def training_batch(self):
start1 = datetime.datetime.now()
start_time = datetime.datetime.now()
mnist_net_batch = network_class.Network([(196), (150), (10)],
weights=self.params[1],
bias=self.params[2],
activation_function="sigmoid",
initilizer="predefined",
dropout=0.0)
for i in range(1000):
sample = random.sample(range(len(self.training_images)), 60)
images = []
labels = []
for m in range(len(sample)):
x = self.training_images[sample[m]]
y = self.training_labels[sample[m]]
images.append(x)
labels.append(y)
mnist_net_batch.train_batch(images, labels)
mnist_net_batch.save_params('weigths_after_test_2')
end1 = datetime.datetime.now()
self.write_time(start1, end1, start_time)
def training_single(self):
start3 = datetime.datetime.now()
start_time = datetime.datetime.now()
mnist_net_single = network_class.Network([(196), (150), (10)],
weights=self.params[1],
bias=self.params[2],
activation_function="sigmoid",
initilizer="predefined",
dropout=0.0)
for ind in range(len(self.training_images)):
x = self.training_images[ind]
y = self.training_labels[ind]
mnist_net_single.test_train_single(x, y)
mnist_net_single.save_params('weights_after_test_1')
end3 = datetime.datetime.now()
self.write_time(start3, end3, start_time)
def training_rand(self):
start2 = time.time()
start_time = datetime.datetime.now()
mnist_net_rand = network_class.Network([(196), (150), (10)],
weights=self.params[1],
bias=self.params[2],
activation_function="sigmoid",
initilizer="predefined",
dropout=0.0)
for i in range(80000):
ind = random.randint(0, len(self.training_images))
x = self.training_images[ind]
y = self.training_labels[ind]
mnist_net_rand.test_train_single(x, y)
mnist_net_rand.save_params('weigths_after_test_3')
end2 = time.time()
self.write_time(start2, end2, start_time)
def main(self):
dt = np.dtype("Float64")
# ----- Flower Example 2-1 Network
#
# wei = [np.matrix([[0.9, 0.8]], dt)]
# bia = [np.matrix([[1]], dt)]
# a = network_class.Network([(2), (2), (1)], weights= wei, bias=bia, activation_function="sigmoid", initilizer="xavier_sigmoid")
#
# data = [[3, 1.5, 1], [2, 1, 0], [4, 1.5, 1], [3, 1, 0], [3.5, .5, 1], [2, .5, 0], [5.5, 1, 1], [1, 1, 0]]
# mysteriy = []
#
# costs = []
#
# for i in range(19999):
# ind = np.random.randint(len(data))
# point = data[ind]
# a.test_train_single([[point[0]], [point[1]]], [point[2]])
# costs.append(a.cost())
#
# graph_x = []
# graph_y = []
#
# graph_x_r = []
# graph_y_r = []
#
# graph_x_b = []
# graph_y_b = []
#
# for m in range(len(data)):
# graph_x.append(data[m][0])
# graph_y.append(data[m][1])
#
# for y in range(len(graph_x)):
# pred = a.test_info([[graph_x[y]],[graph_y[y]]], [data[y][2]])
# if pred.item(0) > 0.5:
# graph_x_r.append(graph_x[y])
# graph_y_r.append(graph_y[y])
# else:
# graph_x_b.append(graph_x[y])
# graph_y_b.append(graph_y[y])
#
# plt.plot(graph_x_r, graph_y_r, 'ro')
# plt.plot(graph_x_b, graph_y_b, 'bo')
#
# plt.show()
#
# plt.plot(costs)
# plt.show()
#
# ----- Training a simple 1-1-1 Network
# x = [[0.5]]
# a.test_train_single(x, [[0]])
# a.test_info(x, [[0]])
# ------ Training with 4 Inputs, 2 Hidden Layers, 1 Output
# x = self.create_training_samples()
#
# print("Hallo")
# print(len(x))
# for i in random.sample(x, 10000):
# a.test_train(i, self.create_target(i))
#
# a.print_nn_info()
#
# for i in range(20):
# y = self.create_batch(x)
# a.train_batch(y[0],y[1])
#
#
# im = [[0.9], [0.2], [0.3], [0.0]]
# aus = [[0.3], [0.5], [0.1], [0.5]]
#
# print("\n\n\n--------------")
#
# a.test_info(im, self.create_target(im))
# a.test_info(aus, self.create_target(aus))
# #------ Binary 4-Digits --> ODD/EVEN
#
b = network_class.Network([(4), (3), (1)],
weights=None,
bias=None,
activation_function="sigmoid",
initilizer="xavier_relu",
dropout=0.0)
data_b = []
for first in range(2):
for second in range(2):
for third in range(2):
for fourth in range(2):
data_b.append([[first], [second], [third], [fourth]])
print(len(data_b))
x = random.sample(
data_b, 14
) # <--- Take only a small amount of numbers train with these. 8 of 16 is very good!
