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

emuccino

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 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)

emuccino

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