CNN培训多标签分类---不起作用

Question

Try to predict texture images' labels, an image can contain two labels like['banded', 'striped'], though most of them only have one label.

The output accuracy is extremely high....the first epoch can have 0.96 acc...but the prediction array are all close to 0, which is wrong, there must be at least one number is relevant closed to 1.

Can someone help me? Thank you!!

Here are the code

Input image = (read by opencv)/255
Multi-labels = First LabelEncoder convert to numbers, then keras.to_categorical

Then I built a CNN model as follow

X_train, X_test, y_train, y_test = train_test_split(img_array, test_value, test_size=0.1)

model = Sequential()
model.add(Conv2D(filters=64, kernel_size=(5, 5), padding='Same', data_format='channels_last', activation='relu',
                 input_shape=(300, 300, 3)))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Conv2D(filters=32, kernel_size=(3, 3), padding='Same', activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(300, init ='uniform',activation='relu'))
model.add(Dense(285, init = 'uniform',activation='sigmoid'))
model.compile(optimizer='sgd', loss='binary_crossentropy', metrics=['accuracy'])


history = model.fit(X_train, y_train, batch_size= 24, epochs=10, validation_split=0.15)

Answer 1

If your model has only 2 labels, the last layer should be

model.add(Dense(2, init = 'uniform',activation='sigmoid'))

However, your class imbalance can also affect the accuracy. If your class imbalance is too high, your model will show 95%+ training, validation, and testing accuracies but the individual accuracies will still be low and the model will not work for real world data.

The detailed and class-based accuracy can be understood using :

from sklearn.metrics import classification_report



X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.30)
X_test1, X_valid, y_test1, y_valid = train_test_split(X_test, y_test, test_size=0.30)
model.fit(X_train, y_train, batch_size=64, epochs=8, shuffle=True, validation_data=(X_test1,y_test1), callbacks=[metrics])

Y_TEST = np.argmax(y_valid, axis=1)
y_pred = model.predict_classes(X_valid)

print("#"*50,"\n",classification_report(Y_TEST, y_pred))

Please share your class distribution for further understanding.

Answer 2

Not sure why the number of neurons in Dense layer is 285. If there are 47 categories, then the output neurons of Dense layer should be 47. Also, use a kernel initializer like he_normal instead of uniform. https://github.com/keras-team/keras-applications/blob/master/keras_applications/resnet50.py

model.add(Dense(47, activation='sigmoid'))
model.compile(optimizer='sgd', loss='binary_crossentropy', metrics=['accuracy'])

This is a multi-label classification example with 5 classes.

https://github.com/suraj-deshmukh/Keras-Multi-Label-Image-Classification