TensorFlow Tutorial 4 - Convolutional Neural Networks with Sequential and Functional API

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In this video we will learn how to build a convolutional neural network (cnn) in TensorFlow 2.0 using the Keras Sequential and Functional API. We take a look at how to do max pooling layers, batch normalization etc.

Resources or prerequisite videos:
1. Deep learning specialization course 4 (No need to watch all, for example not after C4W2L02):

2. Batch Normalization video:

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* Corrections to the video:

When re watching the video there were two things I felt weren't clear or that I didn't cover that I should've:

2. I showed how to do a simple conv net using the Sequential and the Functional, and showed how we can use batch normalization when using the Functional. This can of course also be done using the Sequential API also, I just did it to mix things up! So there wasn't really any reason to use the Functional API here, it was just two illustrate of the implementation would differ between the two APIs.

Also I was inspired and learned the basics of TensorFlow after I completed the TensorFlow specialization on coursera. Personally I think these videos I created give a similar understanding but if you wanna check it out you can. Below you'll find both affiliate and non-affiliate links, the pricing for you is the same but a small commission goes back to the channel if you buy it through the affiliate link which helps me create more future videos.

AladdinPersson

Dude, I know how long it takes to make a video. But you need to upload more videos per day. These are just so awesome. I see this channel growing.

universe

dude there is a reason why there are no dislikes on this video and that's because you are amazing
keep doing your work

navalsurange

Your videos are awsome! One of the best teaching material I saw! Keep going! Good luck in your channel development! Thanks for NLP series!

dktdklc

The explanation is smooth and crisp. Thanks for the tutorial and would like to see more of these. However, I was looking for building the CNN with only TensorFlow, not with Keras. Keras makes it much easier and less flexible.

sridharaddagatla

I liked the way how you are moving in the concepts. I wanted to refresh my concepts and your videos are on dot. Thanks for the Videos.

srinivasadineshparupalli

One of best ways of explanation. Thanks.

underlecht

Thank you Sir, for updating such a great tutorial, please keep it up. Sir your teaching methodology is superb

AltafHussain-gkxe

Correct me if I am wrong. I think layer normalization has a flaw!

Consider two features: House area and number of rooms.

House 1: Area = 1 m², Rooms = 1. After normalization, this would be [0, 0]
House 2: Area = 100 m², Rooms = 100. This would also result in [0, 0] after normalization.

How can two distinct sets of features become the same after normalization?

temanangka

HI, i love your teaching Tensorflow, can you show how to do hyperparameter tuning for the models please

rimeethreddy

hey Aladdin,
awesome content mate...easy to understnd and to the point.
could you please help me with some resources about the differents aspects of tensorflow like flatten, batch sizes, batchnorm etc...i want to understand when and why they are required and how they change the model flow. any recommendations (blogs, video, books...anything) for the same?

puneetbhasin

How come do you have 10 outputs in the last layer without softmax and are using This loss function works on single output which is not one-hot encoded, and you haven't one-hot encoded your y_train labels. Can you explain this please?

*EDIT:*
Is it because of the *train_logits=True* ?

googlable

Why do we not specify a soft max activation for the output? Do we not do this with convnets?

prod.kashkari

x = layers.Conv2D(64, 5, padding='same')(x)

Why change from 3 to 5? and why change from 'valid' to 'same'?

orjihvy

While the original paper talks about applying batch norm just before the activation function, it has been found in practice that applying batch norm after the activation yields better results.

MohammadJahidIbnaBasher-besm

Hi,
First of all, its an amazing tutorial. I have a doubt.
Whats the reason for adding two Dense layers one after the other?
Is there any difference in the accuracy if I direvtly convert it 10 channels (by passing one of the Dense layers)?

Thanks

bhaveshgoyal

12:50 Why increase from 32 to 64 to 128 in different layer? I don't quite get it. Thanks

gavin

shouldn't we use batch-normalization to normalize the output of activation function A as you write in 11:31 you normalize Z
I have seen CNN

donfeto

In the function my_model(), in the second line "x=layers.Conv2D(32, 3)(inputs)". I am geting error
" Dimension value must be integer or None or have an __index__ method, got value 'TensorShape([None, 32, 32, 3])' with type '<class "

jatinlakhani

Can you do a video on transformer models?

grahamhenry

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