I have an image dataset (folder of jpg images) .I would like to split it : 70 % for train and 30% for test randomly.
So I write this simple script:
from sklearn.model_selection import train_test_split
path = ".\dataset"
output_split=train_test_split(path,path,test_size=0.2)
But I don't find anything in folder "output_split"
So where I store output of spliting (train and test)?
I recommend using tf.dataset in your project. You can reference this link here for documentation:
https://www.tensorflow.org/api_docs/python/tf/keras/utils/image_dataset_from_directory
And this link for a example of the function use:
https://keras.io/api/preprocessing/image/
Please remember to use the same seed for both training and (testing/validation).
Please note that no changes will happen to your files in your local.
Related
Trying to use custom folders with images instead of X, y = sklearn.datasets.load_digits(return_X_y=True) for sklearn image classification tasks.
load_files does what I need, but it seems to be created for text files. Any tips for working with image files, would be appreciated.
I have the image files stored in following structure
DataSet/label1/image1.png
DataSet/label1/image2.png
DataSet/label1/image3.png
DataSet/label2/image1.png
DataSet/label2/image2.png
I had the same task and found this thread: Using sklearn load_files() to load images from png as data
Hopefully, this helps you too.
PyCaret seems like a great AutoML tool. It works, fast and simple and I would like to download the generated pipeline code into .py files to double check and if needed to customize some parts. Unfortunately, I don't know how to make it real. Reading the documentation have not helped. Is it possible or not?
It is not possible to get the underlying code since PyCaret takes care of this for you. But it is up to you as the user to decide the steps that you want your flow to take e.g.
# Setup experiment with user-defined options for preprocessing, etc.
setup(...)
# Create a model (uses training split only)
model = create_model("lr")
# Tune hyperparameters (user can pass a custom tuning grid if needed)
# Again, uses training split only
tuned = tune_model(model, ...)
# Finalize model (so that the best hyperparameters are retrained on the entire dataset
finalize_model(tuned)
# Any other steps you would like to do.
...
Finally, you can save the entire pipeline as a pkl file for use later
# Saves the model + pipeline as a pkl file
save_model(final, "my_best_model")
You may get a partial answer: incomplete with 'get_config("prep_pipe")' in 2.6.10 or in 3.0.0rc1
Just run a setup like in examples, store as a cdf1, and try cdf.pipeline and you may get a text like this: Pipeline(..)
When working with pycaret=3.0.0rc4, you have two options.
Option 1:
get_config("pipeline")
Option 2:
lb = get_leaderboard()
lb.iloc[0]['Model']
Option 1 will give you the transformations done to the data whilst option 2 will give you the same plus the model and its parameters.
Here's some sample code (from a notebook, based on their documentation on the Binary Classification Tutorial (CLF101) - Level Beginner):
from pycaret.datasets import get_data
from pycaret.classification import *
dataset = get_data('credit')
data = dataset.sample(frac=0.95, random_state=786).reset_index(drop=True)
data_unseen = dataset.drop(data.index).reset_index(drop=True)
exp_clf101 = setup(data = data, target = 'default', session_id=123)
best = compare_models()
evaluate_model(best)
# OPTION 1
get_config("pipeline")
# OPTION 2
lb = get_leaderboard()
lb.iloc[0]['Model']
I am writing a code of a well-known problem MNIST database of handwritten digits in PyTorch. I downloaded the train and testing dataset (from the main website) including the labeled dataset. The dataset format is t10k-images-idx3-ubyte.gz and after extract t10k-images-idx3-ubyte. My dataset folder looks like
MINST
Data
train-images-idx3-ubyte.gz
train-labels-idx1-ubyte.gz
t10k-images-idx3-ubyte.gz
t10k-labels-idx1-ubyte.gz
Now, I wrote a code to load data like bellow
def load_dataset():
data_path = "/home/MNIST/Data/"
xy_trainPT = torchvision.datasets.ImageFolder(
root=data_path, transform=torchvision.transforms.ToTensor()
)
train_loader = torch.utils.data.DataLoader(
xy_trainPT, batch_size=64, num_workers=0, shuffle=True
)
return train_loader
My code is showing Supported extensions are: .jpg,.jpeg,.png,.ppm,.bmp,.pgm,.tif,.tiff,.webp
How can I solve this problem and I also want to check that my images are loaded (just a figure contains the first 5 images) from the dataset?
Read this Extract images from .idx3-ubyte file or GZIP via Python
Update
You can import data using this format
xy_trainPT = torchvision.datasets.MNIST(
root="~/Handwritten_Deep_L/",
train=True,
download=True,
transform=torchvision.transforms.Compose([torchvision.transforms.ToTensor()]),
)
Now, what is happening at download=True first your code will check at the root directory (your given path) contains any datasets or not.
If no then datasets will be downloaded from the web.
If yes this path already contains a dataset then your code will work using the existing dataset and will not download from the internet.
You can check, first give a path without any dataset (data will be downloaded from the internet), and then give another path which already contains dataset data will not be downloaded.
Welcome to stackoverflow !
The MNIST dataset is not stored as images, but in a binary format (as indicated by the ubyte extension). Therefore, ImageFolderis not the type dataset you want. Instead, you will need to use the MNIST dataset class. It could even download the data if you had not done it already :)
This is a dataset class, so just instantiate with the proper root path, then put it as the parameter of your dataloader and everything should work just fine.
If you want to check the images, just use the getmethod of the dataloader, and save the result as a png file (you may need to convert the tensor to a numpy array first).
Im new to the tensorflow TFRecord. so Im studying Tensorflow object detection API codes
https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/using_your_own_dataset.md
but I can`t find the codes that load tfrecord.
I think they use .config file to load tfrecord because I found this in config file.
tf_record_input_reader {
input_path: "/path/to/train_dataset.record-?????-of-00010"
}
Anyone can help?
Have you converted your dataset to TFRecord format yet?
If so, you should have a path which contains your training dataset sharded to a few record files, with the format
<path_to_training_data>/<train_dataset>.record-?????-of-xxxxx
Where <path_to_training_data> is the abovementioned path to your training dataset, <train_dataset> is the file name you gave to each file, xxxxx is the number of record files created (e.g. 00010), and ????? should be left as is, and is used as a format to all record files.
Once you've replaced <path_to_training_data>, <train_dataset> and xxxxx to the correct values of your dataset, TF OD API should handle everything else (finding all records, reading them, etc.).
Note there's usually tf_record_input_reader for both training dataset and eval dataset (validation/test), and each should have the corresponding above-mentioned values (path, dataset name, number of files).
Hi I want classify a dataset using naivebayesclassifier.For that I want to use external dataset which i have downloaded from google.this dataset contains a two folder for positive reviews and negative reviews.Each folder contains 1000 .txt files.How to import this file in my code as a train dataset in python.I am new to machine learning so I have very less idea about that.Please help me out.
You can use os.listdir, from (https://docs.python.org/2/library/os.html), e.g.:
import os
fileList = os.listdir('train_directory')
for file in fileList:
# add content of file to dataset.