anuj2110

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D,MaxPooling2D,BatchNormalization,Dropout,Flatten,Dense

num_features = 64
num_labels=7
model = Sequential()
model.add(Conv2D(num_features, kernel_size=(3, 3), activation='relu', input_shape=(48, 48, 1), kernel_regularizer=l2(0.01)))
model.add(Conv2D(num_features, kernel_size=(3, 3), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))

anuj2110

import pandas as pd
import numpy as np

def loadDataFromCSV(path="fer2013/fer2013.csv"):
    df = pd.read_csv(path) #readeing the csv
    pixels = df["pixels"].tolist() #extracting the pixels column
    emotions = pd.get_dummies(df["emotion"]).as_matrix() #extracting emotions and turning them into one-hot labels
    faces =  [] #list for storing faces
    for pixel_sequence  in tqdm(pixels): #main loop for turning the pixel value to images and storing them as numpy arrays
        face = [int(pixel) for pixel in pixel_sequence.split(" ")]

anuj2110

import 'dart:html';

void main(){
  
  final ButtonElement button = querySelector('button');
  
  button.onClick
    .timeout(
          
        new Duration(seconds:1),

anuj2110

import 'dart:async';

class Cake {}

class Order {
  String type;
  Order(this.type);
}

void main() {

anuj2110

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

def get_weights(total_data):
    '''
    function for initialize random values in the weight vectors for the neural network to be used.
    uses the no of features to initialize a vector.
    '''
    y = np.random.random()*(2.0/np.sqrt(total_data))