Finally, the function returns the loss variable, which will be minimized by a specified optimizer. The “out” variable represents the logits or raw outputs which will be used for the loss function, and the variable “z” represents the argmax which will be used for predictions in the Spark Transformer. The graph is a simple, fully connected network with 256 neurons in each hidden layer. ![]() The small_model function highlighted below encapsulates the TensorFlow graph. def small_model(): x = tf.placeholder(tf.float32, shape=, name='x') y = tf.placeholder(tf.float32, shape=, name='y') layer1 = tf.nse(x, 256, activation=tf.nn.relu) layer2 = tf.nse(layer1, 256, activation=tf.nn.relu) out = tf.nse(layer2, 10) z = tf.argmax(out, 1, name='out') loss = tf.losses.softmax_cross_entropy(y, out) return loss To fully understand what is going on in the example, it’s worth going through the SparkFlow sections. While TensorFlow, a high performance numerical computation library commonly used for deep learning, is great for training various neural network architectures, it lacks feature engineering support for pipelines on large datasets compared to Apache Spark.Īlthough other open-source libraries exist to train TensorFlow models on Apache Spark, very few take advantage of SparkML’s biggest machine learning strength, which is integrating deep learning models with pipelines. At LifeOmic, having the right tools for feature engineering allows us to automate data processing steps on the raw data before the data is sent to the model for training or predictions. Pipelines greatly simplify the process in which raw data is cleaned, transformed and prepared to the machine learning model to execute predictions. The feature engineering pipeline is often an overlooked part of training machine learning models. However, Apache Spark currently has no out of the box support for deep neural network architectures such as Convolutional Neural Networks for images, Recurrent Neural Networks for natural language processing, and more. Apache Spark, a unified analytics engine for large-scale data processing, and SparkML, the machine learning library built on Apache Spark, provide the architecture to orchestrate complex pipelines on large datasets. Having a strategy to efficiently train deep learning models can be a challenge. The goal of machine learning at LifeOmic is to enable researchers to find hidden insights on the combination of genomic and patient data that couldn’t be found using basic statistical techniques. This includes supporting the traditional techniques such as supervised learning (predicting a label given data) and unsupervised learning (organizing or explaining the data). Credit: zmeel.ĭue to the massive dimension size of these datasets, it is often important to extract the latent variables (or inferred features) through deep learning to reduce the dimension size for further modeling. ![]() DNA sequencing produces large datasets that can benefit from big data analytical techniques including machine learning.
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