I am trying to implement the YOLO algorithm in Keras. What I have so far is the following network:
i = Input(shape=(image_height,image_width, image_channels)) rescaled = Rescaling(1./255)(i) x = Conv2D(16, (1, 1))(rescaled) x = Conv2D(32, (3, 3))(x) x = LeakyReLU(alpha=0.3)(x) x = MaxPooling2D(pool_size=(2, 2))(x) x = Conv2D(16, (3, 3))(x) x = Conv2D(32, (3, 3))(x) x = LeakyReLU(alpha=0.3)(x) x = MaxPooling2D(pool_size=(2, 2))(x) x = Flatten()(x) x = Dense(256, activation='sigmoid')(x) x = Dense(grid_width * grid_height * anchor_number * (5 + class_count))(x) x = Reshape((grid_width, sgrid_height, anchor_number, (5 + class_count)))(x)
Which supposed to output, for each grid cell and anchor box, a vector of $(p(c), b_x, b_y, b_h, b_w, class_0, class_1, ..., class_n))$
Some of the output vectors, namely $p(c)$, $b_x$ and $b_y$, are limited to be between 0 and 1, so they should pass through a sigmoid activation. The part of $class_0, class_1, ..., class_n$ is a classification, so it should pass through a SoftMax activation. So I need a way to specify what part of the output needs to use which activation.
TL;DR: How do I apply different activation functions to different parts of the network output?