Author: Awais Farooq
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Phalcon Query Language:
The Phalcon Framework Query Language, or PHQL, is a high-level object-oriented dialect originally written in C language. In fact, using SQlite technology in the C language library makes database management easier for programmers. In addition, this technology creates less memory footprint (Low Memory Footprint) and therefore ensures database security.
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Use of low resources:
As you run the PHP programming language, the Phalcon framework runs without any lag, so as the number of users on a site increases, the Phalcon framework consumes less server space. In addition, thanks to the C programming language, this framework, unlike PHP programming language, works as a compile. Of course, until there is no…
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Equipped with ORM:
The data of some applications in the database is constantly changing, while in others this data is rarely changing. So the Phalcon framework is a better option for programmers to read and write data from the database as quickly as possible. In addition, the compatibility of this framework with C programming language and cooperation through…
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Benefits of Phalcon Framework
Function The main motivation and goal of designing the Phalcon framework is to speed up code execution, reduce resource usage, and better process large volumes of users. In addition, the most important advantage of the Phalcon framework over its peers is the efficient use of memory, which in principle makes the language more efficient and…
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What is Phalcon?
Phalcon Framework is one of the top PHP language frameworks that is very well integrated thanks to the MVC architecture. The alpha version of the framework came in 2012 and has always been open-source for developers. The core of this framework is based on the C and Zephire programming languages, plus it is the first framework to…
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How can I use stateful RNNs?
Making a RNN stateful means that the states for the samples of each batch will be reused as initial states for the samples in the next batch. When using stateful RNNs, it is therefore assumed that: To use statefulness in RNNs, you need to: To reset the states accumulated: Example: Note that the methods predict, fit, train_on_batch, etc.…
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Modeling-related questions
How can I obtain the output of an intermediate layer (feature extraction)? In the Functional API and Sequential API, if a layer has been called exactly once, you can retrieve its output via layer.output and its input via layer.input. This enables you do quickly instantiate feature-extraction models, like this one: Naturally, this is not possible with models that…
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What’s the difference between Model methods predict() and __call__()?
Let’s answer with an extract from Deep Learning with Python, Second Edition: Both y = model.predict(x) and y = model(x) (where x is an array of input data) mean “run the model on x and retrieve the output y.” Yet they aren’t exactly the same thing. predict() loops over the data in batches (in fact, you can specify the batch size via predict(x, batch_size=64)), and it extracts…
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What if I need to customize what fit() does?
You have two options: 1) Subclass the Model class and override the train_step (and test_step) methods This is a better option if you want to use custom update rules but still want to leverage the functionality provided by fit(), such as callbacks, efficient step fusing, etc. Note that this pattern does not prevent you from building models with the Functional API,…
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What’s the recommended way to monitor my metrics when training with fit()?
Loss values and metric values are reported via the default progress bar displayed by calls to fit(). However, staring at changing ascii numbers in a console is not an optimal metric-monitoring experience. We recommend the use of TensorBoard, which will display nice-looking graphs of your training and validation metrics, regularly updated during training, which you can access…