arnaldog12

train_x:
  type: pandas.CSVDataSet
  filepath: data/01_raw/x_train.csv

test_x:
  type: pandas.CSVDataSet
  filepath: data/01_raw/x_test.csv

autoencoder_scaler:
  type: pickle.PickleDataSet

arnaldog12

#pragma once

#ifndef TENSORFLOW_GRAPH_H
#define TENSORFLOW_GRAPH_H

#include "TensorflowUtils.h"
#include "TensorflowPlaceholder.h"

using namespace tensorflow;
using deeplearning::TensorflowUtils;

arnaldog12

# Windows (Cmder)
;= @echo off
;= rem Call DOSKEY and use this file as the macrofile
;= %SystemRoot%\system32\doskey /listsize=1000 /macrofile=%0%
;= rem In batch mode, jump to the end of the file
;= goto:eof
;= Add aliases below here
e.=explorer .
gl=git log --oneline --all --graph --decorate  $*
ls=ls --show-control-chars -F --color $*

arnaldog12

import numpy as np
import tensorflow as tf
from keras import backend as K


def recall(y_true, y_pred):
    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
    recall_keras = true_positives / (possible_positives + K.epsilon())
    return recall_keras

arnaldog12

from keras.utils.data_utils import Sequence
from imblearn.over_sampling import RandomOverSampler
from imblearn.keras import balanced_batch_generator

class BalancedDataGenerator(Sequence):
    """ImageDataGenerator + RandomOversampling"""
    def __init__(self, x, y, datagen, batch_size=32):
        self.datagen = datagen
        self.batch_size = min(batch_size, x.shape[0])
        datagen.fit(x)

arnaldog12

```
extracted from: https://stats.stackexchange.com/questions/134282/relationship-between-svd-and-pca-how-to-use-svd-to-perform-pca
```

import numpy as np
from numpy import linalg as la
np.random.seed(42)


def flip_signs(A, B):

arnaldog12

import numpy as np
import tensorflow as tf
from os import path
from glob import glob
from tensorflow.python.platform import gfile

def load_graph(model_file):
    graph = tf.Graph()

    graph_def = tf.GraphDef()

arnaldog12

model = Sequential()
model.add(Dense(units=20, activation='relu', kernel_regularizer='l2', input_dim=x.shape[1]))
model.add(Dense(units=10, activation='relu', kernel_regularizer='l2'))
model.add(Dense(units=3, activation='linear'))

arnaldog12

echo "#################### create environment ####################"
conda create -n bootcamp python=3.6.5 numpy=1.14.3 pandas=0.23.0 matplotlib=2.2.2 scikit-learn=0.19.1 jupyter=1.0.0
source activate bootcamp
jupyter nbextension enable --py widgetsnbextension --sys-prefix
jupyter notebook

arnaldog12

import numpy as np
import _pickle as pkl

# activation functions
def linear(x, derivative=False):
    return np.ones_like(x) if derivative else x

def sigmoid(x, derivative=False):
    if derivative:
        y = sigmoid(x)