SherazKhan

import keras
from keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D, GlobalAveragePooling2D, BatchNormalization
from keras.models import Sequential
from keras.optimizers import Adam
from keras import regularizers

def model_cnn_reg(input_shape):
  model=Sequential()
  model.add(Conv2D(filters=64,kernel_size=(3,3), input_shape=input_shape, activation='relu'))
  model.add(MaxPooling2D(pool_size=(2,2)))

SherazKhan

import numpy as np
import tensorflow.keras as keras

def build_model(input_shape):
    """Generates RNN-LSTM model
    :param input_shape (tuple): Shape of input set
    :return model: RNN-LSTM model
    """

SherazKhan

import numpy as np
import tensorflow.keras as keras

def build_model(input_shape):
    """Generates RNN-LSTM model
    :param input_shape (tuple): Shape of input set
    :return model: RNN-LSTM model
    """

    # build network topology

SherazKhan

import numpy as np
import tensorflow.keras as keras

def build_model(input_shape):
    """Generates CNN model
    :param input_shape (tuple): Shape of input set
    :return model: CNN model
    """

    # build network topology

SherazKhan

def extract_feature_means(audio_file_path: str) -> pd.DataFrame:
    # config settings
    number_of_mfcc = c.NUMBER_OF_MFCC

    # 1. Importing 1 file
    y, sr = librosa.load(audio_file_path)

    # Trim leading and trailing silence from an audio signal (silence before and after the actual audio)
    signal, _ = librosa.effects.trim(y)

SherazKhan

import matplotlib
matplotlib.use('Qt5Agg')
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.feature_selection import f_classif, mutual_info_classif
from sklearn.preprocessing import MinMaxScaler
from itertools import cycle
from numpy import interp

SherazKhan

from imblearn.ensemble import BalancedRandomForestClassifier
from sklearn.datasets import make_classification
from imblearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn import metrics

X, y = make_classification(n_samples=1000, n_classes=3,
                           n_informative=4, weights=[0.2, 0.3, 0.5],
                           random_state=0)

SherazKhan

import matplotlib
matplotlib.use('TkAgg')
import mne
import h5py
import numpy as np
import matplotlib.pyplot as plt
import numpy.typing as npt
plt.ion()

def zero_detector(arr: npt.NDArray[np.uint]) -> list([npt.NDArray[np.uint],

SherazKhan

#/bin/sh

# PostgreSQL database backup script

DB_USERNAME=''
DB_NAME=''
DB_PASSWORD=''
DB_HOST=''
TIMESTAMP=`date +%Y%m%d-%H%M`
BACKUP_DIR='/Vast/pg_backup'

SherazKhan

import numpy as np
import matplotlib.pyplot as plt

def rectified(x):
	return max(0.0, x)

X = np.arange(-100, 100)
y = np.array([rectified(x) for x in X])
for index in range(1, 10):
    yy = y ** index