在Python中将Keras CNN的NN输出映射到区间[0; 1]

Question

I attempt to train a CNN to binary classify images of the (maybe uncommon) shape of height=2 and width=1000 pixels. My first approach is a small and simple CNN coded as follows:

def cnn_model_01():
    model = Sequential()
    # Assembly of layers
    model.add(Conv2D(16, (2, 2), input_shape=(1, 2, 1000), activation='relu'))
    model.add(MaxPooling2D(pool_size=(1, 1)))
    model.add(Dropout(0.2))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dense(1, activation='sigmoid'))
    # Compilation of model
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

model = cnn_model_01()

# Fit the model
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5, batch_size=200, verbose=2)

The accuracy and prediction of the NN results in a value which reflects simply the distribution of values in the sample. Typical training output is

13s - loss: 0.7772 - acc: 0.5680 - val_loss: 0.6657 - val_acc: 0.6048
Epoch 2/5
15s - loss: 0.6654 - acc: 0.5952 - val_loss: 0.6552 - val_acc: 0.6048
Epoch 3/5
15s - loss: 0.6514 - acc: 0.5952 - val_loss: 0.6396 - val_acc: 0.6048
Epoch 4/5
15s - loss: 0.6294 - acc: 0.5952 - val_loss: 0.6100 - val_acc: 0.6048
Epoch 5/5
13s - loss: 0.5933 - acc: 0.6116 - val_loss: 0.5660 - val_acc: 0.6052

The reason for this is that the NN assigns all input samples to one class. So, in approximately two thirds it is correct by chance in the case of a sample distributed in exactly this way.

In order to fix the problem and get the NN to produce better results I've inspected the output and encountered that the interval or domain of these values is relatively small, eg between [0.55;0.62]. I've tried to map resp. resize this interval to [0;1]. As a result a got a really good accuracy of ~99%. I've done this mapping "by hand": subtract the minimum value of the array from each value and divide it by the difference of the maximum and minimum.

Can I implement this mapping in Keras? Is there a layer with this functionality?

Or did I do something completely wrong/not advisable with the layers, which leads to this narrow interval of the output?

Answer 1

I'm not sure I entirely understand what you want to achieve. But I have three ideas out of which one or two may help you.

1) Add a Dense(2) layer before the output layer and change the activation of the output layer to softmax . That way you'd have the previous layer classify the image as class 1 or class 2. The last Dense(1) layer would then "merge" that information into a single value 0 or 1 as output.

2) I assume you could pick a threshold, eg 0.5 and simply compare the probability-based output of your NN and so something like result = output > 0.5 . This could also be done inside a Lambda layer, ie model.add(Lambda(lambda x: 1 if x > 0.5 else 0))

3) When predicting, you can use predict_class instead of predict and get 0 or 1 as result instead of probabilities. This resembles my previous suggestion 2.

I hope one of the suggestions matches your task.