Search is not available for this dataset
repo stringlengths 2 152 ⌀ | file stringlengths 15 239 | code stringlengths 0 58.4M | file_length int64 0 58.4M | avg_line_length float64 0 1.81M | max_line_length int64 0 12.7M | extension_type stringclasses 364
values |
|---|---|---|---|---|---|---|
realistic-ssl-evaluation | realistic-ssl-evaluation-master/runs/figure-4-svhn-1000-vat-unl10000.yml | session_name: figure-4-svhn-1000-vat-unl10000
windows:
- window_name: experiments
layout: even-vertical
shell_command_before: cd /root/realistic-ssl-evaluation
panes:
- CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --primary_dataset_name='svhn' --sec... | 1,539 | 153 | 394 | yml |
realistic-ssl-evaluation | realistic-ssl-evaluation-master/runs/figure-4-svhn-1000-vat-unl160000.yml | session_name: figure-4-svhn-1000-vat-unl160000
windows:
- window_name: experiments
layout: even-vertical
shell_command_before: cd /root/realistic-ssl-evaluation
panes:
- CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --primary_dataset_name='svhn' --se... | 1,547 | 153.8 | 395 | yml |
realistic-ssl-evaluation | realistic-ssl-evaluation-master/runs/figure-4-svhn-1000-vat-unl20000.yml | session_name: figure-4-svhn-1000-vat-unl20000
windows:
- window_name: experiments
layout: even-vertical
shell_command_before: cd /root/realistic-ssl-evaluation
panes:
- CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --primary_dataset_name='svhn' --sec... | 1,539 | 153 | 394 | yml |
realistic-ssl-evaluation | realistic-ssl-evaluation-master/runs/figure-4-svhn-1000-vat-unl320000.yml | session_name: figure-4-svhn-1000-vat-unl320000
windows:
- window_name: experiments
layout: even-vertical
shell_command_before: cd /root/realistic-ssl-evaluation
panes:
- CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --primary_dataset_name='svhn' --se... | 1,547 | 153.8 | 395 | yml |
realistic-ssl-evaluation | realistic-ssl-evaluation-master/runs/figure-4-svhn-1000-vat-unl40000.yml | session_name: figure-4-svhn-1000-vat-unl40000
windows:
- window_name: experiments
layout: even-vertical
shell_command_before: cd /root/realistic-ssl-evaluation
panes:
- CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --primary_dataset_name='svhn' --sec... | 1,539 | 153 | 394 | yml |
realistic-ssl-evaluation | realistic-ssl-evaluation-master/runs/figure-4-svhn-1000-vat-unl531131.yml | session_name: figure-4-svhn-1000-vat-unl531131
windows:
- window_name: experiments
layout: even-vertical
shell_command_before: cd /root/realistic-ssl-evaluation
panes:
- CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --primary_dataset_name='svhn' --se... | 1,536 | 152.7 | 384 | yml |
realistic-ssl-evaluation | realistic-ssl-evaluation-master/runs/figure-4-svhn-1000-vat-unl80000.yml | session_name: figure-4-svhn-1000-vat-unl80000
windows:
- window_name: experiments
layout: even-vertical
shell_command_before: cd /root/realistic-ssl-evaluation
panes:
- CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --primary_dataset_name='svhn' --sec... | 1,538 | 152.9 | 393 | yml |
realistic-ssl-evaluation | realistic-ssl-evaluation-master/runs/section-4-3-cifar-filtered-file-tuning.yml | session_name: cifar-fine-tuning
windows:
- window_name: experiments
layout: even-vertical
shell_command_before: cd /root/realistic-ssl-evaluation
panes:
- CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --load_checkpoint=/mnt/experiment-logs/section-4-... | 1,679 | 151.727273 | 475 | yml |
realistic-ssl-evaluation | realistic-ssl-evaluation-master/runs/section-4-3-cifar-fine-tuning.yml | session_name: cifar-fine-tuning
windows:
- window_name: experiments
layout: even-vertical
shell_command_before: cd /root/realistic-ssl-evaluation
panes:
- CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --load_checkpoint=/mnt/experiment-logs/section-4-... | 1,598 | 144.363636 | 448 | yml |
realistic-ssl-evaluation | realistic-ssl-evaluation-master/runs/section-4-3-filtered-imagenet-pre-training.yml | session_name: imagenet-pre-training
windows:
- window_name: experiments
layout: even-vertical
shell_command_before: cd /root/realistic-ssl-evaluation
panes:
- CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --zca_input_file_path='data/' --primary_datas... | 13,404 | 1,217.636364 | 3,307 | yml |
realistic-ssl-evaluation | realistic-ssl-evaluation-master/runs/section-4-3-imagenet-pre-training.yml | session_name: imagenet-pre-training
windows:
- window_name: experiments
layout: even-vertical
shell_command_before: cd /root/realistic-ssl-evaluation
panes:
- CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --zca_input_file_path='data/' --primary_datas... | 1,569 | 141.727273 | 367 | yml |
realistic-ssl-evaluation | realistic-ssl-evaluation-master/runs/table-1-cifar10-4000-fullysup.yml | session_name: table-1-cifar10-4000-fullysup
windows:
- window_name: experiments
layout: even-vertical
