from book_dir.dataset.mnist import load_mnist
import numpy as np
import matplotlib.pyplot as plt
#hyper params
lr = 0.001
batch_size = 100
iterations = 20000
print_iterations = 100
#其他的比如hidden这些不废话了，直接写死了
#init net
net = TwoLayerNet(input_size = 784, hidden_size = 50, output_size = 10)
#load data
((x_train,t_train),(x_test,t_test)) = load_mnist(normalize = True, flatten = True, one_hot_label = True)
loss_history = []
accuracy_history = []
#iterate train
for i in range(iterations):
mask = np.random.choice(x_train.shape[0],batch_size)
x_batch = x_train[mask]
t_batch = t_train[mask]
grads = net.gradient(x_batch,t_batch)
for k in net.params:
net.params[k] -= lr * grads[k]
if i % print_iterations == 0:
loss = net.loss(x_batch,t_batch)#暂时用batch来打印一下
def get_data():
# (x_train,t_train),(x_test,t_test) = load_mnist(normalize=True,flatten=True,one_hot_label=False)
_,(x_test,t_test) = load_mnist(normalize=True,flatten=True,one_hot_label=False)
return x_test,t_test
import numpy as np
from book_dir.dataset.mnist import load_mnist
from PIL import Image
def img_show(img):
pil_img = Image.fromarray(img)#np.uint8(img)
print(type(pil_img))
pil_img.show()
((train_x,train_y),(test_x,test_y)) = load_mnist()
print(type(train_x),train_x.shape)
print(type(train_y),train_y.shape)#not onehot
print(train_y[:10])
print(test_x.shape)
print(test_y.shape)
((train_x,train_y),(test_x,test_y)) = load_mnist(normalize=False,flatten = True)
img = train_x[0]
label = train_y[0]
print(img.shape)
# img_reshaped = img.reshape(28,28)
img_reshaped = img.reshape(int(np.sqrt(len(img))), int(np.sqrt(len(img))))
print(img_reshaped.shape)
print(img.shape)
img_show(img_reshaped)
#计算损失函数时必须将所有的训练数据作为对象。也就是说，如果训练数据 有 100 个的话，我们就要把这 100 个损失函数的总和作为学习的指标。
#神经网络的学习也是从训练数据中选出一批数据(称为mini-batch,小 批量)，然后对每个 mini-batch 进行学习。比如，从 60000 个训练数据中随机 选择 100 笔，再用这 100 笔数据进行学习。这种学习方式称为 mini-batch 学习。
import numpy as np
import tensorflow as tf
import sys, os
sys.path.append(os.pardir)
from book_dir.dataset.mnist import load_mnist
(x_train, t_train), (x_test, t_test) = load_mnist(one_hot_label=True)
train_size = x_train.shape[0]
batch_size = 10
batch_mask = np.random.choice(train_size, batch_size)
x_batch = x_train[batch_mask]
t_batch = t_train[batch_mask]
def cross_entropy_error(y, t):
if y.ndim == 1:
t = t.reshape(1, t.size)
y = y.reshape(1, y.size)
batch_size = y.shape[0]
return -np.sum(t * np.log(y + 1e-7)) / batch_size
#在进行神经网络的学习时，不能将识别精度作为指标。因为如果以识别精度为指标，则参数的导数在绝大多数地方都会变为 0。
#识别精度对微小的参数变化基本上没有什么反应，即便有反应，它的值也是不连续地、突然地变化。
#出于相同的原因，如果使用阶跃函数作为激活函数，神经网络的学习将无法进行。
#如果使用了阶跃函数，那么即便将损失函数作为指标，参数的微 小变化也会被阶跃函数抹杀，导致损失函数的值不会产生任何变化。