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