一小时培训之神经网络入门

系列培训目录

➡️神经网络(Neural Networks)⬅️

卷积神经网络(Convolutional Neural Networks)

循环神经网络(Recurrent Neural Networks)

生成对抗神经网络(Generative Adversarial Networks)

神经网络(Neural Networks)

最简的神经神经网络 — 一个神经元

《一小时培训之神经网络入门》 image

  • 组成:
    • 参数:用x表示
    • 权重:用w表示
    • 偏差:用b表示
    • 激活函数:用f(h)表示
  • 数学形式:
    《一小时培训之神经网络入门》 image
    其中f表示激活函数,通常用
    《一小时培训之神经网络入门》 image
    sigmoid值在0-1之间的数值很像概率适合做分类

《一小时培训之神经网络入门》 image

如何找出这样的函数?

方法:

  • 监督学习的方式训练:
    • 让机器找出使数据目标之间的误差最小的函数
      • 告诉机器衡量误差的函数
      • 梯度下降(Gradient Descent)更新神经元的权重(w),使得误差的方程最小

常用的误差函数:

  • 回归问题用:平方差之和(the sum of squared errors):

    《一小时培训之神经网络入门》 image

    其中: 《一小时培训之神经网络入门》 image
    是为了方便计算在求导时可去除平方,u是每行,j表示每列

  • 分类问题用:最小交叉墒(后面讲)
    《一小时培训之神经网络入门》 image

梯度下降 – 数学

用链式法则去求误差函数对于权重w的偏微分,用来更新神经网络的权重w
存在的问题:
只看梯度的话,会卡在局部最优值

《一小时培训之神经网络入门》 image

数学求法:
为了更新权重,就要求误差函数E对于权重w的偏导,乘以学习率来控制学习速度
η成为learning rate,表示每次更新权重w的步长,用来控制学习速度

《一小时培训之神经网络入门》 image

求误差函数E对于权重w的偏导的求法:
目标:

《一小时培训之神经网络入门》 image

链式法则:

《一小时培训之神经网络入门》 image

再用一次链式法则:

《一小时培训之神经网络入门》 image

带入后,再用一次链式法则:

《一小时培训之神经网络入门》 image

最后,做替换:

《一小时培训之神经网络入门》 image

最终结果:

《一小时培训之神经网络入门》 image

定义:

《一小时培训之神经网络入门》 image

梯度下降 – 代码实现

import numpy as np

def sigmoid(x):
    """
    Calculate sigmoid
    """
    return 1/(1+np.exp(-x))

def sigmoid_prime(x):
    """
    # Derivative of the sigmoid function
    """
    return sigmoid(x) * (1 - sigmoid(x))

learnrate = 0.5
x1,x2,x3,x4 = 1, 2, 3, 4
y = 0.5

# Initial weights
w1,w2,w3,w4 = 0.5, -0.5, 0.3, 0.1

### Calculate one gradient descent step for each weight
### Note: Some steps have been consilated, so there are
###       fewer variable names than in the above sample code

# TODO: Calculate the node's linear combination of inputs and weights
h = x1*w1+x2*w2+x3*w3+x4*w4

# TODO: Calculate output of neural network
y_hat = sigmoid(h)

# TODO: Calculate error of neural network
error = (y - y_hat)

# TODO: Calculate the error term
#       Remember, this requires the output gradient, which we haven't
#       specifically added a variable for.
error_term = error * sigmoid_prime(h)
# Note: The sigmoid_prime function calculates sigmoid(h) twice,
#       but you've already calculated it once. You can make this
#       code more efficient by calculating the derivative directly
#       rather than calling sigmoid_prime, like this:
# error_term = error * nn_output * (1 - nn_output)

# TODO: Calculate change in weights
del_w = learnrate * error_term * x

print('Neural Network output:')
print(nn_output)
print('Amount of Error:')
print(error)
print('Change in Weights:')
print(del_w)
Neural Network output:
0.689974481128
Amount of Error:
-0.189974481128
Change in Weights:
[-0.02031869 -0.04063738 -0.06095608 -0.08127477]

训练方法

迭代直到误差最小:

  1. 正向传播,获得预测值:沿着神经网络,矩阵点乘,计算出预测值\hat y
  2. 反向传播,获得每层的误差梯度:用\hat y计算误差函数,反向传播误差。
  3. 更新权重:根据误差更新权重

以预测研究生是否能入学为例 – 单个神经元版本

《一小时培训之神经网络入门》 image

读取原始数据

import numpy as np
import pandas as pd
admissions=pd.read_csv("entry_admission.csv")
admissions.head()

