def go():
import time
######################### EPOCH BATCH SIZE
EPOCH = 1
BATCH_SIZE = 256
######################### Read Data
# trainset, valset, testset = load_mnist()
train_images, train_labels = load_mnist(kind='train')
test_images, test_labels = load_mnist(kind='t10k')
# ######################### Network
# net = FCNet()
# #
# # ######################### Compile
# net.compile(optimizer='sgd', loss='cross_entropy')
# #
# # ######################### Train FC
# st = time.time()
# net.fit(train_images, train_labels, batch_size=BATCH_SIZE, epochs=EPOCH, verbose=1)
# et = time.time()
# print('time cost: ', et - st)
# print(u'当前进程的内存使用：%.4f GB' % (psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024 / 1024))
#
# ######################### Evaluate FC
# train_acc, train_loss = net.evaluate(train_images, train_labels, batch_size=500)
#
# test_acc, test_loss = net.evaluate(test_images, test_labels, batch_size=500)
# print('#' * 10, 'Fully Connected Network')
# print(f'Train acc -> {train_acc}, Train loss -> {train_loss}')
# print(f'Test acc -> {test_acc}, Test loss -> {test_loss}')
print('Start Training ConvNet---')
######################### Network
net = ConvNet()
######################### Compile
net.compile(optimizer='sgd', loss='cross_entropy', lr=0.01)
######################### Train Conv
st = time.time()
net.fit(train_images,
train_labels,
batch_size=BATCH_SIZE,
epochs=EPOCH,
verbose=1)
et = time.time()
print('time cost: ', et - st)
print(u'当前进程的内存使用：%.4f GB' %
(psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024 / 1024))
# ######################### Evaluate FC
train_acc, train_loss = net.evaluate(train_images,
train_labels,
batch_size=500)
test_acc, test_loss = net.evaluate(test_images,
test_labels,
batch_size=500)
print('#' * 10, 'Convolutional Network')
print(f'Train acc -> {train_acc}, Train loss -> {train_loss}')
print(f'Test acc -> {test_acc}, Test loss -> {test_loss}')
from network import ConvNet
import numpy as np
from dataset import getDataset
dataset = getDataset()
print(dataset["balance_train"])
conv = ConvNet(dataset)
conv.fit(dataset["one_hot_train"],
dataset["one_hot_label_train"],
dataset["one_hot_validation"],
dataset["one_hot_label_validation"],
graphics=True)
print("Final accuracy:")
print(" " + str(
conv.computeAccuracy(dataset["one_hot_validation"],
dataset["labels_validation"])))
print("Friends:")
conv.classify(dataset["one_hot_friends"])
#
# F = np.zeros((4, 2, 2))
# # print(F[:, :, 0])
# # print(F[:, :, 0].shape)
# F[:, :, 0] = [[1, 2], [3, 4], [5, 6], [7, 8]]
# F[:, :, 0] = [[1, 2], [3, 4], [5, 6], [7, 8]]
# # print(F[:, :, 0])
# # print(F[:, :, 1])
# # print(F)
#