如何从以下模型中获取混淆矩阵？

Question

X代表特征，Y代表图像分类的标签。我使用CNN进行二进制图像分类，就像猫和狗一样。

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
import pickle
import numpy as np
from sklearn.metrics import confusion_matrix


X = np.array(pickle.load(open("X.pickle","rb")))
Y = np.array(pickle.load(open("Y.pickle","rb")))

x_test = np.array(pickle.load(open("x_test.pickle","rb")))
y_test = np.array(pickle.load(open("y_test.pickle","rb")))


# X = np.array(pickle.load(open("x_train.pickle","rb")))
# Y = np.array(pickle.load(open("y_train.pickle","rb")))

#scaling our image data
X = X/255.0

model = Sequential()
#model.add(Conv2D(64 ,(3,3), input_shape = X.shape[1:]))
model.add(Conv2D(64 ,(3,3), input_shape = X.shape[1:]))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size = (2,2)))

model.add(Conv2D(128 ,(3,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size = (2,2)))

model.add(Conv2D(256 ,(3,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size = (2,2)))

model.add(Conv2D(512 ,(3,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size = (2,2)))

model.add(Flatten())

model.add(Dense(2048))
model.add(Activation("relu"))

model.add(Dropout(0.5))

np.argmax(model.add(Dense(2)))
model.add(Activation('softmax'))

model.compile(loss="binary_crossentropy",
             optimizer = "adam",
             metrics = ['accuracy'])

predicted = model.predict(x_test)
print(predicted.shape)
print(y_test.shape)
print(confusion_matrix(y_test,predicted))

预测和y_test形状的输出是（90,2）和（90，），当我使用混淆矩阵时，它刷新：-ValueError：分类指标不能处理二进制和连续多输出目标的混合。

Answer 1

下面是一个如何使用PyCM库获取混淆矩阵的示例：您需要在anaconda中安装PyCM库或使用pip3

conda install -c sepandhaghighi pycm

from pycm import *

def plot_confusion_matrix(cm, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):
    """
    This function modified to plots the ConfusionMatrix object.
    Normalization can be applied by setting 'normalize=True'.
    Code Reference : 
    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html
    """
    plt_cm = []
    for i in cm.classes :
        row=[]
        for j in cm.classes:
            row.append(cm.table[i][j])
        plt_cm.append(row)
    plt_cm = np.array(plt_cm)
    if normalize:
        plt_cm = plt_cm.astype('float') / plt_cm.sum(axis=1)[:, np.newaxis]     
    plt.imshow(plt_cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(cm.classes))
    plt.xticks(tick_marks, cm.classes, rotation=45)
    plt.yticks(tick_marks, cm.classes)

    fmt = '.2f' if normalize else 'd'
    thresh = plt_cm.max() / 2.
    for i, j in itertools.product(range(plt_cm.shape[0]), range(plt_cm.shape[1])):
        plt.text(j, i, format(plt_cm[i, j], fmt),
                horizontalalignment="center",
                color="white" if plt_cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('Actual')
    plt.xlabel('Prediction')

......

svm.fit(x_train, y_train)
y_predicted = svm.predict(x_test)

#Get the confusion Matrix:
cm = ConfusionMatrix(actual_vector=y_test, predict_vector=y_predicted)

#Print classes
print("[INFO] Clases")
print(cm.classes)
#Print the table of cmatrix
print(cm.table)
#indicators of the confusion matrix
print(cm)

问候语！

Answer 2

您可以使用Scikit学习：

from sklearn.metrics import confusion_matrix

predicted = model.predict(x_test)
print(confusion_matrix(y_test,predicted.round()))

这里的Scikit学习留档混淆矩阵：

https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html

编辑：

建议：

• 首选在输出层使用Softmax激活，无论是二进制还是多标签分类。使用softmax，输出层的节点数=没有类。