handson-ml2 | Jupyter notebooks that walk you through the fundamentals | Machine Learning library
kandi X-RAY | handson-ml2 Summary
kandi X-RAY | handson-ml2 Summary
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
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QUESTION
I am trying to learn a custom environment using the TFAgents package. I am following the Hands-on-ML book (Code in colab see cell 129). My aim is to use DQN agent on a custom-written grid world environment.
Grid-World environment:
...ANSWER
Answered 2021-Jun-02 at 22:36You cannot use TensorSpec
with PyEnvironment
class objects, this is why your attempted solution does not work. A simple fix should be to use the original code
QUESTION
From the python document, it is mentioned that urllib.request.urlretrieve
returns a tuple and will be used to open file as shown in Code-A below.
However in the example Code-B. The urllib.request.urlretrieve
does not return but the code will fail without it. Please help clarify what does urllib.request.urlretrieve
doing in Code B. THanks
Code A
...ANSWER
Answered 2021-Apr-12 at 14:39The retrieve() method is used to save web content from url (eg csv,images etc) In your case it is saving the housing data saved up in the url. You can check the docs [here][1]
QUESTION
I have been trying to implement the Reinforcement learning algorithm on Python using different variants like Q-learning
, Deep Q-Network
, Double DQN
and Dueling Double DQN
. Consider a cart-pole example and to evaluate the performance of each of these variants, I can think of plotting sum of rewards
to number of episodes
(attaching a picture of the plot) and the actual graphical output where how well the pole is stable while the cart is moving.
But these two evaluations are not really of interest in terms to explain the better variants quantitatively. I am new to the Reinforcement learning and trying to understand if any other ways to compare different variants of RL models on the same problem.
I am referring to the colab link https://colab.research.google.com/github/ageron/handson-ml2/blob/master/18_reinforcement_learning.ipynb#scrollTo=MR0z7tfo3k9C for the code on all the variants of cart pole example.
...ANSWER
Answered 2021-Jan-09 at 03:53You can find the answer in research paper about those algorithms, because when a new algorithm been proposed we usually need the experiments to show the evident that it have advantage over other algorithm.
The most commonly used evaluation method in research paper about RL algorithms is average return (note not reward, return is accumulated reward, is like the score in game) over timesteps, and there many way you can average the return, e.g average wrt different hyperparameters like in Soft Actor-Critic paper's comparative evaluation average wrt different random seeds (initialize the model):
Figure 1 shows the total average return of evaluation rolloutsduring training for DDPG, PPO, and TD3. We train fivedifferent instances of each algorithm with different randomseeds, with each performing one evaluation rollout every1000 environment steps. The solid curves corresponds to themean and the shaded region to the minimum and maximumreturns over the five trials.
And we usually want compare the performance of many algorithms not only on one task but diverse set of tasks (i.e Benchmark), because algorithms may have some form of inductive bias for them to better at some form of tasks but worse on other tasks, e.g in Phasic Policy Gradient paper's experiments comparison to PPO:
We report results on the environments in Procgen Benchmark (Cobbe et al.,2019). This benchmark was designed to be highly diverse, and we expect improvements on this benchmark to transfer well to many other RL environment
QUESTION
I am reading the book "Hands-On Machine Learning" and I have a problem with exercise 9 of chapter 13, the exercise is as follows:
a. Load the Fashion MNIST dataset (introduced in Chapter 10); split it into a training set, a validation set, and a test set; shuffle the training set; and save each dataset to multiple TFRecord files. Each record should be a serialized Example protobuf with two features: the serialized image (use tf.io.serialize_tensor() to serialize each image), and the label.
b. Then use tf.data to create an efficient dataset for each set. Finally, use a Keras model to train these datasets, including a preprocessing layer to standardize each input feature.
You can find the exercise with the solution at the end of this notebook: https://github.com/ageron/handson-ml2/blob/master/13_loading_and_preprocessing_data.ipynb
I loaded the data like this:
...ANSWER
Answered 2020-Dec-14 at 17:01Because you are shuffling only the input data (X_train
) without applying the same shuffling to the corresponding labels y_train
. You should shuffle both together:
QUESTION
I am currently studying the book "Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow". I tried running the following example, without success however. The link is working, pandas is installed correctly, os, tarfile and urllib are system packages. Still, I get the error message below (tried Jupyter & Spyder):
...ANSWER
Answered 2020-Oct-05 at 05:49The local file "datasets/housing/housing.csv"
is created only when you call
QUESTION
For housing data set, I am trying to use DataFrameMapper() from sklearn_pandas to apply polynomial features on selected columns.
My code:
...ANSWER
Answered 2020-Sep-19 at 16:36- From the documentation
- The difference between specifying the column selector as
'column'
(as a simple string) and['column']
(as a list with one element) is the shape of the array that is passed to the transformer. In the first case, a one dimensional array will be passed, while in the second case it will be a 2-dimensional array with one column, i.e. a column vector.
- The difference between specifying the column selector as
- All of the columns must be passed with the same type of column selector.
- In this case, a
list
, since there's alist
of non-transformed columns to keep.
- In this case, a
QUESTION
Getting this memory error. But the book/link I am following doesn't get this error.
A part of Code:
...ANSWER
Answered 2020-Jul-10 at 19:35The message is straight forward, yes, it has to do with the available memory.
359 MiB = 359 * 2^20 bytes = 60000 * 784 * 8 bytes
where MiB = Mebibyte = 2^20 bytes, 60000 x 784 are the dimensions of your array and 8 bytes is the size of float64.
Maybe the 3.1gb free memory is very fragmented and it is not possible to allocate 359 MiB in one piece?
A reboot may be helpful in that case.
QUESTION
SET ML_PATH=E:\Workspace\Handson-ml2
echo $ML_PATH
...ANSWER
Answered 2020-Sep-01 at 03:53You can define a variable in PowerShell using the $varname =
syntax:
QUESTION
I have had problems here, here and there installing TensorFlow 2 over the last year or so. So I am trying Miniconda.
I have an AMD Radeon hd 6670 and an AMD Radeon hd 6450.
I just downloaded Miniconda and made an environment and did a pip install --upgrade tensorflow
in a Miniconda prompt on Windows 8.1 and got TensorFlow 2.2.
When I try to import tensorflow I get the stack trace below.
I did download Visual Studio to get the latest redistributebles (I think).
seems like this occurs near this line: from tensorflow.python.pywrap_tensorflow_internal import *
Edit 1: I used this yaml file for python 3.6 (the other was 3.7), but it produced the same error.
Edit 2: I upgraded to Conda 4.8.3 and Python 3.7 (in the yaml file) and got the same error. This is the line in pywrap internal that shows the problem:
...ANSWER
Answered 2020-Jul-26 at 16:09I ran into a comparable problem (this is the furthest i got) reproducibly on two machines. Some of the discussed issues seems to be known for example here: 1 2 3 4. Not only to reproduce 2, it makes sense to also start using virtual environments in order to test multiple tf versions. This can be achieved like this: (link for virtualenv on windows)
QUESTION
Below is part of the code that is relevant to the question. If there is a need for full code, here is a full reproducible code that downloads data too: https://github.com/ageron/handson-ml2/blob/master/02_end_to_end_machine_learning_project.ipynb
I have a pipeline:
...ANSWER
Answered 2020-Jun-03 at 20:02FeatureUnion
can do the trick:
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