%matplotlib inline
import numpy as np
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
import matplotlib
import matplotlib.pyplot as plt
mnist = fetch_openml("mnist_784") # 加载数据集
help('sklearn')
Help on package sklearn:
NAME
sklearn
DESCRIPTION
Machine learning module for Python
==================================
sklearn is a Python module integrating classical machine
learning algorithms in the tightly-knit world of scientific Python
packages (numpy, scipy, matplotlib).
It aims to provide simple and efficient solutions to learning problems
that are accessible to everybody and reusable in various contexts:
machine-learning as a versatile tool for science and engineering.
See http://scikit-learn.org for complete documentation.
PACKAGE CONTENTS
__check_build (package)
_build_utils (package)
_config
_isotonic
base
calibration
cluster (package)
compose (package)
covariance (package)
cross_decomposition (package)
datasets (package)
decomposition (package)
discriminant_analysis
dummy
ensemble (package)
exceptions
externals (package)
feature_extraction (package)
feature_selection (package)
gaussian_process (package)
impute
isotonic
kernel_approximation
kernel_ridge
linear_model (package)
manifold (package)
metrics (package)
mixture (package)
model_selection (package)
multiclass
multioutput
naive_bayes
neighbors (package)
neural_network (package)
pipeline
preprocessing (package)
random_projection
semi_supervised (package)
setup
svm (package)
tests (package)
tree (package)
utils (package)
FUNCTIONS
clone(estimator, safe=True)
Constructs a new estimator with the same parameters.
Clone does a deep copy of the model in an estimator
without actually copying attached data. It yields a new estimator
with the same parameters that has not been fit on any data.
Parameters
----------
estimator : estimator object, or list, tuple or set of objects
The estimator or group of estimators to be cloned
safe : boolean, optional
If safe is false, clone will fall back to a deep copy on objects
that are not estimators.
config_context(**new_config)
Context manager for global scikit-learn configuration
Parameters
----------
assume_finite : bool, optional
If True, validation for finiteness will be skipped,
saving time, but leading to potential crashes. If
False, validation for finiteness will be performed,
avoiding error. Global default: False.
working_memory : int, optional
If set, scikit-learn will attempt to limit the size of temporary arrays
to this number of MiB (per job when parallelised), often saving both
computation time and memory on expensive operations that can be
performed in chunks. Global default: 1024.
Notes
-----
All settings, not just those presently modified, will be returned to
their previous values when the context manager is exited. This is not
thread-safe.
Examples
--------
>>> import sklearn
>>> from sklearn.utils.validation import assert_all_finite
>>> with sklearn.config_context(assume_finite=True):
... assert_all_finite([float('nan')])
>>> with sklearn.config_context(assume_finite=True):
... with sklearn.config_context(assume_finite=False):
... assert_all_finite([float('nan')])
... # doctest: +ELLIPSIS
Traceback (most recent call last):
...
ValueError: Input contains NaN, ...
get_config()
Retrieve current values for configuration set by :func:`set_config`
Returns
-------
config : dict
Keys are parameter names that can be passed to :func:`set_config`.
set_config(assume_finite=None, working_memory=None)
Set global scikit-learn configuration
.. versionadded:: 0.19
Parameters
----------
assume_finite : bool, optional
If True, validation for finiteness will be skipped,
saving time, but leading to potential crashes. If
False, validation for finiteness will be performed,
avoiding error. Global default: False.
.. versionadded:: 0.19
working_memory : int, optional
If set, scikit-learn will attempt to limit the size of temporary arrays
to this number of MiB (per job when parallelised), often saving both
computation time and memory on expensive operations that can be
performed in chunks. Global default: 1024.
.. versionadded:: 0.20
show_versions()
