learning-python | Learning Python Concepts with easy to understand code | Machine Learning library
kandi X-RAY | learning-python Summary
kandi X-RAY | learning-python Summary
This Repository is meant for Learning Python Fundamentals with the real world examples. It would invoke some thoughts and logic inside you to go further. There would be lots of examples and exercises on the top of that that you can do to be comfortable with Python Programming. Once you are good with basic concepts explained in this repository, you can go ahead and practice those exercises mentioned in exercise folder. Real world examples are the examples which you would see around you in regular basis like conversion of json to csv, pdf to images, web scraping etc.
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Top functions reviewed by kandi - BETA
- Serve the given content
- Get a BeautifulSoup object
- Check if the GitHub user exists
- Gets data for a given tab
- Start timer
- Set timer counter
- Serve a GHBot
- Serve user
- Get login information
- Return a list of profile views
- Return a list of prime primes
- 2Sum function for two arrays
- Calculates the center of the image
- Show clock
- Rename all files
- Display video duration in seconds
- Get available playlists
- Recognize audio using the given recorder
- Login to Google
- Serve the home page
- Draws sierpinski
- Start stopwatch
- Display digital clock
- Get contact details
- Draws a square
- Draw the turtle
learning-python Key Features
learning-python Examples and Code Snippets
Community Discussions
Trending Discussions on learning-python
QUESTION
I'm new to keras. This code is working on classifying between MRI images of brain with or without tumor. When I run model.evaluate() to see the accuracy I get very high loss value even it is low when I'm training the model(normal less than 1) and I get the following error:
ANSWER
Answered 2021-Jan-18 at 06:23Ignore the warning.
Your low training loss and high evaluation loss means that your model is overfitted. Stop training when your validation accuracy starts to increase.
QUESTION
I'm creating a 1D CNN using tensorflow.keras, following this tutorial, with some of the concepts from this tutorial. So far modeling and training seem to be working, but I can't seem to generate a prediction. Here's an example of what I'm dealing with:
ANSWER
Answered 2020-Nov-23 at 09:09please runing the code: model.predict([trainX[0]]), and the model outputs the predicted results
QUESTION
I am trying feature selection on the Iris dateset.
I'm referencing from Feature Selection with Univariate Statistical Tests
I am using below lines and I want to find out the significant features:
...ANSWER
Answered 2020-Oct-14 at 05:18Use indexing, here is possible use columns names, because selected first 4 columns:
QUESTION
Let's say there is some class:
...ANSWER
Answered 2020-Aug-12 at 13:16The __mro__ "attribute" is a data descriptor, similar to property. Instead of fetching the __mro__ attribute value from __dict__, the descriptor fetches the value from another place or computes it. In specific, a indicates a descriptor that fetches the value from an VM-internal location – this is the same mechanism used by __slots__.
QUESTION
import os
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from keras.applications import Xception, VGG16, ResNet50
conv_base = VGG16(weights='imagenet',include_top=False,input_shape=(224, 224, 3))
base_dir = 'NewDCDatatset'
train_dir = os.path.join(base_dir, 'Train')
validation_dir = os.path.join(base_dir, 'Validation')
test_dir = os.path.join(base_dir, 'Test')
datagen = ImageDataGenerator(rescale=1./255)
batch_size = 20
def extract_features(directory, sample_count):
features = np.zeros(shape=(sample_count, 7 , 7 , 512))
labels = np.zeros(shape=(sample_count))
generator = datagen.flow_from_directory(directory,target_size=(224, 224),batch_size=batch_size,class_mode='categorical')
i = 0
for inputs_batch, labels_batch in generator:
features_batch = conv_base.predict(inputs_batch)
features[i * batch_size : (i + 1) * batch_size] = features_batch
labels[i * batch_size : (i + 1) * batch_size] = labels_batch
i += 1
if i * batch_size >= sample_count:
break
return features, labels
train_features, train_labels = extract_features(train_dir, 9900*2)
validation_features, validation_labels = extract_features(validation_dir, 1300*2)
test_features, test_labels = extract_features(test_dir, 2600)
train_features = np.reshape(train_features, (9900*2, 7 * 7 * 512))
validation_features = np.reshape(validation_features, (2600, 7 * 7 * 512))
test_features = np.reshape(test_features, (2600, 7 * 7 * 512))
from keras import models
from keras import layers
from keras import optimizers
model = models.Sequential()
model.add(layers.Dense(256, activation='relu', input_dim=7 * 7 * 512))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(2, activation='softmax'))
model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['acc'])
history = model.fit(train_features, train_labels,epochs=3,batch_size=50,shuffle=True)
print(model.evaluate(test_features,test_labels))
model.save('TLFACE.h5')
ANSWER
Answered 2020-May-28 at 11:05If you are doing multiclass classification (one answer per input , where the answer may be one-of-n possibilities) then I blv. the problem may be remedied using
QUESTION
I've implemented a neural network using tensor flow and it appears to be only running on 1/32 data points. I've then tried to following simple example to see if it was me:
https://pythonprogramming.net/introduction-deep-learning-python-tensorflow-keras/
Even when using identical (copied and pasted) code I still get 1/32 of the training data being processed e.g.
