How to add a regression head after the fully connected layer in convolutional network using Tensorflow?

Question

I am new to deep learning and Tensorflow and have to learn this topic due to a project I am currently working on. I am using convolutional network to detect and find the location of a single object in the image. I am using the method introduced in Standford CS231n class . The lecturer mentioned about connecting a regression head after the fully connected layer in the network to find the location of the object. I know there is DNNRegressor in Tensorflow. Should I use this as the regression head?

Before I modified Tensorflow's tutorial on using ConvNet to recognize handwritten digit for my case. I am not too sure how can I add the regression head to that program so that it can also find a bounding box for the object.

I just had the chance to touch machine learning and deep learning this week, apology if I asked a really silly question, but I really need to find a solution to my problem. Thank you very much.

Answer 1

First of all, in order to train a neural network for object localization task, you have to have a data set with localized objects. This answers your question whether you can work with MNIST data set or not. MNIST contains just a class label for each image, so you need to get another data set . Justin also talks about popular data sets at around 37:34.

The way object localization works is by learning to output 4 values per image, instead of class distribution. This four-valued vector is compared to the ground truth four-valued vector and the loss function is usually L1 or L2 norm of their difference. So in code, regression head is an ordinary regression layer, which can be implemented in tensorflow by a simple tf.reduce_mean call.

A small yet complete example that performs object localization can be found here . Also recommend to take a look at this question .

Answer 2

I was looking for this problem as well and I found the following part in the document .

Dense (fully connected) layers, which perform classification on the features extracted by the convolutional layers and downsampled by the pooling layers. In a dense layer, every node in the layer is connected to every node in the preceding layer.

Based on this quote, it seems you can't do regression but classification .

EDIT: After some research, I found out a way to use a fully-connected layer in tensorflow .

import tensorflow.contrib.slim as slim

#create your network **net**. 

#In the last step, you should use 
y_prime = slim.fully_connected(net, 1, activation_fn=None, reuse=reuse)

loss = tf.reduce_mean(tf.square(y_prime - y)) #L2 norm
lr = tf.placeholder(tf.float32)
opt = tf.train.AdamOptimizer(learning_rate=lr).minimize(loss)

You can add more fully connected layers before the last step which can have more nodes.