commit 76ae33f01f4968537908cbbb4010d1444e974c37
parent 90ba7be49aa4574cddee3de9158f5b425ff27036
Author: David Freifeld <freifeld.david@gmail.com>
Date: Sun, 28 Jun 2020 23:09:19 -0700
Ran benchmarking tests
Diffstat:
| M | bpnn.cpp | | | 4 | ++-- |
| M | example.py | | | 53 | ++++++++++++++++++++++++++++++++++++++++++++--------- |
| M | kerasdemo.py | | | 50 | ++++++++++++++++++++++++++++---------------------- |
3 files changed, 74 insertions(+), 33 deletions(-)
diff --git a/bpnn.cpp b/bpnn.cpp
@@ -290,7 +290,7 @@ void Network::train(int total_epochs)
float epoch_cost = 1000;
float epoch_accuracy = -1;
int epochs = 0;
- // printf("Beginning train on %i instances for %i epochs...\n", instances, total_epochs);
+ //printf("Beginning train on %i instances for %i epochs...\n", instances, total_epochs);
double batch_time = 0;
while (epochs < total_epochs) {
float cost_sum = 0;
@@ -308,7 +308,7 @@ void Network::train(int total_epochs)
}
epoch_accuracy = 1.0/((float) instances/batch_size) * acc_sum;
epoch_cost = 1.0/((float) instances/batch_size) * cost_sum;
- printf("Epoch %i/%i - cost %f - acc %f\n", epochs+1, total_epochs, epoch_cost, epoch_accuracy);
+ // printf("Epoch %i/%i - cost %f - acc %f\n", epochs+1, total_epochs, epoch_cost, epoch_accuracy);
batches=1;
epochs++;
rewind(data);
diff --git a/example.py b/example.py
@@ -1,23 +1,58 @@
import mrbpnn
+import matplotlib.pyplot as plt
import numpy
import time
-def bench():
+def bench(batch_sz, layers):
init = time.time()
- net = mrbpnn.Network("./data_banknote_authentication.txt", 10, 0.001, 0.03);
- net.add_layer(4, "linear");
- net.add_layer(5, "relu");
- net.add_layer(1, "resig");
- net.initialize();
+ net = mrbpnn.Network("./data_banknote_authentication.txt", batch_sz, 0.0155, 0.03)
+ net.add_layer(4, "linear")
+ for i in range(layers):
+ net.add_layer(5, "relu")
+ net.add_layer(1, "resig")
+ net.initialize()
initend = time.time()
- net.train(50);
+ net.train(50)
end = time.time()
return (end-init)
-# print("%s: init %s" % (end-init, initend-init))
# timesum=0
# trials = 1000
# for i in range(trials):
# timesum+=bench()
# print("Averages over %s trials\n--------------\nTime: %s seconds.\n" % (trials, timesum/trials))
-print(bench())
+# x = []
+# y = []
+# i = 1
+# while(i < 1340):
+# y.append(bench(i, 1))
+# x.append(i)
+# i+=1;
+# plt.plot(x,y, label = "ML-in-Parallel (Sequential)")
+# i = 20
+# otherlist = []
+# while(i < 1340):
+# otherlist.append(i)
+# i += 20
+
+# plt.plot(otherlist, [2.3994078636169434, 1.2735769748687744, 1.0030598640441895, 0.8972160816192627, 0.801548957824707, 0.752018928527832, 0.7073678970336914, 0.6857280731201172, 0.6707980632781982, 0.6421489715576172, 0.6614980697631836, 0.6403779983520508, 0.7251319885253906, 0.6796879768371582, 0.6601080894470215, 0.6711599826812744, 0.6432759761810303, 0.6491389274597168, 0.6762490272521973, 0.6859049797058105, 0.7067179679870605, 0.7142889499664307, 0.7258059978485107, 0.7868969440460205, 0.7326970100402832, 0.7365641593933105, 0.7576079368591309, 0.7772500514984131, 0.8062641620635986, 0.7768490314483643, 0.8253629207611084, 0.8264601230621338, 0.8459320068359375, 0.9670729637145996, 0.8388969898223877, 0.9129719734191895, 0.9009649753570557, 0.8916170597076416, 0.8926799297332764, 0.9171609878540039, 0.9242072105407715, 0.9534740447998047, 0.947465181350708, 0.9723358154296875, 1.018247127532959, 1.1208629608154297, 1.014026165008545, 1.034980058670044, 1.0626468658447266, 1.080394983291626, 1.0627479553222656, 1.0839190483093262, 1.0938241481781006, 1.127730131149292, 1.1265759468078613, 1.136888027191162, 1.140428066253662, 1.1712510585784912, 1.206390142440796, 1.2087180614471436, 1.4066569805145264, 1.2425589561462402, 1.280066967010498, 1.2891559600830078, 1.3243582248687744, 1.3152379989624023], label="Keras")
+# plt.legend()
+# plt.show()
+
+sum = 0
+for i in range(10):
+ sum += bench(10, 1)
+print(sum/10)
+
+x = ['Keras', 'MIP-Sequential']
+speed = [4.71297559738, 0.043680644035339354]
+
+x_pos = [i for i, _ in enumerate(x)]
+
+plt.bar(x_pos, speed, color='green')
+plt.ylabel("Time (s)")
+plt.title("Average Runtime (10 trials)")
+
+plt.xticks(x_pos, x)
+
+plt.show()
diff --git a/kerasdemo.py b/kerasdemo.py
@@ -18,25 +18,31 @@ from keras import backend as K
from keras.models import Sequential
from keras.layers import Activation
from keras.layers import Dense
-init = time.time()
-# load the dataset
-dataset = loadtxt('data_banknote_authentication.txt', delimiter=',')
-# split into input (X) and output (y) variables
-X = dataset[:,0:4]
-y = dataset[:,4]
-# define the keras model
-model = Sequential()
-model.add(Dense(4, input_dim=4, activation='linear'))
-model.add(Dense(5, activation='relu'))
-model.add(Dense(1, activation='sigmoid'))
-# compile the keras model
-opt = keras.optimizers.SGD(lr=0.0155)
-model.compile(loss='mse', optimizer=opt, metrics=['accuracy'])
-# fit the keras model on the dataset
-initend = time.time()
-model.fit(X, y, epochs=50, batch_size=10)
-# evaluate the keras model
-#_, accuracy = model.evaluate(X, y)
-#print('Accuracy: %.2f' % (accuracy*100))
-end = time.time()
-print("%s: init %s" % (end-init, initend-init))
+
+def kerasbench(batch_sz, layers):
+ init = time.time()
+ dataset = loadtxt('data_banknote_authentication.txt', delimiter=',')
+ X = dataset[:,0:4]
+ y = dataset[:,4]
+ model = Sequential()
+ model.add(Dense(4, input_dim=4, activation='linear'))
+ for i in range(layers):
+ model.add(Dense(5, activation='relu'))
+ model.add(Dense(1, activation='sigmoid'))
+ opt = keras.optimizers.SGD(lr=0.0155)
+ model.compile(loss='mse', optimizer=opt, metrics=['accuracy'])
+ model.fit(X, y, epochs=50, batch_size=batch_sz)
+ end = time.time()
+ return (end-init)
+
+sum = 0
+for i in range(10):
+ sum += kerasbench(10, 1)
+print(sum/10)
+
+# y2 = []
+# i = 20
+# while (i < 1340):
+# y2.append(kerasbench(i, 1))
+# i+=20
+# print(y2)
