repo_name stringclasses 400
values | branch_name stringclasses 4
values | file_content stringlengths 16 72.5k | language stringclasses 1
value | num_lines int64 1 1.66k | avg_line_length float64 6 85 | max_line_length int64 9 949 | path stringlengths 5 103 | alphanum_fraction float64 0.29 0.89 | alpha_fraction float64 0.27 0.89 | context stringlengths 0 91.6k | context_file_paths listlengths 0 3 |
|---|---|---|---|---|---|---|---|---|---|---|---|
jesbarlow/CP1404_practicals | refs/heads/master | numbers = [3, 1, 4, 1, 5, 9, 2]
#numbers[0] - the value would be 3
#numbers[-1] -
#numbers[3] - the value would be 1
#numbers[:-1] -
#numbers[3:4] -
#5 in numbers - the value would be true
#7 in numbers - the value would be false
#"3" in numbers - the value would be false
#numbers + [6, 5, 3] - will print the list add... | Python | 34 | 20.558823 | 77 | /prac_4/warm_up.py | 0.648907 | 0.590164 | sentence = input("Enter a sentence:")
words = sentence.split()
counting = {}
for word in words:
if word in counting:
counting[word] += 1
else:
counting[word] = 1
print("Text: {}".format(sentence))
for key, value in counting.items():
print("{} : {}".format(key,value))
--- FILE SEPARATOR -... | [
"/prac_5/word_count.py",
"/Prac_3/print_second_letter_name.py",
"/prac_5/colour_codes.py"
] |
jesbarlow/CP1404_practicals | refs/heads/master | items = int(input("Please enter the number of items:"))
if items <= 0:
print("Invalid number of items")
items = input("Please enter the number of items:")
prices = []
count = 0
for i in range(items):
count = count + 1
item_cost = float(input("What is the price of item {}?: $".format(count)))
price... | Python | 21 | 24.190475 | 78 | /Prac_1/shop_calculator.py | 0.606285 | 0.595194 | """
CP1404/CP5632 - Practical
Answer the following questions:
1. When will a ValueError occur?
- value errors occur when the input os anything other than a number(including negative numbers),for example - the
letter a
2. When will a ZeroDivisionError occur?
- this will occur whenever the user input... | [
"/prac_2/exceptions.py",
"/prac_4/quickpick_lottery_generator.py",
"/prac_4/warm_up.py"
] |
jesbarlow/CP1404_practicals | refs/heads/master | def main():
name = get_name()
print_name(name)
def print_name(name):
print(name[::2])
def get_name():
while True:
name = input("What is your name?: ")
if name.isalpha():
break
else:
print("Sorry, i didn't understand that.")
return name
main() | Python | 22 | 13.5 | 53 | /Prac_3/print_second_letter_name.py | 0.518868 | 0.515723 | numbers = [3, 1, 4, 1, 5, 9, 2]
#numbers[0] - the value would be 3
#numbers[-1] -
#numbers[3] - the value would be 1
#numbers[:-1] -
#numbers[3:4] -
#5 in numbers - the value would be true
#7 in numbers - the value would be false
#"3" in numbers - the value would be false
#numbers + [6, 5, 3] - will print the list add... | [
"/prac_4/warm_up.py",
"/prac_2/files.py",
"/Prac_1/shop_calculator.py"
] |
rlebras/pytorch-pretrained-BERT | refs/heads/master | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | Python | 1,193 | 39.125732 | 117 | /examples/run_classifier.py | 0.552663 | 0.547253 | from examples.run_classifier import AnliWithCSKProcessor, convert_examples_to_features_mc
from pytorch_pretrained_bert import BertTokenizer
dir = "../../abductive-nli/data/abductive_nli/one2one-correspondence/anli_with_csk/"
processor = AnliWithCSKProcessor()
examples = processor.get_train_examples(dir)
tokenizer =... | [
"/examples/test_data_processor.py",
"/pytorch_pretrained_bert/file_utils.py"
] |
rlebras/pytorch-pretrained-BERT | refs/heads/master | """
Utilities for working with the local dataset cache.
This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
Copyright by the AllenNLP authors.
