deepanshu-yadav

# Class that handles scaling operation. 
class Scaler:

    def __init__(self, scaler):
        self.scaler = scaler
        # For backward compatibilty with earlier version of scikit learn
        self.scaler.clip = False

    def transform_using_scaler(self, data_x):
        

deepanshu-yadav

import glob
from sklearn.preprocessing import MinMaxScaler

def npy_header_offset(npy_path):
    """Gives the no of header bytes inside a numpy file."""
    with open(str(npy_path), 'rb') as f:
        if f.read(6) != b'\x93NUMPY':
            raise ValueError('Invalid NPY file.')
        version_major, version_minor = f.read(2)
        if version_major == 1:

deepanshu-yadav

# we will utilize our class for creating generator. 
train_gen = CustomGenerator(train_data_descriptor, BATCH_SIZE, min_max_scaler_train)
valid_gen = CustomGenerator(validation_data_descriptor, BATCH_SIZE, min_max_scaler_train)

input_dim = train_gen.getitem(0)[0].shape[1]

# The following two functions actually extract data on demand by using yield.
def gen_data_train():
    j = 0
    while j < NO_OF_EPOCHS:

deepanshu-yadav

# Here is a class that makes it easy to load data in a batch. 
class CustomGenerator():

    def __init__(self, data_desc, batch_size, scaler):
        self.data_desc = data_desc
        self.batch_size = batch_size
        self.scaler = scaler
        self.len = self.__len__()  # an attribute for the length

    def __len__(self):

deepanshu-yadav

import glob
from sklearn.preprocessing import MinMaxScaler

training_files = glob.glob(os.path.join(train_dir, '*'))
validation_files = glob.glob(os.path.join(validation_dir, '*'))
min_max_scaler_train = MinMaxScaler()

# constants declaration. Notice the constants are same for meaningful comparison.
BATCH_SIZE = 32
NO_OF_EPOCHS = 3

deepanshu-yadav

import glob
from sklearn.preprocessing import MinMaxScaler

training_files = glob.glob(os.path.join(train_dir, '*'))
validation_files = glob.glob(os.path.join(validation_dir, '*'))

# Declare constants like BATCH_SIZE and NO_OF_EPOCHS
BATCH_SIZE = 32
NO_OF_EPOCHS = 3

deepanshu-yadav

import tensorflow as tf 
from tensorflow.keras.layers import InputLayer, Dense, Input, Dropout,\
BatchNormalization, Flatten
from tensorflow.keras import regularizers
from tensorflow.keras.models import Sequential, Model, load_model
from tensorflow.keras.optimizers import Adam
from functools import partial

def create_ae_model(num_hidden_layers=3, hidden_layer_neurons=64,
                    lr=0.001, kernel_init='lecun_normal', optimizer='adam',

deepanshu-yadav

import os 
import numpy as np

NO_OF_FILES = 1000
NO_OF_FEATURES = 1000
MAX_ROW_LIMIT_IN_SINGLE_FILE = 370

train_dir = os.path.join(os.getcwd(), 'train')
os.makedirs(train_dir, exist_ok=True)