python123第四周课后作业_吴恩达深度学习第一课第四周课后作业1参考

**第四周课后作业第一部分,对于作业环境安装不知道的可以看一下这里:

http://blog..net/liuzhongkai123/article/details/78766351

**

Building your Deep Neural Network: Step by Step

符号说明

Notation:

- Superscript [l]denotes a quantity associated with the lthlayer.

- Example:a[L] is the Lth layer activation. W[L] andb[L]are the Lth layer parameters.

- Superscript(i)denotes a quantity associated with the ith example.

- Example:x(i) is theith training example.

- Lowerscript idenotes theithentry of a vector.

- Example: a[l]idenotes theith entry of the lthlayer’s activations).

1 - Packages

导入被作业需要的包和模块

Let’s first import all the packages that you will need during this assignment.

- numpy is the main package for scientific computing with Python.

- matplotlib is a library to plot graphs in Python.

- dnn_utils provides some necessary functions for this notebook.

- testCases provides some test cases to assess the correctness of your functions

- np.random.seed(1) is used to keep all the random function calls consistent. It will help us grade your work. Please don’t change the seed.

导入包和模块

import numpy as np

import h5py

import matplotlib.pyplot as plt

from testCases_v2 import *

from testCases_v3 import *

from dnn_utils_v2 import sigmoid, sigmoid_backward, relu, relu_backward

设置绘图配置文件参数

%matplotlib inline 设置绘图的配置文件

plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots

plt.rcParams['image.interpolation'] = 'nearest'

plt.rcParams['image.cmap'] = 'gray'

设置随机数种子,保证随数的连续

np.random.seed(1) #设置随机数种子

2 - Outline of the Assignment

To build your neural network, you will be implementing several “helper functions”. These helper functions will be used in the next assignment to build a two-layer neural network and an L-layer neural network. Each small helper function you will implement will have detailed instructions that will walk you through the necessary steps. Here is an outline of this assignment, you will:

Initialize the parameters for a two-layer network and for an L-layer neural network.

Implement the forward propagation module (shown in purple in the figure below).

Complete the LINEAR part of a layer’s forward propagation step (resulting in Z[l]).

We give you the ACTIVATION function (relu/sigmoid).

Combine the previous two steps into a new [LINEAR->ACTIVATION] forward function.

Stack the [LINEAR->RELU] forward function L-1 time (for layers 1 through L-1) and add a [LINEAR->SIGMOID] at the end (for the final layer L). This gives you a new L_model_forward function.

Compute the loss.

Implement the backward propagation module (denoted in red in the figure below).

Complete the LINEAR part of a layer’s backward propagation step.

We give you the gradient of the ACTIVATE function (relu_backward/sigmoid_backward)

Combine the previous two steps into a new [LINEAR->ACTIVATION] backward function.

Stack [LINEAR->RELU] backward L-1 times and add [LINEAR->SIGMOID] backward in a new L_model_backward function

Finally update the parameters.

Note that for every forward function, there is a corresponding backward function. That is why at every step of your forward module you will be storing some values in a cache. The cached values are useful for computing gradients. In the backpropagation module you will then use the cache to calculate the gradients. This assignment will show you exactly how to carry out each of these steps.

3 - Initialization

You will write two helper functions that will initialize the parameters for your model. The first function will be used to initialize parameters for a two layer model. The second one will generalize this initialization process to L layers.

3.1 - 2-layer Neural Network

Exercise: Create and initialize the parameters of the 2-layer neural network.

Instructions:

The model’s structure is: LINEAR -> RELU -> LINEAR -> SIGMOID.

Use random initialization for the weight matrices. Use np.random.randn(shape)∗0.01 with the correct shape.

Use zero initialization for the biases. Use np.zeros(shape).

定义初始化函数

def initialize_parameters(n_x,n_h,n_y):

np.random.seed(1) #没有这句得不到预期结果

W1=np.random.randn(n_h,n_x)*0.01

b1=np.zeros((n_h,1))

W2=np.random.randn(n_y,n_h)*0.01

b2=np.zeros((n_y,1))

assert(W1.shape==(n_h,n_x))

assert(b1.shape==(n_h,1))

assert(W2.shape==(n_y,n_h))

assert(b2.shape==(n_y,1))

parameters={'W1':W1,

'b1':b1,

'W2':W2,

'b2':b2}

return parameters

测试:

parameters = initialize_parameters(2,2,1)

print("W1 = " + str(parameters["W1"]))

print("b1 = " + str(parameters["b1"]))

print("W2 = " + str(parameters["W2"]))

print("b2 = " + str(parameters["b2"]))

结果:

W1 = [[ 0.01624345 -0.00611756]

[-0.00528172 -0.01072969]]

b1 = [[ 0.]

[ 0.]]

W2 = [[ 0.00865408 -0.02301539]]

b2 = [[ 0.]]

3.2 - L-layer Neural Network

The initialization for a deeper L-layer neural network is more complicated because there are many more weight matrices and bias vectors. When completing the initialize_parameters_deep, you should make sure that your dimensions match between each layer. Recall that n[l] is the number of units in layer l. Thus for example if the size of our input X is (12288,209) (with m=209 examples) then:

python123第四周课后作业_吴恩达深度学习第一课第四周课后作业1参考_第1张图片

Remember that when we compute WX+b in python, it carries out broadcasting. For example, if:

W=⎡⎣⎢jmpknqlor⎤⎦⎥X=⎡⎣⎢adgbehcfi⎤⎦⎥b=⎡⎣⎢stu⎤⎦⎥(2)

Then WX+b will be:

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