<div>
<table border=”1″ class=”dataframe”>
<thead>
<tr style=”text-align: right;”>
<th></th>
<th>admit</th>
<th>gre</th>
<th>gpa</th>
<th>rank</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>0</td>
<td>380</td>
<td>3.61</td>
<td>3</td>
</tr>
<tr>
<th>1</th>
<td>1</td>
<td>660</td>
<td>3.67</td>
<td>3</td>
</tr>
<tr>
<th>2</th>
<td>1</td>
<td>800</td>
<td>4.00</td>
<td>1</td>
</tr>
<tr>
<th>3</th>
<td>1</td>
<td>640</td>
<td>3.19</td>
<td>4</td>
</tr>
<tr>
<th>4</th>
<td>0</td>
<td>520</td>
<td>2.93</td>
<td>4</td>
</tr>
</tbody>
</table>
</div>

数据处理

# Make dummy variables for rank
data = pd.concat([admissions, pd.get_dummies(admissions['rank'], prefix='rank')], axis=1)
data = data.drop('rank', axis=1)

# Standarize features
for field in ['gre', 'gpa']:
    mean, std = data[field].mean(), data[field].std()
    data.loc[:,field] = (data[field]-mean)/std
    
# Split off random 10% of the data for testing
np.random.seed(42)
sample = np.random.choice(data.index, size=int(len(data)*0.9), replace=False)
data, test_data = data.ix[sample], data.drop(sample)

# Split into features and targets
features, targets = data.drop('admit', axis=1), data['admit']
features_test, targets_test = test_data.drop('admit', axis=1), test_data['admit']
features.head()

<div>
<table border=”1″ class=”dataframe”>
<thead>
<tr style=”text-align: right;”>
<th></th>
<th>gre</th>
<th>gpa</th>
<th>rank_1</th>
<th>rank_2</th>
<th>rank_3</th>
<th>rank_4</th>
</tr>
</thead>
<tbody>
<tr>
<th>209</th>
<td>-0.066657</td>
<td>0.289305</td>
<td>0</td>
<td>1</td>
<td>0</td>
<td>0</td>
</tr>
<tr>
<th>280</th>
<td>0.625884</td>
<td>1.445476</td>
<td>0</td>
<td>1</td>
<td>0</td>
<td>0</td>
</tr>
<tr>
<th>33</th>
<td>1.837832</td>
<td>1.603135</td>
<td>0</td>
<td>0</td>
<td>1</td>
<td>0</td>
</tr>
<tr>
<th>210</th>
<td>1.318426</td>
<td>-0.131120</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>1</td>
</tr>
<tr>
<th>93</th>
<td>-0.066657</td>
<td>-1.208461</td>
<td>0</td>
<td>1</td>
<td>0</td>
<td>0</td>
</tr>
</tbody>
</table>
</div>

targets.head()
209    0
280    0
33     1
210    0
93     0
Name: admit, dtype: int64

单神经元版本

def sigmoid(x):
    """
    Calculate sigmoid
    """
    return 1 / (1 + np.exp(-x))

# TODO: We haven't provided the sigmoid_prime function like we did in
#       the previous lesson to encourage you to come up with a more
#       efficient solution. If you need a hint, check out the comments
#       in solution.py from the previous lecture.

# Use to same seed to make debugging easier
np.random.seed(42)

n_records, n_features = features.shape
last_loss = None

# Initialize weights
weights = np.random.normal(scale=1 / n_features**.5, size=n_features)

# Neural Network hyperparameters
epochs = 1000
learnrate = 0.5

for e in range(epochs):#训练的 代 数
    del_w = np.zeros(weights.shape)
    for x, y in zip(features.values, targets):
        # Loop through all records, x is the input, y is the target

        # Note: We haven't included the h variable from the previous
        #       lesson. You can add it if you want, or you can calculate
        #       the h together with the output

        # TODO: Calculate the output
        output = sigmoid(np.dot(x,weights))

        # TODO: Calculate the error
        error = y-output

        # TODO: Calculate the error term
        error_term = error*output*(1-output)

        # TODO: Calculate the change in weights for this sample
        #       and add it to the total weight change
        del_w += error_term*x

    # TODO: Update weights using the learning rate and the average change in weights
    weights += learnrate*del_w

    # Printing out the mean square error on the training set
    if e % (epochs / 10) == 0:
        out = sigmoid(np.dot(features, weights))
        loss = np.mean((out - targets) ** 2)
        if last_loss and last_loss < loss:
            print("Train loss: ", loss, "  WARNING - Loss Increasing")
        else:
            print("Train loss: ", loss)
        last_loss = loss


