learning-python | notes and codes while learning python | Learning library
kandi X-RAY | learning-python Summary
kandi X-RAY | learning-python Summary
notes and codes while learning python
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Top functions reviewed by kandi - BETA
- Update the score of the game
- Returns whether this object is colliding with another object
- Resets the level of the game
- Return all users
- Creates a MySQL DB connection
- Delete an entry
- Return a list of users
- Update the position of the screen
- Check bounds
- Synchronize database writer
- Wait for a message
- Main thread
- Sieve sieve
- Add new todo
- Compute the Euclidean distance between a and b
- Download comic images
- Convert xls to a csv file
- Start a Greeter server
- Removes a key from a map
- Calculate the counts for a sequence
- The main loop
- Parse command line arguments into a dictionary
- Change password
- Read data from sqlite3
- Client client
- Generate a random asteroid
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
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.
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Install learning-python
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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