initDict = { 'Proposed': 'data_init', 'LeCun': 'lecun_uniform', 'GlorotNorm': 'glorot_normal', 'GlorotUni': 'glorot_uniform', 'HeNorm': 'he_normal', 'HeUni': 'he_uniform' } for name, initName in initDict.items(): # ----------------------------------------------- Load History try: HistoryPath = os.path.join( save_dir, optName + "_" + initName + "_Hist_" + act + "_lr_{:1}_epoch_{:1d}_".format(lr, epochs) + model_name) hist = loadHistory(HistoryPath) # -------------------------------------------------------------------------------------------------- key = 'acc' x = np.arange(len(hist[key])) y = hist[key] plt.plot(x, y, label=name) except FileNotFoundError: next plt.title("Accuracy Over Initializations for tanh ( " + optName + " Optimization)") plt.xlabel("No of Epochs ({:d})".format(epochs)) plt.ylabel("Accuracy") plt.legend()
import os from ReadImages import loadHistory, saveHistory import pickle import matplotlib.pyplot as plt import numpy as np base_dir = "/home/ankit/Desktop/Dataset/CIFAR10Dataset/" save_dir = os.path.join(base_dir, 'SavedModels') model_name = 'Keras_CIFAR10_trainModel_v1.h5' idx = 1 hist = {} for idx in range(1, 6, 1): load_model_path = os.path.join(save_dir, str('hist_') + str(idx) + model_name) hist["history{:0}".format(idx)] = loadHistory( load_model_path) #Load History histCopy = hist #----------------------------------------------------------------------------------------------- For all all_para = hist['history1'][key] + hist['history2'][key] + hist['history3'][ key] + hist['history4'][key] + hist['history5'][key] #----------------------------------------------------------------------------------------------- Accuarcy key = 'acc' all_para = hist['history1'][key] + hist['history2'][key] + hist['history3'][ key] + hist['history4'][key] + hist['history5'][key] x = np.arange(len(all_para)) y = all_para