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Home Page: https://medium.com/@sebastiankt9
License: MIT License
All of the code for my Medium articles
Home Page: https://medium.com/@sebastiankt9
License: MIT License
Hello
firstly, thank you for this tutorial.
I succeeded in making the train_dqn run but the training did not lead to a good result.
Game number: 005100 Frame number: 01981609 Average reward: 22.1 Time taken: 38.0s
Is this normal ?
Do you have any suggestions ?
Thanks
Hi,
I was trying flow the tutorial for the DCGAN but I have hit a problem.
When I run the following line gan_output = discriminator(fake_image)
I get the error Dimensions must be equal, but are 3 and 3072 for 'sequential_5/dense_14/MatMul' (op: 'MatMul') with input shapes: [?,3], [3072,1024].
I have checked the numbers and they line up with what is defined above in the tutorial.
Can you help?
Cheers
Dear Sebastian Theiler,
thanks for your great DQN tutorial example. I was wondering if it is possible to suppress the output in the console that looks something like:
1/1 [==============================] - 0s 26ms/step
1/1 [==============================] - 0s 25ms/step
1/1 [==============================] - 0s 22ms/step
1/1 [==============================] - 0s 25ms/step
1/1 [==============================] - 0s 23ms/step
1/1 [==============================] - 0s 21ms/step
1/1 [==============================] - 0s 21ms/step
1/1 [==============================] - 0s 22ms/step
1/1 [==============================] - 0s 22ms/step
1/1 [==============================] - 0s 24ms/step
1/1 [==============================] - 0s 23ms/step
1/1 [==============================] - 0s 27ms/step
1/1 [==============================] - 0s 24ms/step
1/1 [==============================] - 0s 24ms/step
1/1 [==============================] - 0s 28ms/step
1/1 [==============================] - 0s 26ms/step
1/1 [==============================] - 0s 25ms/step
1/1 [==============================] - 0s 26ms/step
1/1 [==============================] - 0s 32ms/step
1/1 [==============================] - 0s 25ms/step
1/1 [==============================] - 0s 25ms/step
1/1 [==============================] - 0s 31ms/step
1/1 [==============================] - 0s 28ms/step
1/1 [==============================] - 0s 31ms/step
1/1 [==============================] - 0s 27ms/step
1/1 [==============================] - 0s 34ms/step
1/1 [==============================] - 0s 30ms/step
1/1 [==============================] - 0s 24ms/step
1/1 [==============================] - 0s 25ms/step
1/1 [==============================] - 0s 23ms/step
1/1 [==============================] - 0s 25ms/step
1/1 [==============================] - 0s 24ms/step
1/1 [==============================] - 0s 23ms/step
1/1 [==============================] - 0s 24ms/step
1/1 [==============================] - 0s 22ms/step
1/1 [==============================] - 0s 22ms/step
1/1 [==============================] -
or make it more informative for each line. Seems to come from Keras?
Every once in a while I see a line like
1/1 [==============================] - 0s 24ms/step
1/1 [==============================] - 0s 25ms/step
1/1 [==============================] - 0s 28ms/step
Game number: 000580 Frame number: 00104874 Average reward: 1.3 Time taken: 12.0s
1/1 [==============================] - 0s 29ms/step
1/1 [==============================] - 0s 27ms/step
1/1 [==============================] - 0s 25ms/step
1/1 [==============================] - 0s 30ms/
coming by.
Or is something going horribly wrong in my environment and console output during training should not be looking like this?
In the class "replay buffer", i found there is "states = np.transpose(np.asarray(states), axes=(0, 2, 3, 1))" in the def get_minibatch.
The problem is why transpose needed here? It looks like the original sequence is already correct so don't need transpose operation here.
Many thanks!
Hi, I found below code in the network part of train_dqn.py
###########################################################
val_stream, adv_stream = Lambda(lambda w: tf.split(w, 2, 3))(x) # custom splitting layer
##############################################################################
It looks like the source from hidden network was divided into 2 different partial parts then one feed to state value, another one to adv value. I have also checked other implementations and paper. It looks like each flow should be the complete copy of the hidden layer rather than partial of it. Can i ask why you want to split it rather than feed the same whole data flow to both stat and adv?
Many thanks!
Edward
looks like target-network and online-network gets updated at the same frequency UPDATE_FREQ
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