I don't know if the two people teaching us in this presentation will ever get this message, but if you guys are reading this, I wanted to thank you so much! Not only for the clear explanations, but also for giving me a bit of perspective as to why this is an interesting topic to learn. I feel like we're a community, and maybe this is what I needed most in these pandemic times. Thanks again!
My parents' scarf store in San Francisco was just about to go bankrupt because nobody buys scarfs in SF. Then Martin Gorner moved there, and the business is thriving again!
I must say something about the math, There are two ways teaching ML: 1. Pre-requiring from your audience the underlying math needed. 2. Explain the needed math during the lecture. Ignoring the math or showing it without explaining it carefully in a detailed fashion, is not helpful. I'm 3rd year college student and this is not a clear lecture for me. All I got is that Tensorflow can do Reinforcement with NN, that we use softmax in the last layer. What I'm missing is the full understanding of the pipeline/graph and the derivation part.
You can find the slides and the code for this talk, as well as all the other talks in the "Tensorflow without a PhD" series at this URL: github.com/GoogleCloudPlatform/tensorflow-without-a-phd
I can't quite get the loss function to work with TF2.0, loss = tf.reduce_sum(R * cross_entropies) model.compile(optimizer="Adam", loss=loss, metrics=['accuracy']) TypeError: Using a `tf.Tensor` as a Python `bool` is not allowed. Use `if t is not None:` instead of `if t:` to test if a tensor is defined, and use TensorFlow ops such as tf.cond to execute subgraphs conditioned on the value of a tensor. Anyone got some advice? Thanks!
But when they take the reward and multiply by the cross_entropy, won't a negative reward ( loss ) turn the cross entropy negative? And by minimizing this, they actually encourage the algorithm to lose? I notice in the slides that they do: loss = - R( ... ), but I can't see this reflected in the code?
Hello, I don't know if you still care about it but if I got this correctly. You compute the gradient of the loss function. Even if the loss is negative, the gradient of a logarithme is positive. Actually, if the loss is negative the gradient of the loss is very large meaning the neural network didn't choose a good option.
Form the code, refer to the following line loss = tf.reduce_sum(processed_rewards * cross_entropies + move_cost) Could I know the reason processed_reward is passed in as it is instead of negating it? Cause to my understanding, even it is normalised, negative or small reward indicate losing point or result of bad action and it should be discouraged. And from the code it minimize loss in optimization function, so it seems to encourage bad action?
Based on the number of outputs at the final layers. Generally if you have more than 2 outputs, you use softmax, and if you have two outputs or less at the output layer, you choose sigmoid. By the way, in the hidden layer, Relu is more preferred than Sigmoid in deep learning. I encourage you to take the Deep Learning Specialization course at Coursera if you would like to have a "deeper" understanding...
For classification (putting things into categories): SOFTMAX on the last layer. For regression (producing numbers with continuous values): most often you use no activation, just the weighted sum. You could use the SIGMOID for this if you are KO with output values between 0 and 1 (or TANH for values between -1 and 1). There is a good example of this in the YOLO algorithm for object detection explained in this session: th-cam.com/video/vaL1I2BD_xY/w-d-xo.html
I've been doing a lot of reading on this, and it's very subjective, but deals with underfitting and overfitting. The more neurons there are the more overfitting the data will become, and vice versa with underfitting. To strike a balance, neural architecture comes into play.
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I don't know if the two people teaching us in this presentation will ever get this message, but if you guys are reading this, I wanted to thank you so much! Not only for the clear explanations, but also for giving me a bit of perspective as to why this is an interesting topic to learn. I feel like we're a community, and maybe this is what I needed most in these pandemic times. Thanks again!
My parents' scarf store in San Francisco was just about to go bankrupt because nobody buys scarfs in SF. Then Martin Gorner moved there, and the business is thriving again!
Here we go....... "without a Phd" I love your sessions!!
Thank you for doing this ....
Thank you for the insight. I was able to successfully apply your approach to problems in OpenAI gym
Very recommendable! I liked the robot arm that learned how to flip pancakes
I must say something about the math, There are two ways teaching ML:
1. Pre-requiring from your audience the underlying math needed.
2. Explain the needed math during the lecture.
Ignoring the math or showing it without explaining it carefully in a detailed fashion, is not helpful.
I'm 3rd year college student and this is not a clear lecture for me.
All I got is that Tensorflow can do Reinforcement with NN, that we use softmax in the last layer.
What I'm missing is the full understanding of the pipeline/graph and the derivation part.
i think it's more of this is a basic RL look to Tf-Agents for more complex implementations of RL
You can find the slides and the code for this talk, as well as all the other talks in the "Tensorflow without a PhD" series at this URL: github.com/GoogleCloudPlatform/tensorflow-without-a-phd
Wonderful man Yeah
Great explanation. Finally understood all the math behind drl
you joking? didn't watch it yet. so I should?
