CODEWITHSUNDEEP
pythondeep-learning
JAugust
JAugust

Reputation: 557

Install issues with 'lr_utils' in python

I am trying to complete some homework in a DeepLearning.ai course assignment.

When I try the assignment in Coursera platform everything works fine, however, when I try to do the same imports on my local machine it gives me an error,

ModuleNotFoundError: No module named 'lr_utils'

I have tried resolving the issue by installing lr_utils but to no avail.

There is no mention of this module online, and now I started to wonder if that's a proprietary to deeplearning.ai?

Or can we can resolve this issue in any other way?

Upvotes: 17

Views: 36487

Answers (13)

ThinkBonobo
ThinkBonobo

Reputation: 16535

As per the answer above, lr_utils is a part of the deep learning course and is a utility to download the data sets. It should readily work with the paid version of the course but in case you 'lost' access to it, I noticed this github project has the lr_utils.py as well as some data sets

https://github.com/andersy005/deep-learning-specialization-coursera/tree/master/01-Neural-Networks-and-Deep-Learning/week2/Programming-Assignments

Note: The chinese website links did not work when I looked at them. Maybe the server storing the files expired. I did see that this github project had some datasets though as well as the lr_utils file.

EDIT: The link no longer seems to work. Maybe this one will do?

https://github.com/knazeri/coursera/blob/master/deep-learning/1-neural-networks-and-deep-learning/2-logistic-regression-as-a-neural-network/lr_utils.py

Upvotes: 11

Imran Hosen
Imran Hosen

Reputation: 51

I faced similar problem and I had followed the following steps:

1. import the following library

import numpy as np
import matplotlib.pyplot as plt
import h5py
import scipy
from PIL import Image
from scipy import ndimage

2. download the train_catvnoncat.h5 and test_catvnoncat.h5 from any of the below link:

[https://github.com/berkayalan/Neural-Networks-and-Deep-Learning/tree/master/datasets] or [https://github.com/JudasDie/deeplearning.ai/tree/master/Improving%20Deep%20Neural%20Networks/Week1/Regularization/datasets]

3. create a folder named datasets and paste these two files in this folder.

[ Note: datasets folder and your source code file should be in same directory]

4. run the following code

def load_dataset():

    with h5py.File('datasets1/train_catvnoncat.h5', "r") as train_dataset:
        train_set_x_orig = np.array(train_dataset["train_set_x"][:])
        train_set_y_orig = np.array(train_dataset["train_set_y"][:])

    with h5py.File('datasets1/test_catvnoncat.h5', "r") as test_dataset:
        test_set_x_orig = np.array(test_dataset["test_set_x"][:])
        test_set_y_orig = np.array(test_dataset["test_set_y"][:])
        classes = np.array(test_dataset["list_classes"][:])

    train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
    test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))

    return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes

5. Load the data:

train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()

check datasets

print(len(train_set_x_orig))
print(len(test_set_x_orig))

your data set is ready, you may check the len of the train_set_x_orig, train_set_y variable. For mine, it was 209 and 50

Upvotes: 3

berkayln
berkayln

Reputation: 995

You can download train and test dataset directly here: https://github.com/berkayalan/Deep-Learning/tree/master/datasets

And you need to add this code to the beginning:

    import numpy as np
    import h5py
    import os      

def load_dataset():
    train_dataset = h5py.File('datasets/train_catvnoncat.h5', "r")
    train_set_x_orig = np.array(train_dataset["train_set_x"][:]) # your train set features
    train_set_y_orig = np.array(train_dataset["train_set_y"][:]) # your train set labels

    test_dataset = h5py.File('datasets/test_catvnoncat.h5', "r")
    test_set_x_orig = np.array(test_dataset["test_set_x"][:]) # your test set features
    test_set_y_orig = np.array(test_dataset["test_set_y"][:]) # your test set labels

    classes = np.array(test_dataset["list_classes"][:]) # the list of classes
    
    train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
    test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
    
    return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes

Upvotes: 3

Sara Perez
Sara Perez

Reputation: 21

I could download the dataset directly from coursera page.

Once you open the Coursera notebook you go to File -> Open and the following window will be display: enter image description here

Here the notebooks and datasets are displayed, you can go to the datasets folder and download the required data for the assignment. The package lr_utils.py is also available for downloading.

Upvotes: 2

olumide
olumide

Reputation: 1

This is how i solved mine, i copied the lir_utils file and paste it in my notebook thereafter i downloaded the dataset by zipping the file and extracting it. With the following code. Note: Run the code on coursera notebook and select only the zipped file in the directory to download.

!pip install zipfile36

zf = zipfile.ZipFile('datasets/train_catvnoncat_h5.zip', mode='w')

try:

    zf.write('datasets/train_catvnoncat.h5')

    zf.write('datasets/test_catvnoncat.h5')

finally:

    zf.close()

Upvotes: 0

lovelance1
lovelance1

Reputation: 71

The way I fixed this problem was by:

  1. clicking File -> Open -> You will see the lr_utils.py file ( it does not matter whether you have paid/free version of the course).
  2. opening the lr_utils.py file in Jupyter Notebooks and clicking File -> Download ( store it in your own folder ), rerun importing the modules. It will work like magic.
  3. I did the same process for the datasets folder.