costs_b = []
# # Training in Batch
# for i in range(5000):
# tar = []
# y = random.sample(data_b, 4)
# for num in y:
# tar.append([eval("0b" + str(num[0][0]) + str(num[1][0]) + str(num[2][0]) + str(num[3][0])) % 2])
# b.train_batch(y, tar)
# costs_b.append(b.cost())
# # TRAINING-SINGLE
for i in range(10000):
ind = np.random.randint(len(x))
point = x[ind]
b.test_train_single(point, [
eval("0b" + str(point[0][0]) + str(point[1][0]) +
str(point[2][0]) + str(point[3][0])) % 2
])
costs_b.append(b.cost())
#
values = []
#params = self.read_params('test.txt')
#test = network_class.Network(params[0], weights=params[1], bias=params[2], activation_function="sigmoid", initilizer="predefined")
for data in data_b:
values.append(b.test(data).item(0))
plt.plot(costs_b)
plt.show()
plt.plot(values)
plt.show()
def main(self):
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# ----- MAX POOLING OF IMAGES (2,2) ------
for img in mnist.train.images:
self.training_images.append(
np.matrix(
skimage.measure.block_reduce(
np.matrix(img).reshape((28, 28)), (2, 2),
np.max)).flatten().transpose())
for lab in mnist.train.labels:
self.training_labels.append(np.matrix(lab).transpose())
for img in mnist.test.images:
self.test_images.append(
np.matrix(
skimage.measure.block_reduce(
np.matrix(img).reshape((28, 28)), (2, 2),
np.max)).flatten().transpose())
for lab in mnist.test.labels:
self.test_labels.append(np.matrix(lab).transpose())
# # ----- SHOW IMAGES OF NUMBERS ------
#
# test1 = np.array(mnist.train.images[986]).reshape((28,28))
# test1_down = self.training_images[986].reshape(14, 14)
#
# print(self.training_labels[986])
# img = plt.imshow(test1)
# plt.show()
# img = plt.imshow(test1_down)
# plt.show()
# self.params = self.read_params('start_weights_for_test.txt')
percentage = 0
start3 = datetime.datetime.now()
start_time = datetime.datetime.now()
mnist_net_single = network_class.Network([(196), (100), (10)],
weights=None,
bias=None,
activation_function="sigmoid",
initilizer="random",
dropout=0.0)
acc1 = []
acc2 = []
for ind in range(10000):
ind = random.randint(0, 55000)
percentage = percentage + 1 / 10000
if ind % 250 == 0:
error1 = 0
error2 = 0
for i in range(10000):
x = mnist_net_single.test(self.test_images[i])
first = np.argmax(x)
seccond = np.argmax(np.delete(x, first))
if seccond >= first:
seccond += 1
y = np.argmax(self.test_labels[i])
if first != y:
error1 += 1
if seccond != y:
error2 += 1
acc1.append(1 - error1 / 10000)
acc2.append(1 - error2 / 10000)
x = self.training_images[ind]
y = self.training_labels[ind]
mnist_net_single.test_train_single(x, y)
print('\n' + str(percentage) + '%\n')
mnist_net_single.save_params('weights_after_test_9')
end3 = datetime.datetime.now()
self.write_time(start3, end3, start_time)
self.write_acc(acc1)
self.write_acc(acc2)
def main(self):
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# ----- MAX POOLING OF IMAGES (2,2) ------
for img in mnist.train.images:
self.training_images.append(
np.matrix(
skimage.measure.block_reduce(
np.matrix(img).reshape((28, 28)), (2, 2),
np.max)).flatten().transpose())
for lab in mnist.train.labels:
self.training_labels.append(np.matrix(lab).transpose())
# # ----- SHOW IMAGES OF NUMBERS ------
#
# test1 = np.array(mnist.train.images[986]).reshape((28,28))
# test1_down = self.training_images[986].reshape(14, 14)
#
# print(self.training_labels[986])
# img = plt.imshow(test1)
# plt.show()
# img = plt.imshow(test1_down)
# plt.show()
self.params = self.read_params('start_weights_for_test.txt')
# ------ Starting Threads ----------
# t1 = threading.Thread(target=self.training_single)
# t2 = threading.Thread(target=self.training_batch)
# t3 = threading.Thread(target=self.training_rand)
#
# t1.start()
# t2.start()
# t3.start()
test_images = []
test_labels = []
parameters = self.read_params(
'weigths_after_test_2_2018-07-06_030533.txt')
for img in mnist.test.images:
test_images.append(
np.matrix(
skimage.measure.block_reduce(
np.matrix(img).reshape((28, 28)), (2, 2),
np.max)).flatten().transpose())
for lab in mnist.test.labels:
test_labels.append(np.matrix(lab).transpose())
mnist_net = network_class.Network([(196), (150), (10)],
weights=parameters[1],
bias=parameters[2],
activation_function="sigmoid",
initilizer="predefined",
dropout=0.0)
error1 = 0
error2 = 0
for i in range(10000):
x = mnist_net.test(test_images[i])
first = np.argmax(x)
seccond = np.argmax(np.delete(x, first))
if seccond >= first:
seccond += 1
y = np.argmax(test_labels[i])
if first != y:
error1 += 1
if seccond != y:
error2 += 1
print("Error")
print(x)
print(first, seccond)
print(y)
img = plt.imshow(test_images[i].reshape(14, 14))
plt.show()
print(error1 / 10000)
print(error2 / 10000)