def visualization(self):
feature_1 = self.x_pca[:, 0]
feature_2 = self.x_pca[:, 1]
labels = self.y
cdict = {0: 'red', 1: 'green'}
labl = {0: 'without bug', 1: 'bug'}
marker = {0: '*', 1: 'o'}
alpha = {0: .3, 1: .5}
fig, ax = plt.subplots(figsize=(7, 5))
fig.patch.set_facecolor('white')
for l in np.unique(labels):
ix = np.where(labels == l)
ax.scatter(feature_1[ix],
feature_2[ix],
c=cdict[l],
s=100,
label=labl[l],
marker=marker[l],
alpha=alpha[l])
# for loop ends
plt.xlabel("First Principal Component", fontsize=14)
plt.ylabel("Second Principal Component", fontsize=14)
plt.legend()
plt.savefig(
str(pathlib.Path().absolute()) + "/File/PCA_visualization.png")
def create_plot(logbook, name_file):
maxFitnessValues, meanFitnessValues, minFitnessValues, medianFitnessValues, stdFitnessValues = \
logbook.select("max", "avg", "min", "median", "std")
plt.plot(maxFitnessValues, color='red', label="Worst Fitness")
plt.plot(meanFitnessValues, color='green', label="Mean Fitness")
plt.plot(minFitnessValues, color='orange', label="Best Fitness")
plt.plot(medianFitnessValues, color='blue', label="Avg. Fitness")
plt.plot(stdFitnessValues, color='pink', label="Std. Fitness")
plt.xlabel('Generation')
plt.ylabel('Max / Average / Min / Median/ Std Fitness')
plt.title('Max, Average, Min, Median and Std Fitness over Generations')
plt.legend(loc='lower right')
plt.savefig(name_file)
plt.close()
content = [x.strip() for x in content]
original_cc = []
supergraph_cc = []
for line in content:
value = line.split(" ")
if len(value):
supergraph_cc.append(float(value.pop()))
original_cc.append(float(value.pop()))
plt.clf()
plt.plot(k_array, original_cc, 'r--', k_array,
supergraph_cc, 'g-')
plt.ylabel("Clustering Coefficent")
plt.xlabel("k_degree")
plt.legend(('Original Graph', 'Supergraph'),
loc='lower center',
shadow=True)
plt.title(str(sys.argv[4]))
plt.savefig("metric_cc_web.png") #if choose dataset web
#plt.savefig("metric_cc_socfb.png")
plt.clf()
list_norm = []
for i in norm:
list_norm.append(float(i))
list_k_array = []
for i in k_array:
list_k_array.append(float(i))
plt.plot(list_k_array, list_norm, 'r--')
plt.ylabel("Norm of vector a")
plt.xlabel("k_degree")
plt.title(str(sys.argv[4]))
epochs = 5 #训练5次
model1.summary() #模型输出
model1.compile(
loss='sparse_categorical_crossentropy', #模型编译
optimizer='adam',
metrics=['accuracy'])
#从训练集中抽取0.2进行验证
history = model1.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
validation_split=0.2)
#-----------------------------------------------保存模型，可视化--------------------------
#保存模型
model1.save('model_CNN_text.h5')
#模型可视化
plot_model(model1, to_file='model_CNN_text.png', show_shape=True)
#加载模型
model = load_model('model_CNN_text.h5')
y_new = model.predict(x_train[0].reshape(1, 50))
#训练结果可视化
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('Model accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend(['Train', 'Valid'], loc='upper left')
plt.savefig('Valid_acc.png')
plt.show()
#-----------------------------------------------------查看解码效果--------------------------------------------
decoded_imgs = model.predict(x_test)
n = 10
plt.figure(figsize=(20, 6))
for i in range(n):
# 原图
ax = plt.subplot(3, n, i+1)
plt.imshow(x_test[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# 解码效果图
ax = plt.subplot(3, n, i+n+1)
plt.imshow(decoded_imgs[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
#----------------------------------------------------训练过程可视化---------------------------------------------
print(history.history.keys())
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper right')
plt.show()
