koshian2

import torch
from torch import nn
import torch.nn.functional as F

class Generator(nn.Module):
    def __init__(self, n_classes):
        super().__init__()
        self.linear = nn.Sequential(
            nn.Linear(100 + n_classes, 768),            
            nn.ReLU(True)

koshian2

import torch
from torch import nn

class Generator(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Sequential(
            nn.Linear(110, 384),
            nn.ReLU(True)
        )

koshian2

import torch
from torch import nn
import torch.nn.functional as F
import torchvision
from torchvision import transforms
from tqdm import tqdm
import statistics
import os
import pickle
import glob

koshian2

import torch
import torchvision
from torchvision import transforms
from PIL import Image, ImageFilter
import numpy as np
from tqdm import tqdm
import glob
import os

def preprocess(n_validation=2048):

koshian2

import torch
from torch import nn
import torchvision
import torchvision.transforms as transforms
import numpy as np
from tqdm import tqdm
import os
import pickle
import statistics

koshian2

import torch
from torch import nn
import torchvision
import torchvision.transforms as transforms
import numpy as np
from tqdm import tqdm
import os
import pickle
import statistics

koshian2

__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            (None, 32, 32, 3)    0                                            
__________________________________________________________________________________________________
conv2d_2 (Conv2D)               (None, 32, 32, 16)   64          input_1[0][0]                    
__________________________________________________________________________________________________
batch_normalization_v1_2 (Batch (None, 32, 32, 16)   64          conv2d_2[0][0]                   
__________________________________________________________________________________________________
activation_2 (Activation)       (None, 32, 32, 16)   0           batch_normalization_v1_2[0][0]   

koshian2

from pelee_net import PeleeNet
import tensorflow as tf
import tensorflow.keras as keras
import numpy as np
from PIL import Image
import pickle
import os
from tensorflow.contrib.tpu.python.tpu import keras_support

def generator(X, y, batch_size, use_augmentation, shuffle, scale):

koshian2

import torch
import torch.nn as nn
from torch.autograd import Variable
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors

# https://github.com/NVIDIA/apex/blob/master/apex/fp16_utils/fp16util.py

class tofp16(nn.Module):
    """
    Utility module that implements::

koshian2

import torch
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from pytorch_models import Layer10CNN, WideResNet
from apex import amp
import numpy as np
import datetime
import time
import pickle