jacobian

commit fd2dd8912dbb784155fd3d46cf835c599b6458f9
parent 76ae33f01f4968537908cbbb4010d1444e974c37
Author: David Freifeld <freifeld.david@gmail.com>
Date:   Mon, 29 Jun 2020 09:35:34 -0700

Poor attempts to benchmark more

Diffstat:
M
bpnn.cpp
 | 
2
+-
M
example.cpp
 | 
41
+++++++++++++++++++----------------------
M
example.py
 | 
40
++++++++++++++++++++--------------------
M
kerasdemo.py
 | 
20
++++++++++----------

4 files changed, 50 insertions(+), 53 deletions(-)
diff --git a/bpnn.cpp b/bpnn.cpp
@@ -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.cpp b/example.cpp
@@ -3,31 +3,28 @@
 #include "unistd.h"
 #include <ctime>
 
-int main()
+double bench(int batch_sz)
 {
-  Network net ("./data_banknote_authentication.txt", 10, 0.01, 0.001);
+  auto start = std::chrono::high_resolution_clock::now();
+  Network net ("./data_banknote_authentication.txt", batch_sz, 0.0155, 0.03);
   net.add_layer(4, "linear");
-  net.add_layer(5, "lecun_tanh");
+  net.add_layer(5, "relu");
   net.add_layer(1, "resig");
   net.initialize();
-  //  net.list_net();
-  net.train(1);
-  //  net.list_net();
-  //printf("%i\n", wc("./data_banknote_authentication.txt"));
+  net.train(50);
+  auto end = std::chrono::high_resolution_clock::now();
+  return std::chrono::duration_cast<std::chrono::nanoseconds>(end - start).count() / pow(10,9);
+}
 
-  // double x = 0.4235;
-  // auto bench_start = std::chrono::high_resolution_clock::now();
-  // x = tanh(x);
-  // printf("%lf", x);
-  // auto tanh_end = std::chrono::high_resolution_clock::now();
-  // cosh(x);
-  // auto cosh_end = std::chrono::high_resolution_clock::now();
-  // exp(x);
-  // auto exp_end = std::chrono::high_resolution_clock::now();
-  // log(x);
-  // auto log_end = std::chrono::high_resolution_clock::now();
-  // x = x - (1/3 * pow(x, 3)) + (2/15 * pow(x, 5)) - (17/315 * pow(x, 7));
-  // printf("%lf", x);
-  // auto pow_end = std::chrono::high_resolution_clock::now();
-  // std::cout << " TANH " << std::chrono::duration_cast<std::chrono::nanoseconds>(tanh_end - bench_start).count() << " COSH " << std::chrono::duration_cast<std::chrono::nanoseconds>(cosh_end - tanh_end).count() << " EXP " << std::chrono::duration_cast<std::chrono::nanoseconds>(exp_end - cosh_end).count() << " BETTER TANH? " << std::chrono::duration_cast<std::chrono::nanoseconds>(log_end - exp_end).count() << " POW " << std::chrono::duration_cast<std::chrono::nanoseconds>(pow_end - log_end).count() << "\n";
+int main()
+{
+  double x[1340];
+  for(int i = 0; i < 3; i++) {
+    x[i] = bench(i);
+  }
+  printf("[");
+  for(int i = 0; i < 3; i++) {
+    printf("%d, ", x[i]);
+  }
+  printf("]");
 }
diff --git a/example.py b/example.py
@@ -11,7 +11,6 @@ def bench(batch_sz, layers):
         net.add_layer(5, "relu")
     net.add_layer(1, "resig")
     net.initialize()
-    initend = time.time()
     net.train(50)
     end = time.time()
     return (end-init)
@@ -21,13 +20,14 @@ def bench(batch_sz, layers):
 # for i in range(trials):
 #     timesum+=bench()
 # print("Averages over %s trials\n--------------\nTime: %s seconds.\n" % (trials, timesum/trials))
-# x = []
-# y = []
-# i = 1
-# while(i < 1340):
-#     y.append(bench(i, 1))
-#     x.append(i)
-#     i+=1;
+x = []
+y = []
+i = 1
+while(i < 1340):
+    y.append(bench(i, 1))
+    x.append(i)
+    i+=1
+print(y)
 # plt.plot(x,y, label = "ML-in-Parallel (Sequential)")
 # i = 20
 # otherlist = []
@@ -39,20 +39,20 @@ def bench(batch_sz, layers):
 # plt.legend()
 # plt.show()
 
-sum = 0
-for i in range(10):
-    sum += bench(10, 1)
-print(sum/10)
+# sum = 0
+# for i in range(10):
+#     sum += bench(10, 1)
+# print(sum/10)
 
-x = ['Keras', 'MIP-Sequential']
-speed = [4.71297559738, 0.043680644035339354]
+# x = ['Keras', 'MIP-Sequential']
+# speed = [4.71297559738, 0.043680644035339354]
 
-x_pos = [i for i, _ in enumerate(x)]
+# 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.bar(x_pos, speed, color='green')
+# plt.ylabel("Time (s)")
+# plt.title("Average Runtime (10 trials)")
 
-plt.xticks(x_pos, x)
+# plt.xticks(x_pos, x)
 
-plt.show()
+# plt.show()
diff --git a/kerasdemo.py b/kerasdemo.py
@@ -35,14 +35,14 @@ def kerasbench(batch_sz, layers):
     end = time.time()
     return (end-init)
 
-sum = 0
-for i in range(10):
-    sum += kerasbench(10, 1)
-print(sum/10)
+# 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)
+y2 = []
+y2.append(kerasbench(1, 1))
+y2.append(kerasbench(5, 1))
+y2.append(kerasbench(10, 1))
+y2.append(kerasbench(15, 1))
+print(y2)