MinaGabriel

import keras
from keras.datasets import mnist
from keras.layers import Input, Dense
from keras.models import load_model
from skimage.util import invert

import numpy as np
import matplotlib.pyplot as plt

(X_train, y_train), (X_test, y_test) = mnist.load_data()

MinaGabriel

import os

import pandas as pd
import mysql.connector
from sqlalchemy import create_engine

# ====== Connection ====== #
# Connecting to mysql by providing a sqlachemy engine
engine = create_engine('mysql+mysqlconnector://myhu:password@192.168.68.102/dbname', echo=True)

MinaGabriel

from keras.datasets import mnist
from keras.layers import Input, Dense
from keras.models import Model

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
(X_train, _), (X_test, _) = mnist.load_data()
X_train = X_train.astype('float32')/255
X_test = X_test.astype('float32')/255

MinaGabriel

from __future__ import absolute_import, division, print_function, unicode_literals

import tensorflow as tf

import numpy as np

from tensorflow import keras
from tensorflow.keras import layers

inputs = keras.Input(shape=(784,), name='digits')

MinaGabriel

from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras import losses
import numpy as np

X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y = np.array([[0], [1], [1], [0]])

model = Sequential()

MinaGabriel

def binary_data(n):
    b = []
    for i in range(1 << n):
        s = bin(i)[2:]
        s = '0' * (n - len(s)) + s
        b.append(map(int, list(s)))
    return b

MinaGabriel

import sys
import numpy
import os

os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
numpy.set_printoptions(threshold=sys.maxsize)
import torch
from torch.distributions import normal
import seaborn as sns;

MinaGabriel

import tensorflow as tf
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'


mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()

MinaGabriel

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import numpy as np
from scipy.stats import norm

fig = plt.figure()
ax = fig.gca(projection='3d')

MinaGabriel

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import numpy as np
from scipy.stats import norm

fig = plt.figure()
ax = fig.gca(projection='3d')