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);