Ok you joked.
I can't quite get the loss function to work with TF2.0,
loss = tf.reduce_sum(R * cross_entropies)
model.compile(optimizer="Adam", loss=loss, metrics=['accuracy'])
TypeError: Using a `tf.Tensor` as a Python `bool` is not allowed. Use `if t is not None:` instead of `if t:` to test if a tensor is defined, and use TensorFlow ops such as tf.cond to execute subgraphs conditioned on the value of a tensor.
Anyone got some advice? Thanks!
neither can I
great explanation by martin and Han . Great work
Great stuff!
Typo:
tf.losses.softmax_cross_entropy(one_hot_labels,
should be:
tf.losses.softmax_cross_entropy(onehot_labels,
Amazing talk. Just what I was looking for.
The code is here.
github.com/GoogleCloudPlatform/tensorflow-without-a-phd/tree/master/tensorflow-rl-pong
But when they take the reward and multiply by the cross_entropy, won't a negative reward ( loss ) turn the cross entropy negative? And by minimizing this, they actually encourage the algorithm to lose? I notice in the slides that they do: loss = - R( ... ), but I can't see this reflected in the code?
Hello, I don't know if you still care about it but if I got this correctly. You compute the gradient of the loss function. Even if the loss is negative, the gradient of a logarithme is positive. Actually, if the loss is negative the gradient of the loss is very large meaning the neural network didn't choose a good option.
This is incredible!! Thank you for the great talk!!
I like this double-act thing they have going on
Form the code, refer to the following line
loss = tf.reduce_sum(processed_rewards * cross_entropies + move_cost)
Could I know the reason processed_reward is passed in as it is instead of negating it? Cause to my understanding, even it is normalised, negative or small reward indicate losing point or result of bad action and it should be discouraged. And from the code it minimize loss in optimization function, so it seems to encourage bad action?
the minus sign has already been applied in "cross-entropies"
How do we decide between SOFTMAX and SIGMOID as a function for the final layer?
Based on the number of outputs at the final layers. Generally if you have more than 2 outputs, you use softmax, and if you have two outputs or less at the output layer, you choose sigmoid.
By the way, in the hidden layer, Relu is more preferred than Sigmoid in deep learning. I encourage you to take the Deep Learning Specialization course at Coursera if you would like to have a "deeper" understanding...
Thanks so much
Completely echo what @Osama Alraddadi said.
For classification (putting things into categories): SOFTMAX on the last layer. For regression (producing numbers with continuous values): most often you use no activation, just the weighted sum. You could use the SIGMOID for this if you are KO with output values between 0 and 1 (or TANH for values between -1 and 1). There is a good example of this in the YOLO algorithm for object detection explained in this session: th-cam.com/video/vaL1I2BD_xY/w-d-xo.html
Actually softmax ist just a generalization of sigmoid to multiple classes. In case of 2 classes softmax is equivalent to sigmoid.
Can anybody explain why there is no dtype e.g. in
observations = tf.placeholder(shape=[None, 80x80])
The code is in: github.com/GoogleCloudPlatform/tensorflow-without-a-phd/tree/master/tensorflow-rl-pong
Does somebody know, is this actually the "REINFORE" algorithm? (Williams 1992)
yep
where is the code for this? where is the game environment anyone know where i can find it ? thank you
code with installation instructions: github.com/GoogleCloudPlatform/tensorflow-without-a-phd
Where is the code for this session?
right here: github.com/GoogleCloudPlatform/tensorflow-without-a-phd/tree/master/tensorflow-rl-pong
This is adapted from Karpathy's Blog. The original post with the code and everything is here : karpathy.github.io/2016/05/31/rl/
Yes it is. Credit where credit is due.
Still need a PhD
تحياتي الخالصة شكرا جزيلا
How to choose the number of neurons of 200 or 20
I've been doing a lot of reading on this, and it's very subjective, but deals with underfitting and overfitting. The more neurons there are the more overfitting the data will become, and vice versa with underfitting. To strike a balance, neural architecture comes into play.
Trial and error lol
Pls introduce tensor flow for 32 bit pc
great talk.
Is the example code published?
Right here: github.com/GoogleCloudPlatform/tensorflow-without-a-phd
Joppe Geluykens follow the link on the screen at 39:30
Michael Fortner Sweet, thanks!
Inspiring!!
Nice will check the code
up bhau teaching TF and RL
Policy Gradient
One day to converge? Thats a lot! Imagine a more complex problem
of course you do not need PhD to understand simple linear algebra.
Thank you for that :-)
BRASILLL