"""
import gzip
import csv
import os
import logging
import shutil
import tempfile
import json
from urllib.parse import urlparse
from pathlib im... | Python | 324 | 33.370369 | 100 | /pytorch_pretrained_bert/file_utils.py | 0.602245 | 0.596408 | from examples.run_classifier import AnliWithCSKProcessor, convert_examples_to_features_mc
from pytorch_pretrained_bert import BertTokenizer
dir = "../../abductive-nli/data/abductive_nli/one2one-correspondence/anli_with_csk/"
processor = AnliWithCSKProcessor()
examples = processor.get_train_examples(dir)
tokenizer =... | [
"/examples/test_data_processor.py",
"/examples/run_classifier.py"
] |
rlebras/pytorch-pretrained-BERT | refs/heads/master | from examples.run_classifier import AnliWithCSKProcessor, convert_examples_to_features_mc
from pytorch_pretrained_bert import BertTokenizer
dir = "../../abductive-nli/data/abductive_nli/one2one-correspondence/anli_with_csk/"
processor = AnliWithCSKProcessor()
examples = processor.get_train_examples(dir)
tokenizer =... | Python | 17 | 30.529411 | 91 | /examples/test_data_processor.py | 0.790654 | 0.783178 | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | [
"/examples/run_classifier.py",
"/pytorch_pretrained_bert/file_utils.py"
] |
MistyW/learngit | refs/heads/master | # _*_ coding: utf-8 _*_
# __author__ = wmm
class Settings():
"""存储《外星人入侵》的所有类"""
def __init__(self):
"""初始化游戏的设置"""
# 屏幕设置
self.screen_width = 1200
self.screen_height = 800
self.bg_color = (230, 230, 230) | Python | 11 | 22.09091 | 39 | /alien_invasion_game/Settings.py | 0.490119 | 0.422925 | [] | |
AllenMkandla/oop_person | refs/heads/master | class Person:
pass
def __init__(self, name, age, gender, interests):
self.name = name
self.age = age
self.gender = gender
self.interests = interests
def hello(self):
interests_str = 'My interests are '
for pos in range(len(self.interests)):
... | Python | 21 | 32.142857 | 106 | /oop.py | 0.546763 | 0.542446 | [] | |
tartaruz/Stein-saks-papir | refs/heads/master | import funk
from time import sleep
import os
clear = lambda: os.system('cls')
valg = 0
while (valg!="avslutt"):
sleep(1)
print()
funk.velkommen()
funk.meny()
print()
valg = funk.valg()
clear()
if valg=="1":
print("--------------Spiller 1's tur--------------")
pvalg=funk.... | Python | 55 | 23 | 60 | /stein-saks-papir.py | 0.468986 | 0.451589 | import random
from time import sleep#for stein saks papir
def velkommen():
print("§-----------------------------------------------------------§")
print("§-----| VELKOMMEN TIL STEIN/SAKS/PAPIR! |-----§")
print("§-----------------------------------------------------------§")
print()
def va... | [
"/funk.py"
] |
tartaruz/Stein-saks-papir | refs/heads/master | import random
from time import sleep#for stein saks papir
def velkommen():
print("§-----------------------------------------------------------§")
print("§-----| VELKOMMEN TIL STEIN/SAKS/PAPIR! |-----§")
print("§-----------------------------------------------------------§")
print()
def va... | Python | 112 | 23.258928 | 74 | /funk.py | 0.361499 | 0.348273 | import funk
from time import sleep
import os
clear = lambda: os.system('cls')
valg = 0
while (valg!="avslutt"):
sleep(1)
print()
funk.velkommen()
funk.meny()
print()
valg = funk.valg()
clear()
if valg=="1":
print("--------------Spiller 1's tur--------------")
pvalg=funk.... | [
"/stein-saks-papir.py"
] |
guanjz20/MM21_FME_solution | refs/heads/master | SAMM_ROOT = '/data/gjz_mm21/SAMM'
CASME_2_LABEL_DIR = '/data/gjz_mm21/CASME_2_LongVideoFaceCropped/CASME_2_longVideoFaceCropped/labels'
# kernel path
GAUSS_KERNEL_PATH = {
'sm_kernel': '/home/gjz/lry_kernels/gauss2D-smooth.npy',
'dr1_kernel': '/home/gjz/lry_kernels/gauss1D-derivative1.npy',
'dr2_kernel': '... | Python | 9 | 40 | 101 | /dataset/params.py | 0.722826 | 0.684783 | '''