# Calculate accuracy on test data
tes_out = sigmoid(np.dot(features_test, weights))
predictions = tes_out > 0.5
accuracy = np.mean(predictions == targets_test)
print("Prediction accuracy: {:.3f}".format(accuracy))
Train loss:  0.286196010415
Train loss:  0.257761346594
Train loss:  0.257722034703
Train loss:  0.257722749419   WARNING - Loss Increasing
Train loss:  0.257722752361   WARNING - Loss Increasing
Train loss:  0.257722752309
Train loss:  0.257722752309
Train loss:  0.257722752309   WARNING - Loss Increasing
Train loss:  0.257722752309   WARNING - Loss Increasing
Train loss:  0.257722752309   WARNING - Loss Increasing
Prediction accuracy: 0.725

神经网络 – 由神经元组成

通过非线性的激活函数的神经元组合起来就是神经网络。
能得出非线性的函数,从而具备找到各种各样函数的能力。

《一小时培训之神经网络入门》 image

数学形式:
矩阵相乘,每一隐含层是一个矩阵 (图待加上偏差)

《一小时培训之神经网络入门》 image

神经网络 – 代码表示

import numpy as np

def sigmoid(x):
    """
    Calculate sigmoid
    """
    return 1/(1+np.exp(-x))

# Network size
N_input = 4
N_hidden = 3
N_output = 2

np.random.seed(42)
# Make some fake data
X = np.random.randn(N_input)

weights_input_to_hidden = np.random.normal(0, scale=0.1, size=(N_input, N_hidden))
weights_hidden_to_output = np.random.normal(0, scale=0.1, size=(N_hidden, N_output))

# element-wise
# TODO: Make a forward pass through the network
hidden_layer_in = np.dot(X, weights_input_to_hidden)
hidden_layer_out = sigmoid(hidden_layer_in)

print('Hidden-layer Output:')
print(hidden_layer_out)

output_layer_in = np.dot(hidden_layer_out, weights_hidden_to_output)
output_layer_out = sigmoid(output_layer_in)

print('Output-layer Output:')
print(output_layer_out)
Hidden-layer Output:
[ 0.41492192  0.42604313  0.5002434 ]
Output-layer Output:
[ 0.49815196  0.48539772]

反向传播 – 将误差的梯度反向传播到神经网络的每个神经元用以更新权重w

反向传播的计算方法:
从最后一层的梯度计算,利用链式法则反向计算每一层梯度

《一小时培训之神经网络入门》 image

公式:
第j层的误差:

《一小时培训之神经网络入门》 image

这里的Σ表示如果下一层(第k层)有多个神经元,则反向传上来的误差要叠加起来
第j层的每个权重w的值:

《一小时培训之神经网络入门》 image

import numpy as np


def sigmoid(x):
    """
    Calculate sigmoid
    """
    return 1 / (1 + np.exp(-x))


x = np.array([0.5, 0.1, -0.2])
target = 0.6
learnrate = 0.5

weights_input_hidden = np.array([[0.5, -0.6],
                                 [0.1, -0.2],
                                 [0.1, 0.7]])

weights_hidden_output = np.array([0.1, -0.3])

## Forward pass
hidden_layer_input = np.dot(x, weights_input_hidden)
hidden_layer_output = sigmoid(hidden_layer_input)

output_layer_in = np.dot(hidden_layer_output, weights_hidden_output)
output = sigmoid(output_layer_in)

## Backwards pass
## TODO: Calculate error
error = target - output

# TODO: Calculate error gradient for output layer
del_err_output = error * output * (1 - output)
# TODO: Calculate change in weights for hidden layer to output layer
delta_weights_hidden_output = learnrate * del_err_output * hidden_layer_output


# TODO: Calculate error gradient for hidden layer
del_err_hidden = np.dot(del_err_output, weights_hidden_output) * \
                 hidden_layer_output * (1 - hidden_layer_output)
# TODO: Calculate change in weights for input layer to hidden layer
delta_weights_input_hidden = learnrate * del_err_hidden * x[:, None]

print('Change in weights for hidden layer to output layer:')
print(delta_weights_hidden_output)
print('Change in weights for input layer to hidden layer:')
print(delta_weights_input_hidden)

Change in weights for hidden layer to output layer:
[ 0.00804047  0.00555918]
Change in weights for input layer to hidden layer:
[[  1.77005547e-04  -5.11178506e-04]
 [  3.54011093e-05  -1.02235701e-04]
 [ -7.08022187e-05   2.04471402e-04]]

回顾训练方法

迭代直到误差最小:

  1. 正向传播,获得预测值:沿着神经网络,矩阵点乘,计算出预测值\hat y
  2. 反向传播,获得每层的误差梯度:用\hat y计算误差函数,反向传播误差。
  3. 更新权重:根据每层的误差更新每层的权重

