add python loss plot file, put all scripts in folder

Author: liruoteng

Diff for: convert_flow_format.py renamed to scripts/convert_flow_format.py

@@ -0,0 +1,101 @@
+#!/usr/bin/python
+import os
+import re
+import sys
+import numpy as np
+import matplotlib.pyplot as plt
+
+TRAIN_LOSS_PATTERN = r"Iteration (\d+), loss = (\d+\.\d*)"
+#TEST_LOSS_PATTERN = r"Iteration (\d+), Testing net \(#0\)\n.*\n.*\n.*\n.* rec_loss = (\d+\.\d+)"
+#TEST_LOSS_PATTERN  = r"Iteration (\d+), Testing net \(#0\)\n.*\n.* loss = (\d+\.\d+)"
+#TEST_ACC_PATTERN   = r"Iteration (\d+), Testing net \(#0\)\n.* accuracy = (\d+\.\d+)"
+
+def main():
+    if len(sys.argv) > 1:
+        log_file_name = sys.argv[1]
+    else:
+        raise("please provide log file to process")
+
+    log_file = open(log_file_name, 'r')
+    log_data = log_file.read()
+    training_result = re.findall(TRAIN_LOSS_PATTERN,log_data)
+    #testing_result = re.findall(TEST_LOSS_PATTERN, log_data)
+    #testing_accuracy = re.findall(TEST_ACC_PATTERN, log_data)
+
+    train_iter = []
+    train_loss = []
+    test_iter = []
+    test_loss = []
+    test_acc_iter = []
+    test_acc = []
+
+   # test_loss_length = len(testing_result[0]) - 1
+    for train in training_result:
+        train_iter.append(int(train[0]))
+        train_loss.append(float(train[1]))
+    '''
+    for test in testing_result:
+        test_iter.append(int(test[0]))
+        temp_loss = 0
+        for i in range(test_loss_length):
+            temp_loss += float(test[i+1])
+        test_loss.append(temp_loss)
+    '''
+    #for test in testing_accuracy:
+    #    test_acc_iter.append(int(test[0]))
+    #    test_acc.append(float(test[1]))
+
+    #print test_iter
+    #print test_loss
+    # display
+    plt.plot(train_iter, train_loss, 'k', label='Train loss', linewidth=0.75)
+    #plt.plot(test_iter, test_loss, 'r', label='Test loss', linewidth=1.0)
+    #plt.plot(test_acc_iter, test_acc, 'b', label='Test accuracy', linewidth=1.0)
+    plt.legend()
+    #plt.minorticks_on()
+    plt.ylabel('Loss')
+    plt.xlabel('Iteration')
+    #plt.yticks(np.arange(0, 2.5, 0.1))
+    plt.grid()
+    plt.savefig(os.path.join(os.path.dirname(log_file_name), log_file_name) +'.png')
+
+
+def disp_results(fig, ax1, ax2, loss_iterations, losses, accuracy_iterations, accuracies, accuracies_iteration_checkpoints_ind, fileName, color_ind=0):
+    modula = len(plt.rcParams['axes.color_cycle'])
+    acrIterations =[]
+    top_acrs={}
+    if accuracies.size:
+        if 	accuracies.size>4:
+		    top_n = 4
+        else:
+            top_n = accuracies.size -1
+        temp = np.argpartition(-accuracies, top_n)
+        result_indexces = temp[:top_n]
+        temp = np.partition(-accuracies, top_n)
+        result = -temp[:top_n]
+        for acr in result_indexces:
+            acrIterations.append(accuracy_iterations[acr])
+            top_acrs[str(accuracy_iterations[acr])]=str(accuracies[acr])
+
+        sorted_top4 = sorted(top_acrs.items(), key=operator.itemgetter(1))
+        maxAcc = np.amax(accuracies, axis=0)
+        iterIndx = np.argmax(accuracies)
+        maxAccIter = accuracy_iterations[iterIndx]
+        maxIter =   accuracy_iterations[-1]
+        consoleInfo = format('\n[%s]:maximum accuracy [from 0 to %s ] = [Iteration %s]: %s ' %(fileName,maxIter,maxAccIter ,maxAcc))
+        plotTitle = format('max accuracy(%s) [Iteration %s]: %s ' % (fileName,maxAccIter, maxAcc))
+        print (consoleInfo)
+        #print (str(result))
+        #print(acrIterations)
+       # print 'Top 4 accuracies:'
+        print ('Top 4 accuracies:'+str(sorted_top4))
+        plt.title(plotTitle)
+    ax1.plot(loss_iterations, losses, color=plt.rcParams['axes.color_cycle'][(color_ind * 2 + 0) % modula])
+    ax2.plot(accuracy_iterations, accuracies, plt.rcParams['axes.color_cycle'][(color_ind * 2 + 1) % modula], label=str(fileName))
+    ax2.plot(accuracy_iterations[accuracies_iteration_checkpoints_ind], accuracies[accuracies_iteration_checkpoints_ind], 'o', color=plt.rcParams['axes.color_cycle'][(color_ind * 2 + 1) % modula])
+    plt.legend(loc='lower right')
+
+
+
+if __name__ == "__main__":
+    main()