matheushent

$ kubectl apply -f - <<EOF
<-- insert YAML content here -->
EOF

OR

$ cat file.yaml | kubectl apply -f -

matheushent

#CDK python WAF with CloudFront and regex and ip set rules (v1.102.0 of CDK and above)

from aws_cdk import (
    core as cdk,
    aws_cloudfront as cloudfront,
    aws_cloudfront_origins as cloudfront_origins,
    aws_s3 as s3,
    aws_certificatemanager as acm,
    aws_route53 as route53,
    aws_wafv2 as wafv2,

matheushent

>>> import pytz
>>> 
>>> for tz in pytz.all_timezones:
...     print tz
...     
... 
Africa/Abidjan
Africa/Accra
Africa/Addis_Ababa
Africa/Algiers

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':

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

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
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 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 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 sklearn import datasets

iris = datasets.load_iris()

X = iris.data
y = iris.target

from sklearn.preprocessing import StandardScaler

scaler_x = StandardScaler()