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Matthew Scarpino
Matthew Scarpino has been a programmer and engineer for more than 20 years. He has worked extensively with machine learning applications, especially those involving financial analysis, cognitive modeling, and image recognition. Matthew is a Google Certified Data Engineer and blogs about TensorFlow at tfblog.com.
Articles & Books From Matthew Scarpino
Article / Updated 06-09-2023
After you install TensorFlow, you're ready to start creating and executing applications. This section walks through the process of running an application that prints a simple message.
Exploring the example code
You can download this example code from the "Downloads" link on Wiley.com. The archive’s name is tf_dummies.
Cheat Sheet / Updated 03-02-2022
TensorFlow is Google’s premier framework for machine learning, and each new version brings a wide range of capabilities and features. After you’ve ascended the learning curve, you can write sophisticated machine-learning applications and execute them at high speed.But rising up the learning curve isn’t easy — with great power comes great complexity.
Article / Updated 06-19-2018
The filesystem on your computer stores data in files and organizes files using directories. Cloud Storage stores data in objects and collects objects inside buckets. Buckets have a lot in common with directories, but there's one major difference: Buckets can’t be nested. That is, you can’t organize buckets into a hierarchy in the way that you can organize directories.
Article / Updated 06-19-2018
Each piece of data in a Cloud Storage bucket is an object. A bucket may contain an unlimited number of objects, but each object must be 5 TB in size or less. Every object has data and metadata. When you upload a file to a bucket, the file’s content becomes the object’s data. An object’s metadata holds name-value pairs that describe the object.
Article / Updated 06-19-2018
The gsutil utility lets you create, access, and modify buckets and objects. For the most part, gsutil commands have the same names and purposes as common *nix commands.This table lists 13 of gsutil's commands. For a more thorough discussion, visit Google's documentation.gsutil Commands
Command
Description
mb [-c class] [-l location].
Article / Updated 06-19-2018
Before you upload data to Cloud Storage, you need to create one or more buckets. The command to know is mb:
gsutil mb [-c class] [-l location] [-p project_id] url…
The –c, –l, and –p flags are particularly important:
-c: The bucket's storage class: multi_regional, regional, nearline, and coldline. (Default: multi_regional or regional).
Article / Updated 06-19-2018
After you create a bucket with the gsutil utility on the Google Cloud Platform (GCP), you can upload files to it, thereby adding objects to the bucket. Similarly, you can download an object to your system as a file. Google makes these operations possible through the cp (copy) and mv (move) commands. Both commands transfer a source entity to a destination, but cp leaves the source entity in place while mv removes the source entity.
Article / Updated 06-19-2018
The ls and stat commands of the gsutil utility provide information about buckets and objects in Cloud Storage. The simplest usage of ls is gsutil ls, which lists all of the buckets associated with the current GCP project.One interesting feature of ls is that it recognizes the virtual hierarchy of objects. For example, suppose that gs://mybucket contains /mydir/a.
Article / Updated 06-19-2018
The cat command in the gsutil utility directs an object's text to standard output. For example, the following command prints the text contained in gs://mybucket/a.txt:
gsutil cat gs://mybucket/a.txt
Despite its name, you can’t concatenate objects with cat, but you can concatenate objects with compose. That is, the following command concatenates the content of a.
Article / Updated 06-19-2018
When the machine learning (ML) Engine executes your application, it passes arguments that provide information about the operating environment. The table lists the possible arguments.Machine Learning Arguments
Argument
Operation
--job-dir
Location of the application’s data
--train_batch_size
Batch size for training
--train_steps
Number of steps for each training epoch
--eval_batch_size
Batch size for evaluation
--eval_steps
Number of steps to run evaluation at each checkpoint
--eval_delay_secs
Time to wait before first evaluation
--min_eval_frequency
Minimum number of training steps between evaluations
--job-dir is particularly important because it tells the application where it should store its output files.