generate the emotion intensity of each frame
'''
# %%
import os
import pdb
import os.path as osp
from numpy.core.numeric import ones
from numpy.lib.function_base import percentile
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import params
# %% main
anno_dict = {} # intensity
label_dict... | [
"/preprocess/samm_2_label_generation.py",
"/trainer_cls.py",
"/model/network.py"
] |
guanjz20/MM21_FME_solution | refs/heads/master | import os
import sys
import cv2
from timm.utils import reduce_tensor
import torch
import shutil
import numpy as np
import os.path as osp
import torch.nn.functional as F
import matplotlib.pyplot as plt
import torch.distributed as dist
from torch.nn.modules import loss
from datetime import datetime
import paths
import d... | Python | 600 | 32.474998 | 98 | /utils.py | 0.506921 | 0.491735 | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import glob
import os
import os.path as osp
from torch.serialization import load
class MLP(nn.Module):
def __init__(self, hidden_units, dropout=0.3):
super(MLP, self).__init__()
input_feature_dim = hidden_units[... | [
"/model/network.py",
"/preprocess/samm_2_label_generation.py",
"/dataset/me_dataset.py"
] |
guanjz20/MM21_FME_solution | refs/heads/master | # SAMM
SAMM_ROOT = '/data/gjz_mm21/SAMM'
SAMM_LABEL_DIR = SAMM_ROOT
SAMM_VIDEO_DIR = '/data/gjz_mm21/SAMM/SAMM_longvideos'
# CASME_2
CASME_2_ROOT = '/data/gjz_mm21/CASME_2_LongVideoFaceCropped/CASME_2_longVideoFaceCropped'
CASME_2_LABEL_DIR = '/data/gjz_mm21/CASME_2_LongVideoFaceCropped/CASME_2_longVideoFaceCropped/la... | Python | 9 | 48.111111 | 115 | /paths.py | 0.786848 | 0.741497 | import argparse
parser = argparse.ArgumentParser(description="x")
parser.add_argument('--store_name', type=str, default="")
parser.add_argument('--save_root', type=str, default="")
parser.add_argument('--tag', type=str, default="")
parser.add_argument('--snap', type=str, default="")
parser.add_argument('--dataset',
... | [
"/config.py",
"/model/network.py",
"/preprocess/params.py"
] |
guanjz20/MM21_FME_solution | refs/heads/master | from unicodedata import name
import cv2
import os
import pdb
import torch
import time
import pywt
import glob
import numpy as np
import os.path as osp
from tqdm import tqdm
from torch.utils.data import Dataset
from torch import nn as nn
from . import params
from . import utils
WT_CHANNEL = 4
sm_kernel = np.load(param... | Python | 284 | 36.070423 | 81 | /dataset/me_dataset.py | 0.510163 | 0.495916 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
from genericpath import exists
import os
from typing import Final
import cv2
import sys
from matplotlib.pyplot import xcorr
from numpy.random import f, sample, shuffle
from torch.utils.data import dataset
from config import parser
if len(sys.argv) > 1:
# use shell a... | [
"/main_cls.py",
"/preprocess/openface/face_crop_align.py",
"/submit.py"
] |
guanjz20/MM21_FME_solution | refs/heads/master | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import glob
import os
import os.path as osp
from torch.serialization import load
class MLP(nn.Module):
def __init__(self, hidden_units, dropout=0.3):
super(MLP, self).__init__()
input_feature_dim = hidden_units[... | Python | 264 | 35.21212 | 79 | /model/network.py | 0.494351 | 0.472803 | '''
generate the emotion intensity of each frame
'''
# %%
import pdb
import os
import os.path as osp
from numpy.core.numeric import ones, ones_like
from numpy.lib.function_base import percentile
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import params
# %% ID2NAME and NAME2ID
# CASME_2_P... | [
"/preprocess/casme_2_label_generation.py",
"/dataset/me_dataset.py",
"/dataset/utils.py"
] |
guanjz20/MM21_FME_solution | refs/heads/master | import argparse
parser = argparse.ArgumentParser(description="x")
