Edit MNIST SavedModel

This workflow shows how to edit a TensorFlow model using the TensorFlow Python API by adding an additional output to a model.
The loaded model does classification on MNIST but only outputs the probabilities for each class. We edit the model such that it outputs the class as well.


Uses a character level encoder-decoder network of LSTMs. The encoder network reads the input sentence character by character and summarizes the sentence in its state. This state is then used as initial state of the decoder network to produce the translated sentence one character at a time. During prediction, the decoder also recieves its previous output as input to the next time. For training we use a technique called "teacher forcing" i.e.


This is the deployment workflow of the encoder-decoder neural architecture for the Neural machine Translation model. It includes reading the encoder and decoder networks from tensorFlow files, applying them to English sentences and create the German character sequence as output.

Train simple CNN

In this workflow we create a simple Convolutional Neural Network using the DL Python Network Creator. We train this network on our image data using the DL Python Network Learner and finally score it using the DL Python Network Executor. The DL Python Network Learner and Executor can be used to write custom training and execution code using Python.

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