Skip to content

Keras Confusion Matrix does not look right

I am running a Keras model on the Breast Cancer dataset. I got around 96% accuracy with it, but the confusion matrix is completely off. Here are the graphs:

enter image description here

enter image description here

And here is my confusion matrix:

enter image description here

The matrix is saying that I have no true negatives and they’re actually false negatives, when I believe that it’s the reverse. Another thing that I noticed is that when the amount of true values are added up and divided by the length of the testing set, the result does not reflect the score that is calculated from the model. Here is the whole code:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

from tensorflow import keras
from tensorflow.math import confusion_matrix
from keras import Sequential
from keras.layers import Dense

breast = load_breast_cancer()
X =
y =

#Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

#Scale data
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.fit_transform(X_test)

#Create and fit keras model
model = Sequential()
model.add(Dense(8, activation='relu', input_shape=[X.shape[1]]))
model.add(Dense(4, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

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

history =, y_train, validation_data=(X_test, y_test), batch_size=16, epochs=50, verbose=1)
history = pd.DataFrame(history.history)

#Display loss visualization


#Create confusion matrix
y_pred = model.predict(X_test)
conf_matrix = confusion_matrix(y_test,y_pred)
cm = sns.heatmap(conf_matrix, annot=True, cmap='gray', annot_kws={'size':30})
cm_labels = ['Positive','Negative']

Am I doing something wrong here? Am I missing something?


Check the confusion matrix values from the sklearn.metrics.confusion_matrix official documentation. The values are so organized:

  • TN: upper left corner
  • FP: upper right corner
  • FN: lower left corner
  • TP: lower right corner

You’re getting 53 true negatives and 90 false negatives from the current confusion matrix.