parser.add_argument('--store_name', type=str, default="")
parser.add_argument('--save_root', type=str, default="")
parser.add_argument('--tag', type=str, default="")
parser.add_argument('--snap', type=str, default="")
parser.add_argument('--dataset',
... | Python | 132 | 39.946968 | 77 | /config.py | 0.477336 | 0.46605 | import pandas as pd
import numpy as np
import os.path as osp
dataset = 'CASME_2'
# dataset = 'SAMM'
submit_name = 'submit_{}.csv'.format(dataset)
result_dir_name = 'results'
submit_npy_name = 'match_regions_record_all.npy'
submit_id = 'done_exp_cls_ca_20210708-215035'
def convert_key(k, dataset):
if dataset == '... | [
"/submit.py",
"/utils.py",
"/main_cls.py"
] |
guanjz20/MM21_FME_solution | refs/heads/master | # CASME_2
CASME_2_ROOT = '/data/gjz_mm21/CASME_2_LongVideoFaceCropped/CASME_2_longVideoFaceCropped'
CASME_2_LABEL_DIR = '/data/gjz_mm21/CASME_2_LongVideoFaceCropped/CASME_2_longVideoFaceCropped/labels'
CASME_2_VIDEO_DIR = '/data/gjz_mm21/CASME_2_LongVideoFaceCropped/CASME_2_longVideoFaceCropped/longVideoFaceCropped'
#... | Python | 11 | 44.454544 | 115 | /preprocess/params.py | 0.786 | 0.742 | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import glob
import os
import os.path as osp
from torch.serialization import load
class MLP(nn.Module):
def __init__(self, hidden_units, dropout=0.3):
super(MLP, self).__init__()
input_feature_dim = hidden_units[... | [
"/model/network.py",
"/preprocess/CNN_feature_extraction.py",
"/dataset/params.py"
] |
guanjz20/MM21_FME_solution | refs/heads/master | import pandas as pd
import numpy as np
import os.path as osp
dataset = 'CASME_2'
# dataset = 'SAMM'
submit_name = 'submit_{}.csv'.format(dataset)
result_dir_name = 'results'
submit_npy_name = 'match_regions_record_all.npy'
submit_id = 'done_exp_cls_ca_20210708-215035'
def convert_key(k, dataset):
if dataset == '... | Python | 47 | 29.702127 | 75 | /submit.py | 0.544006 | 0.523216 | import time
from matplotlib.pyplot import winter
import torch
import torch.nn.functional as F
import numpy as np
import utils
import dataset.utils as dataset_utils
import dataset.params as DATASET_PARAMS
def train(dataloader, model, criterion, optimizer, epoch, logger, args,
amp_autocast, loss_scaler):
... | [
"/trainer_cls.py",
"/preprocess/samm_2_label_generation.py",
"/dataset/utils.py"
] |
guanjz20/MM21_FME_solution | refs/heads/master | import time
from matplotlib.pyplot import winter
import torch
import torch.nn.functional as F
import numpy as np
import utils
import dataset.utils as dataset_utils
import dataset.params as DATASET_PARAMS
def train(dataloader, model, criterion, optimizer, epoch, logger, args,
amp_autocast, loss_scaler):
... | Python | 173 | 36.410404 | 78 | /trainer_cls.py | 0.4983 | 0.489648 | # CASME_2
CASME_2_ROOT = '/data/gjz_mm21/CASME_2_LongVideoFaceCropped/CASME_2_longVideoFaceCropped'
CASME_2_LABEL_DIR = '/data/gjz_mm21/CASME_2_LongVideoFaceCropped/CASME_2_longVideoFaceCropped/labels'
CASME_2_VIDEO_DIR = '/data/gjz_mm21/CASME_2_LongVideoFaceCropped/CASME_2_longVideoFaceCropped/longVideoFaceCropped'
#... | [
"/preprocess/params.py",
"/dataset/params.py",
"/utils.py"
] |
guanjz20/MM21_FME_solution | refs/heads/master | '''
generate the emotion intensity of each frame
'''
# %%
import pdb
import os
import os.path as osp
from numpy.core.numeric import ones, ones_like
from numpy.lib.function_base import percentile
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import params
# %% ID2NAME and NAME2ID
# CASME_2_P... | Python | 150 | 32.060001 | 78 | /preprocess/casme_2_label_generation.py | 0.597096 | 0.571486 | import os
import os.path as osp
from tqdm import tqdm
from glob import glob
from video_processor import Video_Processor
import params
# OpenFace parameters
save_size = 224
OpenFace_exe = params.OpenFace_exe
