cleverhans | adversarial example library for constructing attacks | Machine Learning library

cifar_keras.py:
  --batch_size: Size of training batches
    (default: '128')
    (an integer)
  --filename: Checkpoint filename.
    (default: 'FF_init_model.ckpt')
  --learning_rate: Learning rate for training
    (default: '0.001')
    (a number)
  

cifar_keras.py:
  --batch_size: Size of training batches
    (default: '128')
    (an integer)
  --filename: Checkpoint filename.
    (default: 'FF_init_model.ckpt')
  --learning_rate: Learning rate for training
    (default: '0.001')
    (a number)
  

# PyTorch: find detailed instructions on [http://pytorch.org/](http://pytorch.org/)
pip install torch
pip install torchvision

# TF: find detailed instructions on [http://tensorflow.org/](http://tensorflow.org)
pip install keras
pip install tensorflo  

from absl import app, flags
from easydict import EasyDict
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision

from cleverhans.torch.attacks.fast_gradient_method import fast_gradient_method
from cl  

import math
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
from absl import app, flags
from easydict import EasyDict
from tensorflow.keras import Model
from tensorflow.keras.layers import AveragePooling2D, Conv2D

from   

import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
from absl import app, flags
from easydict import EasyDict
from tensorflow.keras import Model
from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D

from cleve  

!wget -O mini.sh https://repo.anaconda.com/miniconda/Miniconda3-4.6.14-Linux-x86_64.sh 
!bash ./mini.sh -b -f -p /usr/local
!conda install -q -y --prefix /usr/local -c conda-forge mdanalysis
import sys
sys.path.append('/usr/local/lib/pytho