shell_command_before: cd /root/realistic-ssl-evaluation
panes:
- CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --primary_dataset_name='cifar10' --se... | 1,431 | 129.181818 | 347 | yml |
realistic-ssl-evaluation | realistic-ssl-evaluation-master/runs/table-1-cifar10-4000-mean-teacher.yml | session_name: table-1-cifar10-4000-mean-teacher
windows:
- window_name: experiments
layout: even-vertical
shell_command_before: cd /root/realistic-ssl-evaluation
panes:
- CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --primary_dataset_name='cifar10' ... | 1,554 | 154.5 | 359 | yml |
realistic-ssl-evaluation | realistic-ssl-evaluation-master/runs/table-1-cifar10-4000-pi-model.yml | session_name: table-1-cifar10-4000-pi-model
windows:
- window_name: experiments
layout: even-vertical
shell_command_before: cd /root/realistic-ssl-evaluation
panes:
- CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --primary_dataset_name='cifar10' --se... | 1,502 | 149.3 | 347 | yml |
realistic-ssl-evaluation | realistic-ssl-evaluation-master/runs/table-1-cifar10-4000-pseudo-label.yml | session_name: table-1-cifar10-4000-pseudo-label
windows:
- window_name: experiments
layout: even-vertical
shell_command_before: cd /root/realistic-ssl-evaluation
panes:
- CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --primary_dataset_name='cifar10' ... | 1,554 | 154.5 | 359 | yml |
realistic-ssl-evaluation | realistic-ssl-evaluation-master/runs/table-1-cifar10-4000-vat-em.yml | session_name: table-1-cifar10-4000-vat-em
windows:
- window_name: experiments
layout: even-vertical
shell_command_before: cd /root/realistic-ssl-evaluation
panes:
- CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --primary_dataset_name='cifar10' --seco... | 1,584 | 157.5 | 368 | yml |
realistic-ssl-evaluation | realistic-ssl-evaluation-master/runs/table-1-cifar10-4000-vat.yml | session_name: table-1-cifar10-4000-vat
windows:
- window_name: experiments
layout: even-vertical
shell_command_before: cd /root/realistic-ssl-evaluation
panes:
- CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --primary_dataset_name='cifar10' --seconda... | 1,437 | 142.8 | 332 | yml |
realistic-ssl-evaluation | realistic-ssl-evaluation-master/runs/table-1-svhn-1000-fullysup.yml | session_name: table-1-svhn-1000-fullysup
windows:
- window_name: experiments
layout: even-vertical
shell_command_before: cd /root/realistic-ssl-evaluation
panes:
- CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --primary_dataset_name='svhn' --secondar... | 1,389 | 125.363636 | 335 | yml |
realistic-ssl-evaluation | realistic-ssl-evaluation-master/runs/table-1-svhn-1000-mean-teacher.yml | session_name: table-1-svhn-1000-mean-teacher
windows:
- window_name: experiments
layout: even-vertical
shell_command_before: cd /root/realistic-ssl-evaluation
panes:
- CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --primary_dataset_name='svhn' --seco... | 1,512 | 150.3 | 347 | yml |
realistic-ssl-evaluation | realistic-ssl-evaluation-master/runs/table-1-svhn-1000-pi-model.yml | session_name: table-1-svhn-1000-pi-model
windows:
- window_name: experiments
layout: even-vertical
shell_command_before: cd /root/realistic-ssl-evaluation
panes:
- CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --primary_dataset_name='svhn' --secondar... | 1,460 | 145.1 | 335 | yml |
realistic-ssl-evaluation | realistic-ssl-evaluation-master/runs/table-1-svhn-1000-pseudo-label.yml | session_name: table-1-svhn-1000-pseudo-label
windows:
- window_name: experiments
layout: even-vertical
shell_command_before: cd /root/realistic-ssl-evaluation
panes:
- CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --primary_dataset_name='svhn' --seco... | 1,512 | 150.3 | 347 | yml |
realistic-ssl-evaluation | realistic-ssl-evaluation-master/runs/table-1-svhn-1000-vat-em.yml | session_name: table-1-svhn-1000-vat-em
windows:
- window_name: experiments
layout: even-vertical
shell_command_before: cd /root/realistic-ssl-evaluation
panes:
- CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --primary_dataset_name='svhn' --secondary_... | 1,542 | 153.3 | 356 | yml |
realistic-ssl-evaluation | realistic-ssl-evaluation-master/runs/table-1-svhn-1000-vat.yml | session_name: table-1-svhn-1000-vat
windows:
- window_name: experiments
layout: even-vertical
shell_command_before: cd /root/realistic-ssl-evaluation
panes:
- CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --primary_dataset_name='svhn' --secondary_dat... | 1,395 | 138.6 | 320 | yml |
realistic-ssl-evaluation | realistic-ssl-evaluation-master/third_party/vat/__init__.py | 0 | 0 | 0 | py | |
realistic-ssl-evaluation | realistic-ssl-evaluation-master/third_party/vat/vat_utils.py | """Utilities derived from the VAT code."""
import tensorflow as tf
def generate_virtual_adversarial_perturbation(x, logit, forward, hps):
"""Generate an adversarial perturbation.
