matheushent

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('mnist/', one_hot=True)

X_train = mnist.train.images
y_train = mnist.train.labels

X_test = mnist.test.images
y_test = mnist.test.labels

matheushent

import pandas as pd
import numpy as np

base = pd.read_csv('census.csv')

# Transforming 'income' into 0 or 1

income = pd.get_dummies(base['income'], drop_first=True)

base.drop('income', axis=1, inplace=True)

matheushent

from tensorflow.examples.tutorials.mnist import input_data
import numpy as np

mnist = input_data.read_data_sets('mnist/', one_hot=False)

X_train = mnist.train.images
y_train = mnist.train.labels

X_test = mnist.test.images
y_test = mnist.test.labels

matheushent

from sklearn import datasets

iris = datasets.load_iris()

X = iris.data
y = iris.target

from sklearn.preprocessing import StandardScaler

scaler_x = StandardScaler()

matheushent

import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np

from tensorflow.python.keras import backend as K
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import InputLayer, Input
from tensorflow.python.keras.layers import Reshape, MaxPooling2D
from tensorflow.python.keras.layers import Conv2D, Dense, Flatten, Dropout

matheushent

from warnings import filterwarnings
filterwarnings('ignore')
import pandas as pd
import numpy as np

from lightgbm import LGBMClassifier

from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score

matheushent

import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np

tf.reset_default_graph()

mnist = input_data.read_data_sets('mnist/', one_hot=True)

# Test

matheushent

from tensorflow.examples.tutorials.mnist import input_data
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np

mnist = input_data.read_data_sets('mnist/', one_hot=True)

X = mnist.train.images

# 784 -> 128 -> 64 -> 128 -> 784

matheushent

import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.ops import gen_nn_ops

@ops.RegisterGradient("GuidedRelu")
def _GuidedReluGrad(op, grad):
    return tf.select(0. < grad, gen_nn_ops._relu_grad(grad, op.outputs[0]), tf.zeros(grad.get_shape()))

if __name__ == '__main__':
    with tf.Session() as sess:

matheushent

import tensorflow_addons as tfa
import tensorflow as tf

def get_norm_layer(norm):
    """Utility function to get the normalization layer
    """

    if norm == None:
        return lambda: lambda x: x
    elif norm == 'batch_norm':