quiet = True
nomask = True
grey = False
tracked_vid = False
noface_save = False
# dataset
video_root = params.... | [
"/preprocess/openface/face_crop_align.py",
"/preprocess/samm_2_label_generation.py",
"/utils.py"
] |
guanjz20/MM21_FME_solution | refs/heads/master | '''
generate the emotion intensity of each frame
'''
# %%
import os
import pdb
import os.path as osp
from numpy.core.numeric import ones
from numpy.lib.function_base import percentile
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import params
# %% main
anno_dict = {} # intensity
label_dict... | Python | 116 | 30.413794 | 77 | /preprocess/samm_2_label_generation.py | 0.580955 | 0.561197 | import os
import sys
import cv2
from timm.utils import reduce_tensor
import torch
import shutil
import numpy as np
import os.path as osp
import torch.nn.functional as F
import matplotlib.pyplot as plt
import torch.distributed as dist
from torch.nn.modules import loss
from datetime import datetime
import paths
import d... | [
"/utils.py",
"/preprocess/CNN_feature_extraction.py",
"/model/utils.py"
] |
guanjz20/MM21_FME_solution | refs/heads/master | from __future__ import division
from typing import Iterable
import cv2
import os
import time
import six
import sys
from tqdm import tqdm
import argparse
import pickle
import torch
import torch.nn as nn
import numpy as np
import pandas as pd
import torch.utils.data
import os.path as osp
import torch.backends.cudnn as cu... | Python | 341 | 32.498535 | 80 | /preprocess/CNN_feature_extraction.py | 0.591701 | 0.578044 | import time
from matplotlib.pyplot import winter
import torch
import torch.nn.functional as F
import numpy as np
import utils
import dataset.utils as dataset_utils
import dataset.params as DATASET_PARAMS
def train(dataloader, model, criterion, optimizer, epoch, logger, args,
amp_autocast, loss_scaler):
... | [
"/trainer_cls.py",
"/preprocess/casme_2_label_generation.py",
"/model/utils.py"
] |
guanjz20/MM21_FME_solution | refs/heads/master | import os
import os.path as osp
from tqdm import tqdm
from glob import glob
from video_processor import Video_Processor
import params
# OpenFace parameters
save_size = 224
OpenFace_exe = params.OpenFace_exe
quiet = True
nomask = True
grey = False
tracked_vid = False
noface_save = False
# dataset
video_root = params.... | Python | 31 | 26.806452 | 78 | /preprocess/openface/face_crop_align.py | 0.667053 | 0.661253 | from __future__ import division
from typing import Iterable
import cv2
import os
import time
import six
import sys
from tqdm import tqdm
import argparse
import pickle
import torch
import torch.nn as nn
import numpy as np
import pandas as pd
import torch.utils.data
import os.path as osp
import torch.backends.cudnn as cu... | [
"/preprocess/CNN_feature_extraction.py",
"/submit.py",
"/dataset/utils.py"
] |
guanjz20/MM21_FME_solution | refs/heads/master | import torch.nn as nn
def init_weights(model):
for k, m in model.named_modules():
if isinstance(m, nn.Conv3d) or isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight,
mode='fan_out',
nonlinearity='relu')
#... | Python | 18 | 41.166668 | 76 | /model/utils.py | 0.520422 | 0.509881 | import os
import sys
import cv2
from timm.utils import reduce_tensor
import torch
import shutil
import numpy as np
import os.path as osp
import torch.nn.functional as F
import matplotlib.pyplot as plt
import torch.distributed as dist
from torch.nn.modules import loss
from datetime import datetime
import paths
import d... | [
"/utils.py",
"/dataset/params.py",
"/trainer_cls.py"
] |
guanjz20/MM21_FME_solution | refs/heads/master | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
from genericpath import exists
import os
from typing import Final
import cv2
import sys
from matplotlib.pyplot import xcorr