Args:
x: Model inputs.
logit: Original model output without perturbation.
forward: Callable which computs ... | 1,682 | 28.526316 | 79 | py |
whiiif | whiiif-master/LICENSE.md | GNU Lesser General Public License
=================================
_Version 3, 29 June 2007_
_Copyright © 2007 Free Software Foundation, Inc. <<http://fsf.org/>>_
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
This version of the GNU L... | 7,523 | 44.878049 | 80 | md |
whiiif | whiiif-master/README.md | # Whiiif - Word Highlighting (in) IIIF
Whiiif is an implementation of the [IIIF Search API](https://iiif.io/api/search/1.0/) designed to provide full-text
search with granular, word-level Annotation results to enable front-end highlighting.
OCR transcriptions are ingested from word-level [ALTO](https://www.loc.gov/st... | 3,498 | 47.597222 | 327 | md |
whiiif | whiiif-master/_config.yml | theme: jekyll-theme-slate | 25 | 25 | 25 | yml |
whiiif | whiiif-master/setup.py | from setuptools import setup, find_packages
setup(
name='whiiif',
version='2.0.0',
long_description=" ",
packages=find_packages(),
include_package_data=True,
zip_safe=False,
install_requires=[
'flask',
'requests',
'lxml',
'bleach'
],
author="Mike Benn... | 963 | 32.241379 | 223 | py |
whiiif | whiiif-master/tests/manifests.py | COLLECTION_ONE = """
{
"@context": "http://iiif.io/api/presentation/2/context.json",
"@id": "http://mytestserver/manifests/collection-manifest",
"@type": "sc:Manifest",
"label": "Whiiif Test Manifest 1",
"viewingHint": "individuals",
"service": [
{
"@context": "http://iiif.io... | 6,804 | 42.903226 | 114 | py |
whiiif | whiiif-master/tests/solr_responses.py | IIIF = {
"responseHeader": {
"status": 0,
},
"response": {"numFound": 1, "start": 0, "docs": [
{
"id": "test-manifest",
"manifest_url": "http://mytestserver/manifests/test-manifest",
"ocr_text": "/test/manifests/test-manifest"
}]
}... | 13,951 | 37.86351 | 111 | py |
whiiif | whiiif-master/tests/test_whiiif.py | import unittest
from unittest.mock import patch, mock_open
from whiiif import app
import solr_responses
import manifests
from requests.exceptions import ConnectionError
class FakeResponse(object):
"""Class to simulate the responses from SOLR via monkeypatching the request.get calls"""
status_code = 200
... | 29,967 | 60.789691 | 120 | py |
whiiif | whiiif-master/utils/__init__.py | 0 | 0 | 0 | py | |
whiiif | whiiif-master/utils/iiif_order.py | #!/usr/bin/python3
""" iiif_order.py: tools for ordering the json structure of IIIF objects """
from collections import OrderedDict
start_keys = ["@context", "@id", "@type"]
descriptive_keys = ["label", "metadata", "description", "thumbnail", "attribution", "license", "logo"]
linking_keys = ["seeAlso", "service", "r... | 3,421 | 44.626667 | 102 | py |
whiiif | whiiif-master/utils/index_with_plugin.py | #!/usr/bin/python3
""" index_with_plugin.py: indexing tool for whiiif, using solr-ocrhighlighting """
import argparse
import json
from collections import OrderedDict
from os import path
import requests
from iiif_order import order_object
from whiiif import app
__version__ = '0.3.0'
DEBUG = True
def dprint(*args):... | 4,845 | 34.896296 | 111 | py |
whiiif | whiiif-master/whiiif/__init__.py | import os
from flask import Flask
app = Flask(__name__)
app.config.from_object('whiiif.default_settings')
app.config.from_envvar('WHIIIF_SETTINGS', silent=True)
#from werkzeug.contrib.fixers import ProxyFix
#app.wsgi_app = ProxyFix(app.wsgi_app)
if not app.debug:
import logging
from logging.handlers import ... | 739 | 32.636364 | 106 | py |
whiiif | whiiif-master/whiiif/default_settings.py | # WHIIIF DEFAULT SETTINGS FILE
# Rather than change values here, it's probably better to override the variables in the settings.cfg file
# REQUIRED SETTINGS
# General Whiiif Settings
SERVER_NAME = 'localhost:5000'
LOG_DIR = '.' # create log files in current working directory
# SOLR Configuration
SOLR_URL = 'http://l... | 1,933 | 52.722222 | 114 | py |
whiiif | whiiif-master/whiiif/views.py | import json
import re
