text_plain_output_1.png
from tqdm import tqdm
import os
import time
from random import randint
from keras.callbacks import CSVLogger
import numpy as np
from scipy import stats
import pandas as pd
from keras.utils import np_utils
from keras.callbacks import ModelCheckpoint
from sklearn.model_selection import train_test_split
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVR
from sklearn.model_selection import KFold
import nibabel as nib
import pydicom as pdm
import nilearn as nl
import nilearn.plotting as nlplt
import h5py
import matplotlib.pyplot as plt
from matplotlib import cm
import matplotlib.animation as anim
import matplotlib.patches as mpatches
import matplotlib.gridspec as gridspec
import seaborn as sns
import imageio
from skimage.transform import resize
from skimage.util import montage
from IPython.display import Image as show_gif
from IPython.display import clear_output
from IPython.display import YouTubeVideo
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
import torch.nn.functional as F
from torch.optim import Adam
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.nn import MSELoss
import albumentations as A
from albumentations import Compose, HorizontalFlip
import SimpleITK as sitk
import sys
import os
import warnings
warnings.simplefilter('ignore')
import numpy as np
import pandas as pd
import os
import pydicom
import matplotlib.pyplot as plt
from pydicom.pixel_data_handlers.util import apply_voi_lut
import glob
import cv2
import torch
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torch import optim
import keras
import warnings
import os
import zipfile
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
warnings.filterwarnings('ignore')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
np.seterr(divide='ignore', invalid='ignore')
from keras.layers import Conv3D, MaxPool3D, Flatten, Dense
from keras.layers import Dropout, Input, BatchNormalization
from sklearn.metrics import confusion_matrix, accuracy_score
from plotly.offline import iplot, init_notebook_mode
from keras.losses import categorical_crossentropy
from keras.optimizers import Adadelta
import plotly.graph_objs as go
from matplotlib.pyplot import cm
from keras.models import Model
import numpy as np
import keras
import h5py
import SimpleITK as sitk
init_notebook_mode(connected=True)