HPC_Cluster_Test.py

# -*- coding: utf-8 -*-
"""
Created on Thu Oct 29 16:22:11 2020

@author: jaych
"""
#%%
#print("test")
import time
start = time.time()
import tensorflow as tf
#assert tf.__version__ >= "2.0"

# Common imports
import numpy as np
import os
#import numpy as np
from numpy import loadtxt
from keras.models import Sequential
from keras.layers import Dense

# to make this notebook's output stable across runs
np.random.seed(42)

# To plot pretty figures
#%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
# mpl.rc('axes', labelsize=14)
# mpl.rc('xtick', labelsize=12)
# mpl.rc('ytick', labelsize=12)


#%%

dataset = loadtxt('pima-indians-diabetes.csv.txt', delimiter=',')
print(dataset.shape)
X = dataset[:,0:8]
y = dataset[:,8]

#%%
# define the keras model
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
#%%
model.fit(X, y, epochs=1000, batch_size=10)
#evaluate the keras model
_, accuracy = model.evaluate(X, y)
print('Accuracy: %.2f' % (accuracy*100))


model1 = Sequential()
model1.add(Dense(12, input_dim=8, activation='relu'))
model1.add(Dense(8, activation='relu'))
model1.add(Dense(1, activation='sigmoid'))

model1.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
#%%
model1.fit(X, y, epochs=3000, batch_size=10)
#evaluate the keras model
_, accuracy = model.evaluate(X, y)
print('Accuracy: %.2f' % (accuracy*100))


          
#%%
end = time.time()
print(f"Runtime of the program is {end - start}")