from os import path
import bleach
import requests
from flask import render_template, request
from whiiif import app
@app.route('/')
def index():
app.logger.debug('Index page loaded - this should (currently) only occur when tests are being run')
return render_template('index.html')
@a... | 14,234 | 45.368078 | 130 | py |
whiiif | whiiif-master/whiiif/templates/index.html | <!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Whiiif</title>
</head>
<body>
Welcome to Whiiif
</body>
</html>
| 154 | 13.090909 | 26 | html |
null | id-reveal-main/README.md | ## ID-Reveal: Identity-aware Deepfake Video Detection
Davide Cozzolino, Andreas Rössler, Justus Thies, Matthias Nießner, Luisa Verdoliva
[](https://grip-unina.github.io/id-reveal/)
[:
s1 = (y.shape[-1] - x.shape[-1]) // 2
e1 = s1 + x.shape[-1]
y = y[..., s1:e1]
if x.shape[1] > y.shape[1]:
d = [int(i) for i in y.shape]
d[1] = int(x.shape[1] - y.shape[1])
y = torch.cat((y, torch.zeros(d, dtype=y.dtype, device=y.device)), -3... | 5,638 | 40.463235 | 135 | py |
null | id-reveal-main/docs/_config.yml | remote_theme: grip-unina/webtemplate
plugins:
- jekyll
- jekyll-seo-tag
| 76 | 14.4 | 36 | yml |
null | id-reveal-main/docs/index.md | ---
layout: paper
paper: ID-Reveal: Identity-aware DeepFake Video Detection
github_url: https://github.com/grip-unina/id-reveal
authors:
- name: Davide Cozzolino
link: http://www.grip.unina.it/people/userprofile/davide_cozzolino.html
index: 1
- name: Andreas Rössler
link: http://niessnerlab.org/... | 3,988 | 65.483333 | 1,100 | md |
typeface-corpus | typeface-corpus-master/README.md | typeface-corpus
===============
The repository is initially focused on compiling data that is relevant to the OCR activities conducted in the natural history collections communities and in the digital humanities communities. These communities face the challenge of needing to extract high-quality text from documents an... | 755 | 107 | 600 | md |
typeface-corpus | typeface-corpus-master/submission_procedures.md | OCR Typeface Corpus Repository - Submission Procedures
Introduction
The following procedures outline the scope, purpose, and process for data, code, and documentation to be submitted to the OCR Typeface Corpus Repository (hereinafter ‘the repository’). Submissions to this repository will help inform and enhance the p... | 6,378 | 79.746835 | 600 | md |
null | QCNN-main/LICENSE.md | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
... | 11,357 | 55.227723 | 77 | md |
null | QCNN-main/README.md | # QOSF_project
This is an implementation of Quantum convolutional neural network for classical data classification (https://arxiv.org/abs/2108.00661). It uses Pennylane software (https://pennylane.ai) for classifying MNIST and Fashion MNIST datasets.
### 1. Data Preparation
**"data.py"**: loads dataset (MNIST or Fash... | 2,608 | 78.060606 | 336 | md |
null | QCNN-main/QCNN/Angular_hybrid.py | # This is an implementation of an alternative Mottonen State Preparation to avoid normalization problem.
import pennylane as qml
# 3 bits of information is embedded in 2 wires
def Angular_Hybrid_2(X, wires):
qml.RY(X[0], wires=wires[0])
qml.PauliX(wires=wires[0])
qml.CRY(X[1], wires=[wires[0], wires[1]])
... | 1,560 | 32.934783 | 104 | py |
null | QCNN-main/QCNN/Benchmarking.py | import data
import Training
import QCNN_circuit
import Hierarchical_circuit
import numpy as np
def accuracy_test(predictions, labels, cost_fn, binary = True):
if cost_fn == 'mse':
if binary == True:
acc = 0
for l, p in zip(labels, predictions):
if np.abs(l - p) < 1:
... | 9,069 | 34.708661 | 144 | py |
null | QCNN-main/QCNN/CNN.py | import data
import numpy as np
import torch
import torch.nn as nn
def get_n_params(model):
np=0
for p in list(model.parameters()):
np += p.nelement()
return np
def accuracy_test(predictions, labels):
acc = 0
for (p,l) in zip(predictions, labels):
if p[0] >= p[1]:
pred =... | 3,732 | 34.552381 | 138 | py |
null | QCNN-main/QCNN/Hierarchical_circuit.py | # Implementaion of Hierarchical Quantum Classifier Structure.
