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TensorFlow.js Interview Questions
NumPy supports multi-dimensional arrays, matrices. Hence it deals with the data in the form of arrays.

Matplotlib handles all the graphical representation in TensorFlow. It supports the MATLAB interface.
DeepSpeech is an open-source engine used to convert Speech into Text. It uses a model which is trained by machine learning techniques. It is based on Baidu's Deep Speech research paper. It uses Google's TensorFlow to make the implementation easier.
 
We can list the command line options through deep Speech, and the syntax for that is given below:
./deepspeech.py  
We can create tensors such as numpy arrays and lists with the help of Python objects. We can easily perform it using tf.convert_to_tensor() operation.
Tensorflow is a high-level library. A variable is a state or value that can be modified by performing operations on it. In TensorFlow variables are created using the Variable() constructor.
 
The Variable() constructor expects an initial value for the variable, which can be any kind or shape of Tensor. The type and form of the variable are defined by its initial value. The shape and the variables are fixed once they are created. let’s look at a few examples of how to create variables in TensorFlow.
 
Syntax : 
tf.Variable(initial_value=None, trainable=None, validate_shape=True, 
caching_device=None, name=None, variable_def=None, dtype=None, import_scope=None,
constraint=None,synchronization=tf.VariableSynchronization.AUTO, 
aggregation=tf.compat.v1.VariableAggregation.NONE, shape=None)​

 

Parameters :
 
initial_value : by default None. The initial value for the Variable is a Tensor, or a Python object convertible to a Tensor.

trainable : by default None.  If True, GradientTapes will keep an eye on this variable’s usage.

validate_shape : by default True. Allows the variable to be initialised with an unknown shape value if False. The shape of initial value must be known if True, which is the default.

name : by default None. The variable’s optional name. Defaults to ‘Variable’ and is automatically uniquified.

variable_def : by default None.

dtype : by default None. If set, initial_value will be converted to the given type. If None, either the datatype will be kept (if initial_value is a Tensor), or convert_to_tensor will decide.

shape : by default None. if None the shape of initial_value will be used. if any shape is specified, the variable will be assigned with that particular shape.
tf.Variable() constructor is used to create a variable in TensorFlow.
tensor = tf.Variable([3,4])
Output :
<tf.Variable ‘Variable:0’ shape=(2,) dtype=int32, numpy=array([3, 4], dtype=int32)>
 
Dimension, size, shape, and dtype of a TensorFlow variable :
# import packages
import tensorflow as tf
 
# create variable
tensor1 = tf.Variable([3, 4])
 
# The shape of the variable
print("The shape of the variable: ",
      tensor1.shape)
 
# The number of dimensions in the variable
print("The number of dimensions in the variable:",
      tf.rank(tensor1).numpy())
 
# The size of the variable
print("The size of the tensorflow variable:",
      tf.size(tensor1).numpy())
 
# checking the datatype of the variable
print("The datatype of the tensorflow variable is:",
      tensor1.dtype)
Output :
The shape of the variable:  (2,)
The number of dimensions in the variable: 1
The size of the tensorflow variable: 2
The datatype of the tensorflow variable is: <dtype: 'int32'>
We use the assign() method to modify the variable. It is more like indexing and then using the assign() method. There are more methods to assign or modify the variable such as Variable.assign_add() and Variable.assign_sub().
 
Example :
 
assign() : It’s used to update or add a new value.
 
Syntax : assign(value, use_locking=False, name=None, read_value=True)
 
parameters :
 
* value : The new value for this variable.
* use_locking :  locking during assignment if “true”.

import tensorflow as tf
 
tensor1 = tf.Variable([3, 4])
tensor1[1].assign(5)
tensor1
Output :
<tf.Variable ‘Variable:0’ shape=(2,) dtype=int32, numpy=array([3, 5], dtype=int32)>


Example :

Syntax : assign_add(delta, use_locking=False, name=None, read_value=True)

parameters :

* delta :
The value to be added to the variable(Tensor).
* use_locking : During the operation, if True, utilise locking.
* name :  name of the operation.
* read_value : If True, anything that evaluates to the modified value of the variable will be returned; if False, the assign op will be returned.

# import packages
import tensorflow as tf

# create variable
tensor1 = tf.Variable([3, 4])

# using assign_add() function
tensor1.assign_add([1, 1])

tensor1

Output :

<tf.Variable ‘Variable:0’ shape=(2,) dtype=int32, numpy=array([4, 5], dtype=int32)>

 

Example :

Syntax : assign_sub(  delta, use_locking=False, name=None, read_value=True)

parameters :

* delta : The value to be subtracted from the variable
* use_locking : During the operation, if True, utilise locking.
* name : name of the operation.
* read_value : If True, anything that evaluates to the modified value of the variable will be returned; if False, the assign op will be returned.

# import packages
import tensorflow as tf

# create variable
tensor1 = tf.Variable([3, 4])

# using assign_sub() function
tensor1.assign_sub([1, 1])

tensor1

Output :

<tf.Variable ‘Variable:0’ shape=(2,) dtype=int32, numpy=array([2, 3], dtype=int32)>
The tf.variable and tf.placeholder both are almost similar to each other, but there are some differences as following:

tf.variable tf.placeholder
  • It defines variable values which are modified with time.
  • It defines specific input data that does not change with time.
  • It requires an initial value at the time of definition.
  • It does not require an initial value at the time of definition.
Scaler Dashboard visualizes scaler statistics that vary over time. It uses a simple API for performing such visualizations. For example, We might want to examine the model's loss or learning rate.
 
We can compare multiple runs, and the data is established by tag.
The Histogram Dashboard is used to display how the statistical distribution of a Tensor varies overtime. It helps to visualize the data recorded via tf.summary.histogram. Each chart displays the temporal "slices" of data, where each slice is a histogram of the tensor at a given step. It is arranged with the oldest timestep in the back, and the most recent timestep in front.
 
If a Histogram mode is changed from "offset" to "overlay", the perspective will rotate. As a result, every histogram slice is rendered as a line and overlaid with one another.
The audio dashboard serves to primarily help users embed playable widgets stored in files. tf.summary.audio is used for the storage of these files, and the tagging system is used to embed the latest audio based on the storage policies.