Print useful debugging information
DATA
__SKLEARN_SETUP__ = False
__all__ = ['calibration', 'cluster', 'covariance', 'cross_decompositio...
VERSION
0.20.3
FILE
/root/anaconda3/lib/python3.7/site-packages/sklearn/__init__.py
train_X,test_X,train_y,test_y = train_test_split(mnist.data,mnist.target,test_size = 0.15) #拆分数据集
print(train_y.shape,test_y.shape)
(59500,) (10500,)
fig = plt.figure()
k = 0
for i in np.arange(1):
grf_mat = train_X[i].reshape((28,28))
k = 231+ i
plt.subplot(k)
plt.imshow(grf_mat,cmap = matplotlib.cm.binary,interpolation = 'nearest')
plt.axis("off")
plt.title(train_y[i])
plt.show()
[外链图片转存失败(img-ZuUdVEot-1562899955977)(output_4_0.png)]
1、数据规整（假设数据无异常值、空值等清洗问题），仅对数据维度做出改变。
# 训练集规整数据格式
train_X = [np.reshape(x,(784,1)) for x in train_X]
train_y_temp = np.zeros((train_y.shape[0],10))
for i,j in zip(np.arange(train_y.shape[0]),train_y):
i = int(i)
j = int(j)
train_y_temp[i][j] = 1
train_y = [np.reshape(y,(10,1)) for y in train_y_temp]
# 测试集数据规整
test_X = [np.reshape(x,(784,1)) for x in test_X]
# 整合数据
train_data = list(zip(train_X,train_y))
test_data = list(zip(test_X,test_y))
print(len(test_data))
print(len(train_data))
10500
59500
2、构建神经网络模型
class Network(object):
# 初始化网络结构
def __init__(self,net_sizes):
self.size = net_sizes
self.layer_num = len(net_sizes)
self.weights = [np.random.randn(y,x) for x, y in zip(net_sizes[:-1],net_sizes[1:])]
self.baises = [np.random.randn(x,1) for x in net_sizes[1:]]
# 定义sigmoid函数
def sigmoid(x):
return 1.0/(1.0 + np.exp(-x))
# 定义sigmoid 函数导数
def sigmoid_prim(x):
y = Network.sigmoid(x)
return y*(1.0-y)
# 构建SGD
def SGD(self,train_data,test_data,epochs,mini_batch_size,rate):
if test_data is not None:
test_len = len(test_data)
n = len(train_data)
for i in np.arange(epochs):
np.random.shuffle(train_data)
mini_batchs = [train_data[k:k + mini_batch_size] for k in np.arange(0,n,mini_batch_size)]
for mini_batch in mini_batchs:
self.update_mini_batch(mini_batch,rate)
if test_data is not None:
print("Epochs {0} : {1}/{2}".format(i
, self.evaluate(test_data)
, test_len))
else:
print("Epoch {0} complete".format(i))
def update_mini_batch(self,mini_batch,rate):
nabla_w = [np.zeros(w.shape) for w in self.weights]
nabla_b = [np.zeros(b.shape) for b in self.baises]
for x,y in mini_batch:
detal_nabla_w,detal_nabla_b = self.backprop(x,y)
nabla_w = [nw + dnw for nw,dnw in zip(nabla_w,detal_nabla_w)]
nabla_b = [bw + dnb for bw,dnb in zip(nabla_b,detal_nabla_b)]
self.weights = [w - (rate/len(mini_batch))*nw for w,nw in zip(self.weights,nabla_w)]
self.baises = [b - (rate/len(mini_batch))*nb for b,nb in zip(self.baises,nabla_b)]
def backprop(self,x,y):
nabla_w = [np.zeros(w.shape) for w in self.weights]
nabla_b = [np.zeros(b.shape) for b in self.baises]
#记录每一个activation，与f(a)
activation = x # 用于保存带权输入
activations = [x]
zs = [] #输出值
for w,b in zip(self.weights,self.baises):
z = np.dot(w,activation) + b
zs.append(z)
activation = Network.sigmoid(z)
activations.append(activation)
# 反向传播
# 1、输出层到隐藏层的梯度通项
detal = self.cost_derivative(activations[-1],y)*Network.sigmoid_prim(zs[-1])
nabla_w[-1] = np.dot(detal,activations[-2].transpose())
nabla_b[-1] = detal
# 2、隐层到隐层的梯度
for i in np.arange(2,self.layer_num):
# 梯度通项
sp = Network.sigmoid_prim(zs[-i])
detal = np.dot(self.weights[-i+1].transpose(),detal)*sp
nabla_w[-i] = np.dot(detal,activations[-i-1].transpose())
nabla_b[-i] = detal
return nabla_w,nabla_b
def cost_derivative(self, output_activations, y):
output_activations = output_activations.astype(np.float64)
y = np.float64(y)
cost = (output_activations - y)
return cost
def evaluate(self, test_data):
test_results = [(np.argmax(self.feedforward(x)), y) for (x, y) in test_data]
return sum(int(int(x) == int(y)) for (x, y) in test_results)
def feedforward(self, a):
for b, w in zip(self.baises, self.weights):
a = Network.sigmoid(np.dot(w, a)+b)
return a
net = Network([784,100,50,30,10])