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HPC_Cluster_Test.py

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+# -*- 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}")

HPC_Cluster_Test_long_runtime.py

@@ -0,0 +1,72 @@
+# -*- 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}")