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TensorFlow.js Interview Questions
In TensorFlow, three main components are used to deploy a Lite model :
Java API : Used as a wrapper around the C++ API for Android

C++ API : Used to load the TensorFlow Lite model and call the interpreter

Interpreter : Used to handle kernel loading and the execution of the model
The Image Dashboard is used to display png files that were saved via a tf.summary.image. The dashboard is configured in such a way so that each row corresponds to a different tag, and each column corresponds to a run. The image dashboard also supports arbitrary pngs which can be used to embed custom visualizations (e.g.,matplotlib scatterplots) into TensorBoard. This dashboard always shows the latest image for each tag.
If you do not have TensorFlow installed then, TensorBoard 1.14+ can be run but with a reduced feature set. The primary limitation is that as of TensorFlow 1.14, only the following plugins are supported: scalars, custom scalars, image, audio, graph, projector (partial), distributions, histograms, text, PR curves, mesh. Also, there is no support for log directories on Google Cloud Storage.
Activation functions are functions applied to the output side of a neural network that serves to be the input of the next layer. It forms a very important part of neural networks as it provides nonlinearity that sets apart a neural network from logistic regression.
There are two commands depending on the Python version :
Python 2 :
python -c 'import tensor flow as tf; print(tf.__version__)'
Python 3 :
python3 -c 'import tensor flow as tf; print(tf.__version__)'
Convolutional Neural Network Recurrent Neural Network
Known as the feed-forward model For the series of data
Memoryless model Requires memory to store previous inputs
Cannot handle sequential data Can handle Sequential data
Used for Image recognition Used for Text recognition
Can handle fixed length of input/ output  Can handle arbitrary lengths of input/ output 
Feature compatibility is more Feature compatibility is less 
Handles permanent data Handles temporary data
TensorFlow supports the following Dashboards :
* Scalar Dashboard
* Histogram Dashboard
* Image Dashboard
* Graph Explorer
* Audio Dashboard
* Text Dashboard
* Distributer Dashboard
* Projector
A person can report about any security issue directly to The report to this email is delivered to the security team at TensorFlow. The emails are then acknowledged within 24 hours, and detailed response is provided within a week along with the next steps.
In TensorFlow, we create graphs and provide values to that graph. The graph itself processes all the hardwork and generates the output based on the configuration that we have applied in the graph. Now, when we provide values to the graph, then first, we need to create a TensorFlow session.
Once the session is initialized, then we are supposed to use that session. It is necessary because all the variables and settings are now part of the session.
So, there are two possible ways that we can apply to pass external values to the graph so that the graph accepts them.
* The first one is to call the .run() while you are using the session and it is being executed.
* Another way to this is to use .eval(). The full syntax of .eval() is

tf.get_default_session().run(values)  ​

At the place of values.eval(), we can put tf.get_default_session().run(values) and It will provide the same behavior. Here, eval is using the default session and then executing run().
The weighted standard error is a standard metric that is used to compute the coefficient of determination when working with a linear regression model.
It provides an easy way to evaluate the model and can be used as shown below :
# Used along with TFLearn estimators
weighted_r2 = WeightedR2()
regression = regression(net, metric=weighted_r2)