Introduction: TensorFlow is a machine learning framework for JavaScript applications. It helps us build models that can be directly used in the browser or in the node.js server. We use this framework for building an application that can recognize drawings of different types using a sequential model
Description: The model chosen is a Recurrent Neural Network model. This is used for finding groups via paths in sequences. A Sequence Clustering algorithm is like a clustering algorithm mentioned above but instead of finding groups based on similar attributes, it finds groups based on similar paths in a sequence. A sequence is a series of events. For example, a series of web clicks by a user is a sequence. It can be also be compared to the IDs of any sortable data maintained in a separate table. Usually, there is support for a sequence column. Support is a metric based on probabilities. The sequence data has a nested table that contains a sequence ID which can be any sortable data type.
The JavaScript application loads the model before using it for prediction. When enough training data images have been processed, the model learns the characteristics of the drawings which results in their labels. Then as it runs through the test data set, it can predict the label of the drawing using the model. TensorFlow has a library called Keras which can help author the model and deploy it to an environment such as Colab where the model can be trained on a GPU. Once the training is done, the model can be loaded and run anywhere else including a browser. The power of TensorFlow is in its ability to load the model and make predictions in the browser itself.
The labeling of drawings starts with a sample of say a hundred classes. The data for each class is available on Google Cloud as numpy arrays with several images numbering say N, for that class. The dataset is pre-processed for training where it is converted to batches and outputs the probabilities.
As with any ML learning example, the data is split into 70% training set and 30% test set. There is no order to the data and the split is taken over a random set.
TensorFlow makes it easy to construct this model using the Keras API. It can only present the output after the model is trained. In this case, the model must be run after the training data has labels assigned. This might be done by hand. The model works better with fewer parameters. It might contain 3 convolutional layers and 2 dense layers. Keras.Sequential() instantiates the model. The pooling size is specified for each of the convolutional layers and they are stacked up on the model. The model is trained using the tf.train.AdamOptimizer() and compiled with a loss function, optimizer just created, and a metric such as top k in terms of categorical accuracy. The summary of the model can be printed for viewing the model. With a set of epochs and batches, the model can be trained.
With the model and training/test sets defined, it is now as easy to evaluate the model and run the inference. The model can also be saved and restored. It is executed faster when there is GPU added to the computing.
When the model is trained, it can be done in batches of predefined size. The number of passes of the entire training dataset called epochs can also be set up front. A batch size of 256 and the number of steps as 5 could be used. These are called model tuning parameters. Every model has a speed, Mean Average Precision and output. The higher the precision, the lower the speed. It is helpful to visualize the training with the help of a high chart that updates the chart with the loss after each epoch. Usually there will be a downward trend in the loss which is referred to as the model is converging.
When the model is trained, it might take a lot of time say about 4 hours. When the test data has been evaluated, the model’s efficiency can be predicted using precision and recall, terms that are used to refer to positive inferences by the model and those that were indeed positive within those inferences.
Conclusion: Tensorflow.js is becoming a standard for implementing machine learning models. Its usage is simple, but the choice of model and the preparation of data takes significantly more time than setting it up, evaluating, and using it.
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