...ANSWER
Answered 2020-May-15 at 10:43This is a common misconception, there are been updates to Keras and it now shows batches, not samples, in the progress bar. And this is perfectly consistent because you say 1/32 of the data provided, and 32 is the default batch size in keras.
QUESTION
I'm learning TensorFlow and Keras. I'd like to try https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438/, and it seems to be written in Keras.
Would it be fairly straightforward to convert code to tf.keras?
I'm not more interested in the portability of the code, rather than the true difference between the two.
...ANSWER
Answered 2019-Mar-15 at 08:38At this point tensorflow has pretty much entirely adopted the keras API and for a good reason - it's simple, easy to use and easy to learn, whereas "pure" tensorflow comes with a lot of boilerplate code. And yes, you can use tf.keras without any issues, though you might have to re-work your imports in the code. For instance
QUESTION
Currently learning about try and except and trying out to catch errors with dividing to numbers
This is my code:
...ANSWER
Answered 2020-Apr-07 at 11:56If you want to try passing a character instead of integer, try: print(divide(2,"a")).
Passing a without defining it before will cause a failure during the evaluation of your code and not during runtime and that's why catching NameError won't help here.
QUESTION
I'm new to the ML and I was following this tutorial which teaches how to do cryptocurrency predictions based on some futures.
My code to do the prediction:
...ANSWER
Answered 2020-Mar-03 at 01:22LSTM expects inputs shaped (batch_size, timesteps, channels); in your case, timesteps=60, and channels=128. batch_size is how many samples you're feeding at once, per fit / prediction.
Your error indicates preprocessing flaws:
- Rows of your DataFrame, based on index name
time, would fill dim 1 ofx->timesteps - Columns are usually features, and would fill dim 2 of
x->channels - dim 0 is the samples dimension; a "sample" is an independent observation - depending on how your data is formatted, one file could be one sample, or contain multiple
Once accounting for above:
print(x.shape)should read(N, 60, 128), whereNis the number of samples,>= 1- Since you're iterating over
ready_x,xwill sliceready_xalong its dim 0 - soprint(ready_x.shape)should read(M, N, 60, 128), whereM >= 1; it's the "batches" dimension, each slice being 1 batch.
As basic debugging: insert print(item.shape) throughout your preprocessing code, where item is an array, DataFrame, etc. - to see how shapes change throughout various steps. Ensure that there is a step which gives 128 on the last dimension, and 60 on second-to-last.
QUESTION
Few weeks back, in my 4th week of learning python, I have written my first real-life program. Now about a month later, with a little bit more experience in hand, I am trying to refactor the code. First thing I want to do is to change my naming convention to follow the PEP8 guidelines.
Originally I have used a Jupyter Notebook to interactively get the program ready. Since I have come from a Java programming background, using the notebook interface was quite a positive and pleasant surprise as I could inspect the values and code in an interactive manner. Then, once done, I copied and pasted the entire code in a python file and used the Mu Code Editor to run it as a whole.
Now that I am trying to refactor by doing a simple renaming of all the items from a camelCase to underscore convention, both Jupyter and Mu Code Editor did not have any support for refactoring. Hence, after doing a bit of googling, I installed PyCharm and started trying it. The ability to refactor-rename is quite nice, however, I find that it has a number of nuances that I don't fully understand - are there any tips for using renaming so that I could convert all the variables, functions using camelCase to use the underscore convention quickly - for example, all the variables names to be changed in one go, rather than doing it one by one?
Here is the code I am trying to refactor - https://github.com/ssamsudeen/learning-python/blob/master/quranSRS.py
Note: After posting this, I did a bit of experiment to see how Find & Replace works in Jupyter works - Find & Replace dialog had 3 toggle buttons for case sensitivity, regular expressions, and the ability to only replace within selected cells. I found the output it provides to be meaningful - so, this is what I am doing now, till I find a better way to do this. Any suggestion on how I can do Find & Replace better would also be welcome (Ideally, I want the replace to exclude my comments or certain cells, but could not figure out how to do them)
...ANSWER
Answered 2019-Dec-31 at 16:30Unfortunately this has to be done with a script, because regular expression string substitution on its own can't change letter case. Here's a Python script to do it:
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You can use learning-python 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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