actionClasses,
str(np.argmax(output.data.cpu().numpy()))):
print("Correct!!!")
else:
print("Failed!!!")
x[expectedOut.item()][np.argmax(
output.data.cpu().numpy())] = x[expectedOut.item()][np.argmax(
output.data.cpu().numpy())] + 1
# print(output.shape)
# print(np.argmax(output.data.cpu().numpy()))
print(x.shape)
for i in range(0, x.shape[0]):
pass
trace = go.Heatmap(z=(x - np.min(x)) / (np.max(x) - np.min(x)),
x=sorted(actionClasses.keys()),
y=sorted(actionClasses.keys()))
data = [trace]
# plotly.offline.plot({'data': data}, filename='20190416-unfrozen-32.html')
#get pandas dataframe
df_cm = DataFrame(x, index=range(0, x.shape[0]), columns=range(0, x.shape[0]))
#colormap: see this and choose your more dear
cmap = 'PuRd'
cm(df_cm, cmap=cmap)
print("\nGround turth label -- " + fetchClassName(actionClasses, str(expectedOut.item())))
print("Predicted label ----- " + fetchClassName(actionClasses, str(np.argmax(output.data.cpu().numpy()))))
if fetchClassName(actionClasses, str(expectedOut.item())) == fetchClassName(actionClasses, str(np.argmax(output.data.cpu().numpy()))):
print("Correct!!!")
else:
print("Failed!!!")
x[expectedOut.item()][np.argmax(output.data.cpu().numpy())] = x[expectedOut.item()][np.argmax(output.data.cpu().numpy())] + 1;
# print(output.shape)
# print(np.argmax(output.data.cpu().numpy()))
print(x.shape)
for i in range(0, x.shape[0]):
pass
trace = go.Heatmap(z=(x-np.min(x))/(np.max(x)-np.min(x)), x=sorted(actionClasses.keys()), y=sorted(actionClasses.keys()))
data=[trace]
# plotly.offline.plot({'data': data}, filename='20190416-unfrozen-32.html')
#get pandas dataframe
df_cm = DataFrame(x, index=range(0,x.shape[0]), columns=range(0,x.shape[0]))
#colormap: see this and choose your more dear
cmap = 'PuRd'
cm(df_cm, cmap=cmap);