import pennylane as qml
import unitary
import embedding
dev_TTN = qml.device('default.qubit', wires=8)
def Hierarchical_structure(U, params, U_params):
param1 = params[0 * U_params:1 * U_params]
param2 = params[1 * U_params:2 * U_params]
param3... | 1,975 | 32.491525 | 105 | py |
null | QCNN-main/QCNN/Hybrid_embedding_accuracy.py | # This module implements the measurement method of the entangling capability
import pennylane as qml
import QCNN_circuit
import unitary
import embedding
import numpy as np
import data
import Training
from Benchmarking import Encoding_to_Embedding, accuracy_test
dev = qml.device('default.qubit', wires=8)
@qml.qnode(d... | 4,715 | 41.107143 | 144 | py |
null | QCNN-main/QCNN/Hybrid_embedding_entanglement.py | import numpy as np
import Hybrid_embedding_accuracy
best_params_AE12_1 = np.array([-0.2291373, -0.50155557, -1.59247677, -0.72832542, -0.30422235, -0.92627882,
1.24700552, 1.30503354, 1.35506516, 0.91254374, -0.47094015, -0.16747956,
1.18360927, 0.39397264, 1.46964729])
best_params_AE12_2 = np.array([-1.7404... | 9,044 | 69.116279 | 179 | py |
null | QCNN-main/QCNN/QCNN_circuit.py | import pennylane as qml
import unitary
import embedding
# Quantum Circuits for Convolutional layers
def conv_layer1(U, params):
U(params, wires=[0, 7])
for i in range(0, 8, 2):
U(params, wires=[i, i + 1])
for i in range(1, 7, 2):
U(params, wires=[i, i + 1])
def conv_layer2(U, params):
U... | 3,483 | 29.034483 | 86 | py |
null | QCNN-main/QCNN/Training.py | # Implementation of Quantum circuit training procedure
import QCNN_circuit
import Hierarchical_circuit
import pennylane as qml
from pennylane import numpy as np
import autograd.numpy as anp
def square_loss(labels, predictions):
loss = 0
for l, p in zip(labels, predictions):
loss = loss + (l - p) ** 2
... | 2,275 | 35.126984 | 136 | py |
null | QCNN-main/QCNN/data.py | # Loads and Processes the data that will be used in QCNN and Hierarchical Classifier Training
import numpy as np
import tensorflow as tf
from sklearn.decomposition import PCA
from tensorflow.keras.models import Model
from tensorflow.keras import layers, losses
pca32 = ['pca32-1', 'pca32-2', 'pca32-3', 'pca32-4']
autoen... | 6,591 | 43.843537 | 126 | py |
null | QCNN-main/QCNN/embedding.py | # This is an implementation of data_embedding function used for 8 qubits Quantum Convolutional Neural Network (QCNN)
# and Hierarchical Quantum Classifier circuit.
import pennylane as qml
from pennylane.templates.embeddings import AmplitudeEmbedding, AngleEmbedding
from pennylane.templates.state_preparations import Mot... | 5,710 | 45.811475 | 120 | py |
null | QCNN-main/QCNN/result.py | # This generates the results of the bechmarking code
import Benchmarking
"""
Here are possible combinations of benchmarking user could try.
Unitaries: ['U_TTN', 'U_5', 'U_6', 'U_9', 'U_13', 'U_14', 'U_15', 'U_SO4', 'U_SU4', 'U_SU4_no_pooling', 'U_SU4_1D', 'U_9_1D']
U_num_params: [2, 10, 10, 2, 6, 6, 4, 6, 15, 15, 15... | 1,254 | 42.275862 | 136 | py |
null | QCNN-main/QCNN/unitary.py | # This module contains the set of unitary ansatze that will be used to benchmark the performances of Quantum Convolutional Neural Network (QCNN) in QCNN.ipynb module
import pennylane as qml
# Unitary Ansatze for Convolutional Layer
def U_TTN(params, wires): # 2 params
qml.RY(params[0], wires=wires[0])
qml.RY(... | 3,602 | 32.672897 | 165 | py |
null | QCNN-main/QCNN/Result/Data/CNN_result.py | import numpy as np
CNN_resize256_adam = np.array([0.96, 0.9585, 0.5, 0.9535, 0.9525])
CNN_pca8_adam = np.array([0.925, 0.91, 0.8745, 0.8985, 0.935])
CNN_ae8_adam = np.array([0.928, 0.9495, 0.9495, 0.9215, 0.9395])
CNN_pca16_adam = np.array([0.906, 0.944, 0.909, 0.9465, 0.9195])
CNN_ae16_adam = np.array([0.961, 0.952,... | 1,727 | 65.461538 | 120 | py |
null | QCNN-main/QCNN/Result/Data/Hybrid_embedding(ansatz)_accuracy.py | 0 | 0 | 0 | py | |
null | QCNN-main/QCNN/Result/Data/Hybrid_embedding(structure)_accuracy.py | # This is an accuracy result for various hybrid embedding
import numpy as np
######### Angular Hybrid 2 #########
AE12_1 = np.array([0.8822695035460993, 0.9744680851063829, 0.9536643026004729, 0.9891252955082742, 0.9593380614657211])
AE12_2 = np.array([0.9621749408983451, 0.9862884160756501, 0.8884160756501182, 0.9702... | 7,900 | 58.856061 | 120 | py |
null | QCNN-main/QCNN/Result/Data/QCNN_1D_chain_result.py | # This is a mean and standard deviation of 1D chain QCNN with SU4 unitary ansatz
import numpy as np
U9_resize256_MNIST = np.array([0.858628841607565, 0.9460992907801419, 0.9281323877068558, 0.798581560283688, 0.7976359338061466])
U9_AE8_MNIST = np.array([0.851063829787234, 0.9550827423167849, 0.9309692671394799, 0.968... | 4,051 | 70.087719 | 131 | py |
null | QCNN-main/QCNN/Result/Data/QCNN_double_layer_result.py | # This is a mean and standard deviation of Double Layer QCNN with U9 and SU4 unitary ansatz
import numpy as np
U9_resize256_MNIST = np.array([0.8884160756501182, 0.891725768321513, 0.9347517730496454, 0.8222222222222222, 0.8600472813238771])
