【免费】吴恩达improvingDeepNetwork课程第三周作业原始文件和满分答案资源-CSDN文库

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人工智能

DeepLe

Tensor

吴恩达

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吴恩达深度学习 improving Deep Network课程第三周作业，下载这个在本地可以直接进行jupyter notebook 文件中包含数据和所需要的图片文件，这样你就可以在本地jupyter 进行练习如果你没有这个课程的会员，也可以采取这样的方式进行练习。吴恩达的课是必须进行练习才能掌握的，这个练习主要针对 TensorFlow 的 Tutorial 通过本次练习，你可以用于TensorFlow进行手指数字识别。入门TensorFlow

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week3.rar （13个子文件）

week3

Tensorflow+Tutorial.pdf 1014KB

images

hands.png 565KB

thumbs_up.jpg 989KB

onehot.png 118KB

123.jpg 49KB

improv_utils.py 11KB

__pycache__

tf_utils.cpython-36.pyc 3KB

Tensorflow+Tutorial_答案.ipynb 133KB

tf_utils.py 5KB

.ipynb_checkpoints

Tensorflow+Tutorial-checkpoint.ipynb 133KB

Tensorflow+Tutorial.ipynb 50KB

datasets

test_signs.h5 1.41MB

train_signs.h5 12.67MB

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TensorFlow Tutorial

Welcome to this week's programming assignment. Until now, you've always used numpy to build neural networks. Now we will step you through a deep learning

framework that will allow you to build neural networks more easily. Machine learning frameworks like TensorFlow, PaddlePaddle, Torch, Caffe, Keras, and many

others can speed up your machine learning development significantly. All of these frameworks also have a lot of documentation, which you should feel free to read.

In this assignment, you will learn to do the following in TensorFlow:

Initialize variables

Start your own session

Train algorithms

Implement a Neural Network

Programing frameworks can not only shorten your coding time, but sometimes also perform optimizations that speed up your code.

1 - Exploring the Tensorflow Library

To start, you will import the library:

In[3]:

Now that you have imported the library, we will walk you through its different applications. You will start with an example, where we compute for you the loss of one

training example.

loss



( ,

) = (

−



(

)

(

)

(1)

import

math

import

numpy

import

h5py

import

matplotlib.pyplot

plt

import

tensorflow

from

tensorflow.python.framework

import

ops

from

tf_utils

import

load_dataset, random_mini_batches, convert_to_one_hot, predict

matplotlib inline

np.random.seed(1)

In[4]:

Writing and running programs in TensorFlow has the following steps:

1. Create Tensors (variables) that are not yet executed/evaluated.

2. Write operations between those Tensors.

3. Initialize your Tensors.

4. Create a Session.

5. Run the Session. This will run the operations you'd written above.

Therefore, when we created a variable for the loss, we simply defined the loss as a function of other quantities, but did not evaluate its value. To evaluate it, we

had to run init=tf.global_variables_initializer() . That initialized the loss variable, and in the last line we were finally able to evaluate the value of loss

and print its value.

Now let us look at an easy example. Run the cell below:

In[6]:

As expected, you will not see 20! You got a tensor saying that the result is a tensor that does not have the shape attribute, and is of type "int32". All you did was

put in the 'computation graph', but you have not run this computation yet. In order to actually multiply the two numbers, you will have to create a session and run it.

WARNING:tensorflow:From D:\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py:263: colocate_with (from tensorflow.pytho

n.framework.ops) is deprecated and will be removed in a future version.

Instructions for updating:

Colocations handled automatically by placer.

Tensor("Mul_1:0", shape=(), dtype=int32)

y_hat = tf.constant(36, name='y_hat')

# Define y_hat constant. Set to 36.

y = tf.constant(39, name='y')

# Define y. Set to 39

loss = tf.Variable((y

y_hat)

2, name='loss')

# Create a variable for the loss

init = tf.global_variables_initializer()

# When init is run later (session.run(init)),

# the loss variable will be initialized and ready to be computed

with

tf.Session()

session:

# Create a session and print the output

session.run(init)

# Initializes the variables

print(session.run(loss))

# Prints the loss

a = tf.constant(2)

b = tf.constant(10)

c = tf.multiply(a,b)

print(c)

In[7]:

Great! To summarize, remember to initialize your variables, create a session and run the operations inside the session.

Next, you'll also have to know about placeholders. A placeholder is an object whose value you can specify only later. To specify values for a placeholder, you can

pass in values by using a "feed dictionary" ( feed_dict variable). Below, we created a placeholder for x. This allows us to pass in a number later when we run the

session.

In[8]:

When you first defined x you did not have to specify a value for it. A placeholder is simply a variable that you will assign data to only later, when running the

session. We say that you feed data to these placeholders when running the session.

Here's what's happening: When you specify the operations needed for a computation, you are telling TensorFlow how to construct a computation graph. The

computation graph can have some placeholders whose values you will specify only later. Finally, when you run the session, you are telling TensorFlow to execute

the computation graph.

1.1 - Linear function

Lets start this programming exercise by computing the following equation: , where and are random matrices and b is a random vector.

Exercise: Compute where , and are drawn from a random normal distribution. W is of shape (4, 3), X is (3,1) and b is (4,1). As an example, here

is how you would define a constant X that has shape (3,1):

X = tf.constant(np.random.randn(3,1), name = "X")

You might find the following functions helpful:

tf.matmul(..., ...) to do a matrix multiplication

tf.add(..., ...) to do an addition

np.random.randn(...) to initialize randomly

W X

sess = tf.Session()

print(sess.run(c))

# Change the value of x in the feed_dict

x = tf.placeholder(tf.int64, name = 'x')

print(sess.run(2

x, feed_dict = {x: 3}))

sess.close()

In[]:

Expected Output :

result [[-2.15657382] [ 2.95891446] [-1.08926781] [-0.84538042]]

1.2 - Computing the sigmoid

# GRADED FUNCTION: linear_function

def

linear_function():

"""

Implements a linear function:

Initializes W to be a random tensor of shape (4,3)

Initializes X to be a random tensor of shape (3,1)

Initializes b to be a random tensor of shape (4,1)

Returns:

result -- runs the session for Y = WX + b

"""

np.random.seed(1)

### START CODE HERE ### (4 lines of code)

X =

None

W =

None

b =

None

Y =

None

### END CODE HERE ###

# Create the session using tf.Session() and run it with sess.run(...) on the variable you want to calculate

### START CODE HERE ###

sess =

None

result =

None

### END CODE HERE ###

# close the session

sess.close()

return

result

print( "result = "

str(linear_function()))

Great! You just implemented a linear function. Tensorflow offers a variety of commonly used neural network functions like tf.sigmoid and tf.softmax . For this

exercise lets compute the sigmoid function of an input.

You will do this exercise using a placeholder variable x . When running the session, you should use the feed dictionary to pass in the input z . In this exercise,

you will have to (i) create a placeholder x , (ii) define the operations needed to compute the sigmoid using tf.sigmoid , and then (iii) run the session.

Exercise : Implement the sigmoid function below. You should use the following:

tf.placeholder(tf.float32, name = "...")

tf.sigmoid(...)

sess.run(..., feed_dict = {x: z})

Note that there are two typical ways to create and use sessions in tensorflow:

Method 1:

sess = tf.Session()

# Run the variables initialization (if needed), run the operations

result = sess.run(..., feed_dict = {...})

sess.close()

# Close the session

Method 2:

with

tf.Session()

sess:

# run the variables initialization (if needed), run the operations

result = sess.run(..., feed_dict = {...})

# This takes care of closing the session for you :)

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