Thanatoz-1

from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot
from keras.utils import plot_model
from kt_utils import *


plot_model(happyModel, to_file='HappyModel.png')
SVG(model_to_dot(model).create(prog='dot', format='svg'))

Thanatoz-1

import itertools
import threading
import time
import sys

done = False
#here is the animation
def animate():
    for c in itertools.cycle(['|', '/', '-', '\\']):
        if done:

Thanatoz-1

Hyperparameter are simply 'knobs' and 'turns' used to tune the statistical model of deep learing. 
They are model-specific properties that you fix even before specifying a network and further tune them to create the required fitting of your data.
List of few hyperparameters are:
1. Learning rate
2. Decay rate
3. Number of hidden layers
4. Dropout
5. Activation function
6. Momentum
7. Batch size & a lot other..