U9_AE8_MNIST = np.array([0.9598108747044918, 0.9465721040189126, 0.9527186761... | 4,079 | 68.152542 | 132 | py |
null | QCNN-main/QCNN/Result/Data/QCNN_layers_plot.py | 0 | 0 | 0 | py | |
null | QCNN-main/QCNN/Result/Data/QCNN_triple_layer_result.py | # This is a mean and standard deviation of Triple Layer QCNN with U9 and SU4 unitary ansatz
import numpy as np
U9_resize256_MNIST = np.array([0.924822695035461, 0.9011820330969267, 0.9200945626477541, 0.9739952718676123, 0.9716312056737588])
U9_AE8_MNIST = np.array([0.9408983451536643, 0.9801418439716312, 0.9706855791... | 4,064 | 72.909091 | 131 | py |
null | QCNN-main/QCNN/Result/Data/loss_history.py | import matplotlib.pyplot as plt
from Benchmarking import Encoding_to_Embedding
from data import data_load_and_process
from Training import circuit_training
import numpy as np
# Loss histories in order of pca8, autoencoder8, pca16, autoencoder16
loss_histories_CNN_MNIST = np.array([[0.714053750038147, 0.6757248044013977... | 17,856 | 342.403846 | 4,115 | py |
null | QCNN-main/QCNN/Result/Data/CNN/CNN_result.py | import numpy as np
pca8_MNIST_L3 = np.array([0.5366430260047281, 0.9867612293144208, 0.9744680851063829, 0.9768321513002364, 0.9820330969267139, 0.9773049645390071, 0.9749408983451536, 0.9787234042553191, 0.984869976359338, 0.957919621749409 ])
ae8_MNIST_L3 = np.array([0.8297872340425532, 0.9352245862884161, 0.9635933... | 4,274 | 86.244898 | 227 | py |
null | QCNN-main/QCNN/Result/Data/CNN/loss_history.py | import matplotlib.pyplot as plt
import numpy as np
# Loss histories in order of pca8, autoencoder8, pca16, autoencoder16
loss_histories_CNN_MNIST_3L = np.array([[0.6950972676277161, 0.624514102935791, 0.583101212978363, 0.6993337869644165, 0.6476080417633057, 0.6364650130271912, 0.6012523174285889, 0.6161369681358337,... | 130,040 | 1,584.865854 | 4,234 | py |
null | QCNN-main/QCNN/Result/Data/CNN/loss_history_plot.py | import numpy as np
import matplotlib.pyplot as plt
def mean_std(x):
x1, x2, x3, x4, x5 = x[0], x[1], x[2], x[3], x[4]
mean, std = [], []
for i in range(200):
group = np.array([x2[i], x2[i], x3[i], x4[i], x5[i]])
mean.append(group.mean())
std.append(group.std())
return np.array([... | 325,794 | 1,528.553991 | 4,245 | py |
null | QCNN-main/QCNN/Result/Data/Hybrid Embedding/CrossEntropy_Hybrid.py | # Result for Hybrid Embedding Accuracy
import numpy as np
#MNIST
TTN_pca30_mnist_mse = np.array([0.9829787234042553, 0.9749408983451536, 0.9754137115839243, 0.9820330969267139, 0.9810874704491725])
TTN_ae30_mnist_mse = np.array([0.6657210401891253, 0.7673758865248227, 0.7900709219858156, 0.826950354609929, 0.832624113... | 23,763 | 79.829932 | 134 | py |
null | QCNN-main/QCNN/Result/Data/Hybrid Embedding/MSE_Hybrid.py | # Result for Hybrid Embedding Accuracy
import numpy as np
#MNIST
TTN_pca30_mnist_mse = np.array([0.9858156028368794, 0.9839243498817967, 0.9839243498817967, 0.9796690307328605, 0.9546099290780142])
TTN_ae30_mnist_mse = np.array([0.8591016548463357, 0.7848699763593381, 0.7408983451536643, 0.6865248226950355, 0.82222222... | 16,335 | 77.917874 | 134 | py |
null | QCNN-main/QCNN/Result/Data/QCNN 1D/QCNN_1D.py | 0 | 0 | 0 | py | |
null | QCNN-main/QCNN/Result/Data/QCNN 1D/QCNN_1D_chain_result(crossentropy).py | # This is a mean and standard deviation of 1D chain QCNN with SU4 unitary ansatz
import numpy as np
U9_resize256_MNIST = np.array([0.8969267139479905, 0.866193853427896, 0.9016548463356974, 0.9437352245862884, 0.9111111111111111])
U9_PCA8_MNIST = np.array([0.9763593380614657, 0.9825059101654846, 0.9782505910165484, 0.... | 4,098 | 69.672414 | 132 | py |
null | QCNN-main/QCNN/Result/Data/QCNN 1D/QCNN_1D_chain_result(mse).py | # This is a mean and standard deviation of 1D chain QCNN with SU4 unitary ansatz
import numpy as np
U9_resize256_MNIST = np.array([0.858628841607565, 0.9460992907801419, 0.9281323877068558, 0.798581560283688, 0.7976359338061466])
U9_AE8_MNIST = np.array([0.851063829787234, 0.9550827423167849, 0.9309692671394799, 0.968... | 4,064 | 70.315789 | 131 | py |
kserve | kserve-master/CONTRIBUTING.md | See [How to contribute](https://github.com/kserve/community#how-can-i-help-) in [KServe community repository](https://github.com/kserve/community). | 147 | 147 | 147 | md |
kserve | kserve-master/README.md | # KServe
[](https://pkg.go.dev/github.com/kserve/kserve)
[](https://githu... | 5,156 | 91.089286 | 485 | md |
kserve | kserve-master/ROADMAP.md | # KServe 2023 Roadmap
## Objective: "Graduate core inference capability to stable/GA"
- Promote `InferenceService` and `ClusterServingRuntime`/`ServingRuntime` CRD from v1beta1 to v1
* Improve `InferenceService` CRD for REST/gRPC protocol interface
* Unify model storage spec and implementation between KServe and ... | 3,676 | 57.365079 | 366 | md |
kserve | kserve-master/SECURITY.md | ## Reporting a Vulnerability
We strongly encourage you to report security vulnerabilities to
our private security mailing list: kserve-security@lists.lfaidata.foundation - first, before
disclosing them in any public forums.
