CNN Image Recognition with Regression Output on Tensorflow

Question

I want to predict the estimated wait time based on images using a CNN. So I would imagine that this would use a CNN to output a regression type output using a loss function of RMSE which is what I am using right now, but it is not working properly.

Can someone point out examples that use CNN image recognition to output a scalar/regression output (instead of a class output) similar to wait time so that I can use their techniques to get this to work because I haven't been able to find a suitable example.

All of the CNN examples that I found are for the MSINT data and distinguishing between cats and dogs which output a class output, not a number/scalar output of wait time.

Can someone give me an example using tensorflow of a CNN giving a scalar or regression output based on image recognition.

Thanks so much! I am honestly super stuck and am getting no progress and it has been over two weeks working on this same problem.

Answer 1

Check out the Udacity self-driving-car models which take an input image from a dash cam and predict a steering angle (i.e. continuous scalar) to stay on the road...usually using a regression output after one or more fully connected layers on top of the CNN layers.

https://github.com/udacity/self-driving-car/tree/master/steering-models/community-models

Here is a typical model:

https://github.com/udacity/self-driving-car/tree/master/steering-models/community-models/autumn

...it uses tf.atan() or you can use tf.tanh() or just linear to get your final output y.

Use MSE for your loss function.

Here is another example in keras...

model = models.Sequential()
model.add(convolutional.Convolution2D(16, 3, 3, input_shape=(32, 128, 3), activation='relu'))
model.add(pooling.MaxPooling2D(pool_size=(2, 2)))
model.add(convolutional.Convolution2D(32, 3, 3, activation='relu'))
model.add(pooling.MaxPooling2D(pool_size=(2, 2)))
model.add(convolutional.Convolution2D(64, 3, 3, activation='relu'))
model.add(pooling.MaxPooling2D(pool_size=(2, 2)))
model.add(core.Flatten())
model.add(core.Dense(500, activation='relu'))
model.add(core.Dropout(.5))
model.add(core.Dense(100, activation='relu'))
model.add(core.Dropout(.25))
model.add(core.Dense(20, activation='relu'))
model.add(core.Dense(1))
model.compile(optimizer=optimizers.Adam(lr=1e-04), loss='mean_squared_error')

They key difference from the MNIST examples is that instead of funneling down to a N-dim vector of logits into softmax w/ cross entropy loss take it down to a 1-dim vector w/ MSE loss.

Answer 2

The key is to have NO activation function in your last Fully Connected (output) layer. Note that you must have at least 1 FC layer beforehand.