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Showing posts from December, 2017

CNN Input shape for Deep Learning Frameworks

In Deep learning use cases images are represented as 3-D tensors (for colored images) and 2-D tensors (for gray scale images). Images mainly have three attributes: height, width, channels . Different deep learning frameworks expect these attributes to be specified in different order as per respective frameworks. In this post we are going to discuss the formats in which popular deep learning frameworks expect these attributes to be specified. Currently there are many deep learning frameworks in the market like:   1. Keras (for Python): Keras is a deep learning framework for Python. Keras is a wrapper around numerical computing libraries to provide user an easy interface to code Deep Learning networks. As a backend Keras could use:     a. Tensorflow     b. Theano 2. DL4J (for Java/Scala): DL4J is a deep learning framework written in Java. It could be used with Java as well scala programming languages.