This is a private mailing list where only active maintainers of the project are allowed to be ... | 436 | 47.555556 | 139 | md |
kserve | kserve-master/agent.Dockerfile | # Build the inference-agent binary
FROM golang:1.20 as builder
# Copy in the go src
WORKDIR /go/src/github.com/kserve/kserve
COPY go.mod go.mod
COPY go.sum go.sum
RUN go mod download
COPY pkg/ pkg/
COPY cmd/ cmd/
# Build
RUN CGO_ENABLED=0 GOOS=linux go build -a -o agent ./cmd/agent
# Copy the inference-age... | 523 | 21.782609 | 67 | Dockerfile |
kserve | kserve-master/coverage.sh | #!/bin/sh
while read p || [ -n "$p" ]
do
sed -i "/${p}/d" ./coverage.out
done < ./.cov-ignore
go tool cover -func coverage.out > coverage.cov
tail -1 coverage.cov | 167 | 15.8 | 47 | sh |
kserve | kserve-master/prow_config.yaml | # This file configures the workflows to trigger in our Prow jobs.
# see https://github.com/kubeflow/testing/blob/master/py/kubeflow/testing/run_e2e_workflow.py
workflows: []
# - app_dir: kserve/kserve/test/workflows
# component: workflows
# name: e2e
# job_types:
# - presubmit
# include_dirs:
... | 912 | 25.085714 | 93 | yaml |
kserve | kserve-master/router.Dockerfile | # Build the inference-router binary
FROM golang:1.20 as builder
# Copy in the go src
WORKDIR /go/src/github.com/kserve/kserve
COPY go.mod go.mod
COPY go.sum go.sum
RUN go mod download
COPY pkg/ pkg/
COPY cmd/ cmd/
# Build
RUN CGO_ENABLED=0 go build -a -o router ./cmd/router
# Copy the inference-router int... | 519 | 21.608696 | 68 | Dockerfile |
kserve | kserve-master/.github/PULL_REQUEST_TEMPLATE.md | <!-- Thanks for sending a pull request! Here are some tips for you:
1. If this is your first time, read our contributor guidelines https://www.kubeflow.org/docs/about/contributing/ and developer guide https://github.com/kserve/kserve/blob/master/docs/DEVELOPER_GUIDE.md
2. If you want *faster* PR reviews, read how: ht... | 2,211 | 44.142857 | 201 | md |
kserve | kserve-master/.github/ISSUE_TEMPLATE/bug_report.md | ---
name: Bug report
about: Tell us about a problem you are experiencing
---
/kind bug
**What steps did you take and what happened:**
[A clear and concise description of what the bug is.]
**What did you expect to happen:**
**What's the InferenceService yaml:**
[To help us debug please run `kubectl get isvc $name... | 761 | 22.090909 | 96 | md |
kserve | kserve-master/.github/ISSUE_TEMPLATE/feature_request.md | ---
name: Feature enhancement request
about: Suggest an idea for this project
---
/kind feature
**Describe the solution you'd like**
[A clear and concise description of what you want to happen.]
**Anything else you would like to add:**
[Miscellaneous information that will assist in solving the issue.]
| 308 | 19.6 | 66 | md |
kserve | kserve-master/.github/actions/base-download/action.yml | name: 'Base Download Actions'
description: 'A wrapper around download docker artifact to download a set of base images'
runs:
using: "composite"
steps:
- name: Download controller image
uses: ishworkh/docker-image-artifact-download@v1
with:
image: "kserve/kserve-controller:${{ github.sha }... | 657 | 28.909091 | 89 | yml |
kserve | kserve-master/.github/actions/kserve-dep-setup/action.yml | name: 'Kserve dependency setup action'
description: 'Sets up Kserve dependencies on the running kubernetes cluster'
inputs:
network-layer:
description: 'Network layer to install'
required: true
default: 'istio'
runs:
using: "composite"
steps:
- name: Setup KServe dependencies
shell: bash
... | 1,298 | 31.475 | 76 | yml |
kserve | kserve-master/.github/actions/minikube-setup/action.yml | name: 'Minikube setup action'
description: 'Sets up minikube on the github runner'
runs:
using: "composite"
steps:
- name: Free up disk space
shell: bash
run: |
echo "Disk usage before cleanup:"
df -h
# remove non-essential tools and libraries, see:
# https://github... | 1,433 | 26.056604 | 92 | yml |
kserve | kserve-master/.github/workflows/agent-docker-publish.yml | name: Agent Docker Publisher
on:
push:
# Publish `master` as Docker `latest` image.
branches:
- master
# Publish `v1.2.3` tags as releases.
tags:
- v*
# Run tests for any PRs.
pull_request:
env:
IMAGE_NAME: agent
jobs:
