錯誤越來越混亂矩陣

Question

我想用下面的代碼（MNIST分類）混淆矩陣：

from sklearn.metrics import confusion_matrix 
from __future__ import print_function 
import keras 
from keras.datasets import mnist 
from keras.models import Sequential 
from keras.layers import Dense, Dropout, Flatten 
from keras.layers import Conv2D, MaxPooling2D 
from keras import backend as K 
from keras.callbacks import TensorBoard 
import numpy as np 
batch_size = 128 
num_classes = 10 
epochs = 1 

# input image dimensions 
img_rows, img_cols = 28, 28 

# the data, shuffled and split between train and test sets 
(x_train, y_train), (x_test, y_test) = mnist.load_data() 

if K.image_data_format() == 'channels_first': 
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols) 
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols) 
    input_shape = (1, img_rows, img_cols) 
else: 
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1) 
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1) 
    input_shape = (img_rows, img_cols, 1) 

x_train = x_train.astype('float32') 
x_test = x_test.astype('float32') 
x_train /= 255 
x_test /= 255 
print('x_train shape:', x_train.shape) 
print(x_train.shape[0], 'train samples') 
print(x_test.shape[0], 'test samples') 

# convert class vectors to binary class matrices 
y_train = keras.utils.to_categorical(y_train, num_classes) 
y_test = keras.utils.to_categorical(y_test, num_classes) 

model = Sequential() 
model.add(Conv2D(32, kernel_size=(3, 3), 
       activation='relu', 
       input_shape=input_shape)) 
model.get_weights() 
model.add(Conv2D(64, (3, 3), activation='relu')) 
model.add(MaxPooling2D(pool_size=(2, 2))) 
model.add(Dropout(0.25)) 
model.add(Flatten()) 
model.add(Dense(128, activation='relu')) 
model.add(Dropout(0.5)) 
model.add(Dense(num_classes, activation='softmax')) 

model.compile(loss=keras.losses.categorical_crossentropy, 
       optimizer=keras.optimizers.Adadelta(), 
       metrics=['accuracy']) 

model.fit(x_train, y_train, 
      batch_size=batch_size, 
      epochs=epochs, 
      verbose=1, 
      validation_data=(x_test, y_test)) 
y_pred=model.predict(x_test) 
confusion_matrix(y_test, y_pred) 

，但我得到了以下錯誤：

ValueError: Can't handle mix of multilabel-indicator and continuous-multioutput. I think I wrong interpreted meaning of y_pred or calculted it wrong.

我怎樣才能解決這個問題？

Answer 1

confusion_matrix期望真實的和預測的類別標籤，而不是一個熱點/概率分佈表示。使用以下內容替換最後一行：

confusion_matrix(y_test.argmax(axis=1), y_pred.argmax(axis=1)) 

這將10000x10格式轉換爲10000對應於每個樣本的預測類。