вход
zds0, zds1 = np.random.randint(2, 20), np.random.randint(2, 20)
d0, d1 = map.dataset(0, zds0), map.dataset(1, zds1)
x = np.concatenate((d0, d1), axis=0)
x_normalized = x / np.amax(x, axis=0)
# ожидаемые данные для проверки
td0 = np.array([[0]] * d0.shape[0], dtype=float)
td1 = np.array([[1]] * d1.shape[0], dtype=float)
t = np.concatenate((td0, td1), axis=0) # уже нормализован
# выход
y = np.round(net.predict(x_normalized))
if args.verbose:
print "Input:"
print x
print "Output (Expected):"
print t
print "Output (Actual):"
print y
res = (y == t)
if res.all():
print "\nAll Good!"
else:
print "{}% are good!".format(res.sum() * 100 / len(res))
if args.plotting:
# фильтрация 'попаданий' и 'промахов'
good = []
bad = []
for i, v in enumerate(res):
if v:
good.append(x[i])
else:
bad.append(x[i])
map.plot(np.array(good), np.array(bad), train_d0, train_d1, args.plot_name)
Z_train = res.reshape(Xn * Yn, 1)
Z_train = remap(Z_train, np.min(Z_train), np.max(Z_train), 0.01, 0.99)
Z_ideal = f(X, Y)
epochs = 2000
err = []
for i in range(epochs):
err.append(np.max(network.train(XY_train, Z_train)))
if i % 100 == 0:
print("running " + str(i) + " epoch")
print("error on end of train = " + str(err[-1]))
XY_pred = np.stack((X.ravel(), Y.ravel()), axis=-1)
Z_predicted = network.predict(XY_pred).reshape(Xn, Yn) # XY_train
Z_predicted = remap(Z_predicted, 0.05, 0.95, np.min(Z_train), np.max(Z_train))
fig = plt.figure(0)
ax = fig.gca(projection='3d')
plot = ax.plot_wireframe(X, Y, Z_ideal, color='red', linewidth=1)
fig.suptitle('Input function', fontsize=16)
fig2 = plt.figure(1)
ax = fig2.gca(projection='3d')
plot2 = ax.plot_wireframe(X, Y, Z_predicted, color='gray', linewidth=0.5)
fig2.suptitle('Network approximation cfg:' + str(config) + "\n avg err = {:7f}".format(err[-1]), fontsize=16)
err_fig = plt.figure(2)
ax = err_fig.add_subplot(111)
ax.plot(range(epochs), err)
import numpy as np
import matplotlib.pyplot as plt
from nnet import NeuralNetwork
import activation_functions as funcs
config = [1, 5, 5, 1]
net = NeuralNetwork(config,
learning_rate=0.1,
act_func=funcs.sigmoid,
df_act_func=funcs.df_sigmoid)
def f(x):
return x**2
vf = np.vectorize(f)
x = np.array(np.linspace(-1, 1, num=10), ndmin=2)
y = vf(x)
epochs = 5000
err = net.fit_raw(x, y, epochs, True, 10)
test_sample = np.array([0.1])
y_pred = net.predict(test_sample)
plt.plot(np.array(range(epochs) + 1), np.array(err))
plt.show()
validate_file = open("../mnist_data/mnist_test_10.csv")
validate_list = validate_file.readlines()
validate_file.close()
scorecard = []
for i in range(len(validate_list)):
raw_values = validate_list[i].split(',')
label = raw_values[0]
x = np.asfarray(raw_values[1:])
x = remap(x, 0, 255, 0.01, 0.99)
y = np.zeros(10) + 0.01
y[int(label)] = 0.99
y_pred = net.predict(x)
# plt.imshow(x.reshape((28, 28)), cmap='Greys', interpolation=None)
# plt.suptitle(label)
# plt.show()
# print("res " + str(y_pred))
pred_num = np.argmax(y_pred)
if pred_num == int(label):
scorecard.append(1)
else:
scorecard.append(0)
print(label + " predicted as " + str(pred_num))
# accuracy