# Run tests.
# See also https://docs.docker.com/docker-h... | 2,351 | 25.133333 | 100 | yml |
kserve | kserve-master/.github/workflows/alibiexplainer-docker-publish.yml | name: Alibi Explainer Docker Publisher
on:
push:
# Publish `master` as Docker `latest` image.
branches:
- master
# Publish `v1.2.3` tags as releases.
tags:
- v*
# Run tests for any PRs.
pull_request:
env:
IMAGE_NAME: alibi-explainer
jobs:
# Run tests.
# See also https://doc... | 2,047 | 24.6 | 100 | yml |
kserve | kserve-master/.github/workflows/artexplainer-docker-publish.yml | name: ARTExplainer Docker Publisher
on:
push:
# Publish `master` as Docker `latest` image.
branches:
- master
# Publish `v1.2.3` tags as releases.
tags:
- v*
# Run tests for any PRs.
pull_request:
env:
IMAGE_NAME: art-explainer
jobs:
# Run tests.
# See also https://docs.dock... | 2,037 | 24.475 | 100 | yml |
kserve | kserve-master/.github/workflows/cherry-pick.yml | on:
pull_request:
branches:
- main
- master
types: ["closed"]
jobs:
cherry_pick_release_0_10:
runs-on: ubuntu-latest
name: Cherry pick into release-0.10
if: ${{ contains(github.event.pull_request.labels.*.name, 'release-0.10') && github.event.pull_request.merged == true }}
steps... | 707 | 24.285714 | 124 | yml |
kserve | kserve-master/.github/workflows/custom-model-grpc-publish.yml | name: Custom model gRPC Docker Publisher
on:
push:
# Publish `master` as Docker `latest` image.
branches:
- master
# Run tests for any PRs.
pull_request:
env:
IMAGE_NAME: custom-model-grpc
jobs:
# Run tests.
# See also https://docs.docker.com/docker-hub/builds/automated-testing/
test:
... | 1,993 | 25.236842 | 100 | yml |
kserve | kserve-master/.github/workflows/e2e-test.yml | name: E2E Tests
on:
pull_request:
branches: [master, release*]
paths:
- '**'
- '!.github/**'
- '!docs/**'
- '!**.md'
- '.github/workflows/e2e-test.yml'
workflow_dispatch:
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
jobs:
kserve-image-build:
runs-on: ubuntu-latest... | 20,379 | 30.645963 | 155 | yml |
kserve | kserve-master/.github/workflows/go.yml | name: Go
on:
push:
branches: [master, release*]
pull_request:
branches: []
workflow_dispatch:
jobs:
build:
name: Build
runs-on: ubuntu-latest
steps:
- name: Set up Go 1.x
uses: actions/setup-go@v2
with:
go-version: '1.20'
id: go
- name: Check out co... | 1,407 | 22.864407 | 103 | yml |
kserve | kserve-master/.github/workflows/helm-publish.yml | name: helm-publish
on:
release:
types: [prereleased, released]
env:
HELM_CHARTS_PATH: charts
jobs:
upload-helm-charts:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- uses: azure/setup-helm@v1
- name: Package helm charts
run: for dir in ./*/; do helm package... | 678 | 21.633333 | 82 | yml |
kserve | kserve-master/.github/workflows/kserve-controller-docker-publish.yml | name: Kserve controller Docker Publisher
on:
push:
# Publish `master` as Docker `latest` image.
branches:
- master
# Publish `v1.2.3` tags as releases.
tags:
- v*
# Run tests for any PRs.
pull_request:
env:
IMAGE_NAME: kserve-controller
jobs:
# Run tests.
# See also https://... | 2,320 | 25.375 | 100 | yml |
kserve | kserve-master/.github/workflows/lightgbm-docker-publish.yml | name: LightGBMServer Docker Publisher
on:
push:
# Publish `master` as Docker `latest` image.
branches:
- master
# Publish `v1.2.3` tags as releases.
tags:
- v*
# Run tests for any PRs.
pull_request:
env:
IMAGE_NAME: lgbserver
jobs:
# Run tests.
# See also https://docs.docke... | 2,017 | 24.544304 | 100 | yml |
kserve | kserve-master/.github/workflows/paddle-docker-publish.yml | name: PaddleServer Docker Publisher
on:
push:
# Publish `master` as Docker `latest` image.
branches:
- master
# Publish `v1.2.3` tags as releases.
tags:
- v*
# Run tests for any PRs.
pull_request:
env:
IMAGE_NAME: paddleserver
jobs:
# Run tests.
# See also https://docs.docke... | 2,023 | 24.620253 | 100 | yml |