Adam-optimizer | Implemented Adam optimizer in python | Machine Learning library
kandi X-RAY | Adam-optimizer Summary
kandi X-RAY | Adam-optimizer Summary
Implemented Adam optimizer in python
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Trending Discussions on Adam-optimizer
QUESTION
i was building a dense neural network for predicting poker hands. First i had a problem with the reproducibility, but then i discovered my real problem: That i can not reproduce my code is because of the adam-optimizer, because with sgd it worked. This means
...ANSWER
Answered 2020-May-05 at 13:13They both are the same. However, in the tensorflow.train.AdamOptimizer you can change the learning rate
QUESTION
I built a neural network of the dimensions Layers = [203,100,100,100,2]. So I have 203 features and get two classes as a Result. I think, in my case, it would not be necessary to have two classes. My result is the prediction of a customer quitting his contract. So I guess one class would be sufficient (And 1 being quit, 0 being stay). I built the network with two classes to keep it flexible if I want to add more output-classes in the future.
I put dropout,batch_normalization, and weight-decay. I am training with an Adam-optimizer. At the end of the day, I come up with
precision: 0.7826087, recall: 0.6624 on test-data.
precision: 0.8418698, recall: 0.72445 on training-data
This means if I predict a customer to quit, I can be 78% confident that he really quits. On the opposite, if he quits his contract, I predicted with 66% that he will do so.
So my classifier doesn´t work too bad. One thing keeps nagging at me: How do I know if there is any chance to do better still? In other words: Is there a possibility to calculate the Bayes-error my setup determines? Or to say it clearer: If the difference of my training-error and test-error is high like this, can I conclude for sure, that I am having a variance problem? Or is it possible that I must cope with the fact the test-accuracy cannot be improved?
What else can I try to train better?
...ANSWER
Answered 2019-Dec-17 at 15:29I put more training data. Now I use 70000 records instead of 45000. My results:
precision: 0.81765974, recall: 0.65085715 on test-data
precision: 0.83833283, recall: 0.708 on training-data
I am pretty confident that this result is as good as possible. Thanks for reading
QUESTION
I am using the DQN Agent from Ray/RLLib. To gain more insight into how the training process is going, I would like to access the internal state of the Adam-Optimizer, to eg visualize how the running average of the gradient is changing over time. See the minimal code snippet below for illustration.
...ANSWER
Answered 2019-Feb-12 at 21:31The TF optimizer object is accessible via agent.get_policy()._optimizer.
The reason you were seeing "no attribute _optimizer" before is because _policy_graph is the policy class, not the object instance, which is present in local_evaluator.policy_map or via agent.get_policy().
QUESTION
I'm trying to train an autoencoder with mse loss function with TensorFlow r1.2, but I keep getting a FailedPreconditionError which states that one of the variables related to computing the mse is uninitialized (see full stack trace printout below). I'm running this in Jupyter notebook and I'm using Python 3.
I trimmed down my code to a minimal example as follows
...ANSWER
Answered 2017-Dec-05 at 01:02Looks like you're doing everything right with initialization, so I suspect your error is that you're using tf.metrics.mean_squared_error incorrectly.
The metrics package of classes allows you to compute a value, but also accumulate that value over multiple calls to sess.run. Note the return value of tf.metrics.mean_square_error in the docs:
https://www.tensorflow.org/api_docs/python/tf/metrics/mean_squared_error
You get back both mean_square_error, as you appear to expect, and an update_op. The purpose of the update_op is that you ask tensorflow to compute the update_op and it accumulates the mean square error. Each time you call mean_square_error you get the accumulated value. When you want to reset the value you would run sess.run(tf.local_variables_initializer()) (note local and not global to clear "local" variables as the metrics package defines them).
I don't think the metrics package was intended to be used the way you're using it. I think your intention was to compute the mse only based on the current batch as your loss and not accumulate the value over multiple calls. I'm not even sure how differentiation would work with respect to an accumulated value like this.
So I think the answer to your question is: don't use the metrics package this way. Use metrics for reporting, and for accumulating results over multiple iterations of a test dataset, for example, not for generating a loss function.
I think what you mean to use is tf.losses.mean_squared_error
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Install Adam-optimizer
You can use Adam-optimizer like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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