在 Tensorflow2.x 中使用 image_dataset_from_directory 时如何绘制混淆矩阵？

Question

我的 TF 版本是 2.9 和 Python 3.8。

我已经建立了一个图像二进制分类 CNN model 并且我正在尝试获得一个混淆矩阵。

数据集结构如下。

train/
│------ benign/
│------ normal/ 

test/
│------ benign/
│------ normal/ 

数据集配置如下。

train_ds = tf.keras.utils.image_dataset_from_directory(
    directory = train_data_dir,
    labels="inferred",
    validation_split=0.2,
    subset="training",
    seed=1337,
    color_mode='grayscale',
    image_size=(img_height, img_width),
    batch_size=batch_size,
)

val_ds = tf.keras.utils.image_dataset_from_directory(
    directory = train_data_dir,
    labels="inferred",
    validation_split=0.2,
    subset="validation",
    seed=1337,
    color_mode='grayscale',
    image_size=(img_height, img_width),
    batch_size=batch_size,
)

test_ds = tf.keras.utils.image_dataset_from_directory(
    directory = test_data_dir,
    color_mode='grayscale',
    seed=1337,
    image_size=(img_height, img_width),
    batch_size=batch_size,
)

我参考以下链接编写了代码以获取混淆矩阵。 参考页

这是我关于混淆矩阵的代码。

predictions = model.predict(test_ds)

y_pred = []
y_true = []

# iterate over the dataset
for image_batch, label_batch in test_ds:   # use dataset.unbatch() with repeat
   # append true labels
   y_true.append(label_batch)
   # compute predictions
   preds = model.predict(image_batch)
   # append predicted labels
   y_pred.append(np.argmax(preds, axis = - 1))

# convert the true and predicted labels into tensors
true_labels = tf.concat([item for item in y_true], axis = 0)
predicted_labels = tf.concat([item for item in y_pred], axis = 0)


from sklearn.metrics import confusion_matrix

cm = confusion_matrix(true_labels, predicted_labels)
print(cm)

y_pred 和 y_true 是从上面的 test_ds 中得到的，混淆矩阵的结果如下。

[[200 0]
  [200 0]]

所以我尝试输出true_labels和predicted_labels，并确认predicted_labels都是0，如下所示。

print(true_labels)
<tf.Tensor: shape=(400,), dtype=int32, numpy=
array([0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0,
       1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0,
       0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0,
       0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0,
       1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0,
       0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1,
       0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1,
       1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1,
       1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1,
       0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,
       1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0,
       0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1,
       0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0,
       1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0,
       1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0,
       0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1,
       1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0,
       0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0,
       0, 0, 1, 1])>

print(predicted_labels)
<tf.Tensor: shape=(400,), dtype=int64, numpy=
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0], dtype=int64)>

我不确定为什么 predict_labels 都为零。

但这是错误的。 我认为以下结果是正确的。

[[200 0]
  [0 200]]

怎么了？ 我已经挣扎了几天。 请帮帮我。

非常感谢。

Answer 1

在图像二元分类的情况下，应使用阈值在 model.predict(test_ds) 之后获得预测 label。 我发现将问题y_pred.append(np.argmax(preds, axis = - 1))中的代码修改为y_pred.append(np.where(preds > threshold, 1, 0))解决了这个问题。 希望它对某人有帮助。