  We present visual-like morphologies over 16 photometric bands, from
ultra-violet to near infrared, for 8,412 galaxies in the Cluster Lensing And
Supernova survey with Hubble (CLASH) obtained by a convolutional neural network
(CNN) model. Our model follows the CANDELS main morphological classification
scheme, obtaining the probability for each galaxy at each CLASH band of being
spheroid, disk, irregular, point source, or unclassifiable. Our catalog
contains morphologies for each galaxy with Hmag < 24.5 in every filter where
the galaxy is observed. We trained an initial CNN model using approximately
7,500 expert eyeball labels from The Cosmic Assembly Near-IR Deep Extragalactic
Legacy Survey (CANDELS). We created eyeball labels for 100 randomly selected
galaxies per each of the 16-filters set of CLASH (1,600 galaxy images in
total), where each image was classified by at least five of us. We use these
labels to fine-tune the network in order to accurately predict labels for the
CLASH data and to evaluate the performance of our model. We achieve a
root-mean-square error of 0.0991 on the test set. We show that our proposed
fine-tuning technique reduces the number of labeled images needed for training,
as compared to directly training over the CLASH data, and achieves a better
performance. This approach is very useful to minimize eyeball labeling efforts
when classifying unlabeled data from new surveys. This will become particularly
useful for massive datasets such as the ones coming from near future surveys
such as EUCLID or the LSST. Our catalog consists of prediction of probabilities
for each galaxy by morphology in their different bands and is made publicly
available at http://www.inf.udec.cl/~guille/data/Deep-CLASH.csv.