from numpy.random import f, sample, shuffle
from torch.utils.data import dataset
from config import parser
if len(sys.argv) > 1:
# use shell a... | Python | 437 | 36.016018 | 107 | /main_cls.py | 0.542718 | 0.53499 | from __future__ import division
from typing import Iterable
import cv2
import os
import time
import six
import sys
from tqdm import tqdm
import argparse
import pickle
import torch
import torch.nn as nn
import numpy as np
import pandas as pd
import torch.utils.data
import os.path as osp
import torch.backends.cudnn as cu... | [
"/preprocess/CNN_feature_extraction.py",
"/paths.py",
"/preprocess/openface/face_crop_align.py"
] |
guanjz20/MM21_FME_solution | refs/heads/master | from albumentations.augmentations.transforms import GaussNoise
import cv2
import os
import numpy as np
import os.path as osp
import albumentations as alb
# from torch._C import Ident
# from torch.nn.modules.linear import Identity
class IsotropicResize(alb.DualTransform):
def __init__(self,
max_si... | Python | 195 | 30.897436 | 77 | /dataset/utils.py | 0.521145 | 0.509085 | SAMM_ROOT = '/data/gjz_mm21/SAMM'
CASME_2_LABEL_DIR = '/data/gjz_mm21/CASME_2_LongVideoFaceCropped/CASME_2_longVideoFaceCropped/labels'
# kernel path
GAUSS_KERNEL_PATH = {
'sm_kernel': '/home/gjz/lry_kernels/gauss2D-smooth.npy',
'dr1_kernel': '/home/gjz/lry_kernels/gauss1D-derivative1.npy',
'dr2_kernel': '... | [
"/dataset/params.py",
"/config.py",
"/paths.py"
] |
gowtham59/fgh | refs/heads/master | f12,f22=input().split()
f22=int(f22)
for y in range(f22):
print(f12)
| Python | 4 | 16.75 | 23 | /g.py | 0.661972 | 0.492958 | [] | |
wendeehsu/MangoClassification | refs/heads/master | """# Load libraries"""
import os, shutil
import matplotlib.pyplot as plt
import numpy as np
import random
import pandas as pd
from keras.applications.inception_v3 import InceptionV3
from keras.layers import Activation, Dense, GlobalAveragePooling2D, Dropout
from keras.layers.core import Flatten
from keras.models impor... | Python | 94 | 29.223404 | 97 | /train_incept.py | 0.679099 | 0.662913 | import os, shutil
import random
import matplotlib.pyplot as plt
import numpy as np
from tensorflow.python.keras.models import load_model
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import classification_report, confusio... | [
"/test.py",
"/train.py"
] |
wendeehsu/MangoClassification | refs/heads/master | import os, shutil
import random
import matplotlib.pyplot as plt
import numpy as np
from tensorflow.python.keras.models import load_model
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import classification_report, confusio... | Python | 54 | 30.037037 | 87 | /test.py | 0.741647 | 0.729117 | """# Load libraries"""
import os, shutil
import matplotlib.pyplot as plt
import numpy as np
import random
import pandas as pd
from keras.applications.inception_v3 import InceptionV3
from keras.layers import Activation, Dense, GlobalAveragePooling2D, Dropout
from keras.layers.core import Flatten
from keras.models impor... | [
"/train_incept.py",
"/train.py"
] |
wendeehsu/MangoClassification | refs/heads/master | """# Load libraries"""
import os, shutil
import matplotlib.pyplot as plt
import numpy as np
import random
import pandas as pd
from keras.applications.resnet import ResNet152
from keras.layers.core import Dense, Flatten
from keras.layers import Activation,Dropout
from keras.models import Model
from keras.optimizers imp... | Python | 99 | 26.464647 | 97 | /train.py | 0.651838 | 0.632353 | import os, shutil
import random
import matplotlib.pyplot as plt
import numpy as np
from tensorflow.python.keras.models import load_model
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import classification_report, confusio... | [
"/test.py",
"/train_incept.py"
] |
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