This workflow reads a trained SavedModel for the MNIST dataset and executes it on test data.
This workflow trains a simple convolutional neural network (CNN) on the MNIST dataset via TensorFlow.
This workflow uses the TensorFlow Python bindings to create and train a multilayer perceptron using the Python API. The trained network is then used to predict the class of unseen data. For more information on the dataset see https://archive.ics.uci.edu/ml/datasets/Statlog+(Landsat+Satellite)
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.
This workflow shows how to train a simple neural network for text classification, in this case sentiment analysis. The used network learns a 128 dimensional word embedding followed by an LSTM.
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.
In this workflow we pre-process the image data, which we will use throughout the following example workflows.
Instead of creating our own network architecture as in the previous workflow "Train simple CNN", in this workflow we use the pre-trained network architecture VGG16.
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.