Upvotes: 6

swateek
swateek

Reputation: 7580

The above answers didn't help, some links had expired.

So, lr_utils is not a pip library but a file in the same notebook as the CourseEra website.

You can click on "Open", and it'll open the explorer where you can download everything that you would want to run in another environment.

(I used this on a browser.)

Upvotes: 0

Aamir Jan Khan
Aamir Jan Khan

Reputation: 11

below is your code, just save your file named "lr_utils.py" and now you can use it.

import numpy as np
import h5py
def load_dataset():
train_dataset = h5py.File('datasets/train_catvnoncat.h5', "r")
train_set_x_orig = np.array(train_dataset["train_set_x"][:]) # your train set features
train_set_y_orig = np.array(train_dataset["train_set_y"][:]) # your train set labels
test_dataset = h5py.File('datasets/test_catvnoncat.h5', "r")
test_set_x_orig = np.array(test_dataset["test_set_x"][:]) # your test set features
test_set_y_orig = np.array(test_dataset["test_set_y"][:]) # your test set labels
classes = np.array(test_dataset["list_classes"][:]) # the list of classes
train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes

if your code file can not find you newly created lr_utils.py file just write this code:

import sys
sys.path.append("full path of the directory where you saved Ir_utils.py file")

Upvotes: 1

Mateusz Wysocki
Mateusz Wysocki

Reputation: 1

I may add to the answers that you can save the file with lr_utils script on the disc and import that as a module using importlib util function in the following way.

The below code came from the general thread about import functions from external files into the current user session:

How to import a module given the full path?

### Source load_dataset() function from a file
# Specify a name (I think it can be whatever) and path to the lr_utils.py script locally on your PC: 
util_script = importlib.util.spec_from_file_location("utils function", "D:/analytics/Deep_Learning_AI/functions/lr_utils.py")

# Make a module
load_utils = importlib.util.module_from_spec(util_script)

# Execute it on the fly
util_script.loader.exec_module(load_utils)

# Load your function
load_utils.load_dataset()

# Then you can use your load_dataset() coming from above specified 'module' called load_utils
train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_utils.load_dataset()

# This could be a general way of calling different user specified modules so I did the same for the rest of the neural network function and put them into separate file to keep my script clean. 
# Just remember that Python treat it like a module so you need to prefix the function name with a 'module' name eg.:
# d = nnet_utils.model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 1000, learning_rate = 0.005, print_cost = True)

nnet_script = importlib.util.spec_from_file_location("utils function", "D:/analytics/Deep_Learning_AI/functions/lr_nnet.py")
nnet_utils = importlib.util.module_from_spec(nnet_script)
nnet_script.loader.exec_module(nnet_utils)

That was the most convenient way for me to source functions/methods from different files in Python so far. I am coming from the R background where you can call just one line function source() to bring external scripts contents into your current session.

Upvotes: 0

Jishnu Dey
Jishnu Dey

Reputation: 127

You will be able to find the lr_utils.py and all the other .py files (and thus the code inside them) required by the assignments:

  1. Go to the first assignment (ie. Python Basics with numpy) - which you can always access whether you are a paid user or not

  2. And then click on 'Open' button in the Menu bar above. (see the image below)

    .

Then you can include the code of the modules directly in your code.

Upvotes: 11

Jaden
Jaden

Reputation: 322

  1. Here is the way to get dataset from as @ThinkBonobo: https://github.com/andersy005/deep-learning-specialization-coursera/tree/master/01-Neural-Networks-and-Deep-Learning/week2/Programming-Assignments/datasets

  2. write a lr_utils.py file, as above answer @StationaryTraveller, put it into any of sys.path() directory.

  3. def load_dataset(): with h5py.File('datasets/train_catvnoncat.h5', "r") as train_dataset: ....

    !!! BUT make sure that you delete 'datasets/', cuz now the name of your data file is train_catvnoncat.h5

  4. restart kernel and good luck.

Upvotes: 0

StationaryTraveller
StationaryTraveller

Reputation: 1469

Download the datasets from the answer above.

And use this code (It's better than the above since it closes the files after usage):

def load_dataset():
    with h5py.File('datasets/train_catvnoncat.h5', "r") as train_dataset:
        train_set_x_orig = np.array(train_dataset["train_set_x"][:])
        train_set_y_orig = np.array(train_dataset["train_set_y"][:])

    with h5py.File('datasets/test_catvnoncat.h5', "r") as test_dataset:
        test_set_x_orig = np.array(test_dataset["test_set_x"][:])
        test_set_y_orig = np.array(test_dataset["test_set_y"][:])
        classes = np.array(test_dataset["list_classes"][:])

    train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
    test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))

    return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes

Upvotes: 10

cijad
cijad

Reputation: 172

"lr_utils" is not official library or something like that. Purpose of "lr_utils" is to fetch the dataset that is required for course.

  1. option (didn't work for me): go to this page and there is a python code for downloading dataset and creating "lr_utils"

    • I had a problem with fetching data from provided url (but at least you can try to run it, maybe it will work)

  2. option (worked for me): in the comments (at the same page 1) there are links for manually downloading dataset and "lr_utils.py", so here they are:

Upvotes: 8

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