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import numpy as np
from numpy.random import choice, normal
np.random.seed(8)
from tensorflow import set_random_seed
set_random_seed(8)

from tensorflow.python.keras.layers import Input, BatchNormalization,Conv2D, MaxPooling2D
from tensorflow.python.keras.layers import Dropout, Reshape
from tensorflow.python.keras.layers import Dense, Flatten
from tensorflow.python.keras.models import Model

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import numpy as np
#function for getting coordinates given an angle
def get_cartesian_coords(nums):
    theta = nums
    x = np.cos(theta)
    y = np.sin(theta)
    return [x,y]
#generate random starting coordinate
angle = np.random.uniform(-np.pi, np.pi)
coordinates = get_cartesian_coords(random_angle)

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import keras
from keras.datasets import mnist
from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Flatten ,Input
from keras.layers import Conv2D, MaxPooling2D, Reshape, Add
from keras.metrics import categorical_accuracy
from keras.regularizers import l1_l2, l2, l1
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Activation
from keras.utils.generic_utils import get_custom_objects

emuccino

import keras
from keras.datasets import mnist
from keras.models import Model
from keras.layers import Dense, Dropout, Flatten ,Input
from keras.layers import Conv2D, MaxPooling2D, Reshape, Add
from keras.metrics import categorical_accuracy
from keras.regularizers import l1_l2, l2, l1
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Activation
from keras.utils.generic_utils import get_custom_objects

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#select image to create an adversarial example from
img = x_train[0:1]
plt.imshow(img.reshape((28,28)),vmin=0., vmax=1.)
plt.show()
#varify accurate classificaiton
prediction = mnist_model.predict(img)[0]
print(prediction)

#applying random noise does not fool the classifier
quantized_noise = np.round(np.random.normal(loc=0.0, scale=0.3, size=img.shape) * 255.) / 255.

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#custom activation function for keeping adversarial pixel values between 0.0 and 1.0
def clip(x):
    return K.clip(x, 0.0, 1.0)

#custom loss funciton for non-targeted misclassification
def negative_categorical_crossentropy(yTrue,yPred):
    return 0.0 - K.categorical_crossentropy(yTrue,yPred)

#add custom objects to dictionary
get_custom_objects().update({'clip': Activation(clip)})

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from keras.models import Model
from keras.layers import Dense, Input, Reshape
from keras.regularizers import Regularizer
from keras.utils.generic_utils import get_custom_objects
from keras import backend as K
from keras.applications.inception_v3 import InceptionV3
from keras.initializers import RandomUniform
from keras.preprocessing.image import array_to_img, save_img

import numpy as np

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#imports

import tensorflow as tf

from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2,preprocess_input
from tensorflow.keras.layers import Input,GlobalMaxPooling2D,Dense
from tensorflow.keras.models import Model
from tensorflow.keras.preprocessing.image import img_to_array,load_img

import numpy as np

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#set up MobileNet GlobalMaxPooling and unsepcified input resolution

inputs = Input(shape=(None,None,3))
net = MobileNetV2(include_top=False, alpha=0.35, weights='imagenet', input_tensor=inputs, classes=n_classes)
net = GlobalMaxPooling2D()(net.output)
outputs = Dense(n_classes,activation='softmax')(net)

model = Model(inputs=inputs,outputs=outputs)
model.compile(optimizer='Adam', loss='sparse_categorical_crossentropy',metrics=['sparse_categorical_accuracy'])

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#data generator class; yields batches of data for training/testing
class ImageGenerator():

    def __init__(self, directory, batch_size=16, shuffle=False, max_dimension=None):        
        
        self.directories = directory
        self.batch_size = batch_size
        self.shuffle = shuffle
        self.max_dimension = max_dimension