handson-ml2 | Jupyter notebooks that walk you through the fundamentals | Machine Learning library

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

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.

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:

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):

For housing data set, I am trying to use DataFrameMapper() from sklearn_pandas to apply polynomial features on selected columns.

My code:

Getting this memory error. But the book/link I am following doesn't get this error.

A part of Code:

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:

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: