TensorFlow.js Interview Questions

In

**TensorFlow**

, three main components are used to deploy a Lite 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, **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.

**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:
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 -c 'import tensor flow as tf; print(tf.__version__)'`

`python3 -c 'import tensor flow as tf; print(tf.__version__)'`

CNN | RNN |
---|---|

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 :

A person can report about any security issue directly to

**security@tensorflow.org**

. 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.

`tf.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.

**.run()**

while you are using the session and it is being executed.**.eval()**

. The full syntax of **.eval()**

is`tf.get_default_session().run(values) â€‹`

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)
```