以预测研究生是否能入学为例 – 神经网络版本

数据处理

import numpy as np
import pandas as pd

admissions = pd.read_csv('entry_admission.csv')

# Make dummy variables for rank
data = pd.concat([admissions, pd.get_dummies(admissions['rank'], prefix='rank')], axis=1)
data = data.drop('rank', axis=1)

# Standarize features
for field in ['gre', 'gpa']:
    mean, std = data[field].mean(), data[field].std()
    data.loc[:,field] = (data[field]-mean)/std
    
# Split off random 10% of the data for testing
np.random.seed(21)
sample = np.random.choice(data.index, size=int(len(data)*0.9), replace=False)
data, test_data = data.ix[sample], data.drop(sample)

# Split into features and targets
features, targets = data.drop('admit', axis=1), data['admit']
features_test, targets_test = test_data.drop('admit', axis=1), test_data['admit']

神经网络版本

import numpy as np

np.random.seed(21)

def sigmoid(x):
    """
    Calculate sigmoid
    """
    return 1 / (1 + np.exp(-x))


# Hyperparameters
n_hidden = 2  # number of hidden units
epochs = 900
learnrate = 0.005

n_records, n_features = features.shape
last_loss = None
# Initialize weights
weights_input_hidden = np.random.normal(scale=1 / n_features ** .5,
                                        size=(n_features, n_hidden))
weights_hidden_output = np.random.normal(scale=1 / n_features ** .5,
                                         size=n_hidden)

for e in range(epochs):
    del_w_input_hidden = np.zeros(weights_input_hidden.shape)
    del_w_hidden_output = np.zeros(weights_hidden_output.shape)
    for x, y in zip(features.values, targets):
        ## Forward pass ##
        # TODO: Calculate the output
        hidden_input = np.dot(x, weights_input_hidden)
        hidden_output = sigmoid(hidden_input)

        output = sigmoid(np.dot(hidden_output,
                                weights_hidden_output))

        ## Backward pass ##
        # TODO: Calculate the network's prediction error
        error = y - output

        # TODO: Calculate error term for the output unit
        output_error_term = error * output * (1 - output)

        ## propagate errors to hidden layer

        # TODO: Calculate the hidden layer's contribution to the error
        hidden_error = np.dot(output_error_term, weights_hidden_output)

        # TODO: Calculate the error term for the hidden layer
        hidden_error_term = hidden_error * hidden_output * (1 - hidden_output)

        # TODO: Update the change in weights
        del_w_hidden_output += output_error_term * hidden_output
        del_w_input_hidden += hidden_error_term * x[:,None]

    # TODO: Update weights
    weights_input_hidden += learnrate * del_w_input_hidden / n_records
    weights_hidden_output += learnrate * del_w_hidden_output / n_records

    # Printing out the mean square error on the training set
    if e % (epochs / 10) == 0:
        hidden_output = sigmoid(np.dot(x, weights_input_hidden))
        out = sigmoid(np.dot(hidden_output,
                             weights_hidden_output))
        loss = np.mean((out - targets) ** 2)

        if last_loss and last_loss < loss:
            print("Train loss: ", loss, "  WARNING - Loss Increasing")
        else:
            print("Train loss: ", loss)
        last_loss = loss

# Calculate accuracy on test data
hidden = sigmoid(np.dot(features_test, weights_input_hidden))
out = sigmoid(np.dot(hidden, weights_hidden_output))
predictions = out > 0.5
accuracy = np.mean(predictions == targets_test)
print("Prediction accuracy: {:.3f}".format(accuracy))
Train loss:  0.245943442947
Train loss:  0.224108177301
Train loss:  0.228908195703   WARNING - Loss Increasing
Train loss:  0.230352461418   WARNING - Loss Increasing
Train loss:  0.230651907986   WARNING - Loss Increasing
Train loss:  0.230865845199   WARNING - Loss Increasing
Train loss:  0.231183108301   WARNING - Loss Increasing
Train loss:  0.231499116961   WARNING - Loss Increasing
Train loss:  0.231737211823   WARNING - Loss Increasing
Train loss:  0.231882889013   WARNING - Loss Increasing
Prediction accuracy: 0.750

关于我:

linxinzhe,全栈工程师,目前供职于某世界500强银行的金融科技部门(人工智能,区块链)。

GitHub:https://github.com/linxinzhe

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    原文作者:linxinzhe
    原文地址: https://www.jianshu.com/p/b9c35ffaf848
    本文转自网络文章,转载此文章仅为分享知识,如有侵权,请联系博主进行删除。
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