Free TensorFlow Practice Test Online
Participation in the TensorFlow certification examination is required to become a TensorFlow developer. This TensorFlow development certificate is a foundational credential for students, developers, and data scientists who want to demonstrate practical machine learning skills by building and training models with TensorFlow. The TensorFlow Developer certificate can also be obtained via the internet. The TensorFlow Developer Certification is required to demonstrate your ability to utilize TensorFlow effectively.
TensorFlow dict to Tensor
TensorFlow is a Google-developed open-source Python toolkit for developing Machine Learning models and deep learning neural networks. To convert a value to a Tensor, use the convert to tensor() function.
tensorflow.convert_to_tensor( value, dtype, dtype_hint, name )
- value: It’s the number that must be transformed into a Tensor.
- dtype(optional): It specifies the output Tensor’s type.
- dtype_hint(optional): When dtype is None, it is used. While converting to a tensor, a caller may not have a dtype in mind. As a result, you can utilize dtype hint as a soft preference. This parameter has no effect if the conversion to dtype hint is impossible.
- name(optional): It specifies the operation’s name.
Model Parallelism TensorFlow
The technique of spreading a model across several devices or nodes (such as GPU-equipped instances) and constructing an efficient pipeline to train the model across these devices to maximize GPU utilization is known as model parallelism. Model parallelism should be used when training on a single GPU is not practicable due to memory constraints. This method divides the model into many GPUs and runs each portion on a separate accelerator. Huge models can be divided up and made to fit in memory. In the past, putting this strategy into practice was quite difficult. Fair model splitting, accurate GPU assignment, and efficient execution are difficult to achieve.
Tensors can be reshaped using Tensorflow’s built-in reshape() function to meet input requirements and preserve consistency. Using tf.reshape(), we can change the shape and rank of a tensor without affecting the constituent elements or their order of appearance. Regardless of the size and rank of the original tensor, this makes the function incredibly dynamic and quick to implement. This reshapes capability allows Data Scientists to train AI neural networks in various ways, allowing them to perform complex search operations, audio/video file matching, image processing, and natural language classification properly and quickly. It also enables the creation of a wide range of new data models to develop robust and dependable AI solutions.
tensorflow.reshape(tensor, shape, name=None)
- tensor: the to-be-reshaped tensor
- shape: the output tensor’s shape
- name: name of the operation (optional)
The suggested way to describe the shared, permanent state that your program manipulates is with a TensorFlow variable. The tf.Variable class is used to create and track variables. A tf.Variable is a tensor that can alter its value by conducting operations on it. This tensor’s values can be read and modified using specific operators. Tf.Variable is used by higher-level libraries like tf.keras to hold model parameters. Provide an initial value to create a variable. The initialization value’s dtype will be the same as the tf.Variable.
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)
- initial_value: as the default None. The Variable’s initial value is a Tensor or a Python object that can be converted to a Tensor.
- trainable: None is the default value. If True, GradientTapes will monitor the use of this variable.
- validate_shape: True by default. If False, the variable can be started with an unknown shape value. If True, which is the default, the shape of the initial value must be known.
- name: as the default None. The name of the variable is optional. ‘Variable’ is the default value, and it is automatically uniquified.
- variable_def: None is the default value.
- dtype: None is the default value. The initial value will be transformed to the specified type if the initial value is set. If None is specified, either the datatype (if the initial value is a Tensor) or convert to tensor will be used to determine the datatype.
- shape: None is the default value. If None, the initial value’s shape will be utilized. If a shape is supplied, that form will be used to assign the variable.
Tensors can be defined in Einsum by specifying their element-wise processing. Equation, a shorthand form based on Einstein’s summation, is used to define this computation.
tf.einsum (equation, tensors)
- equation: It’s the initial input tensor element, and it’s a string that describes the contraction in the same manner as numpy.einsum.
- . . . tensors: It’s a second input tensor element in which the input(s) are utilized to contract (each a Tensor), and the shapes should match the equation.
- It does not support tensors with two inputs.
- For every given input tensor, it does not accept duplicate axes.
- The… notation isn’t recognized.
To split a tf.tensors into sub tensors, use the tf.split() function.
tf.split (x, numOrSizeSplits, axis?)
- x: The tensor to split as input.
- numOrSizeSplits: It can either be a number indicating the number of splits or an array containing sizes for each output tensor.
- axis: It is a dimension’s axis along which to split.
The Gaussian Error Linear Unit, or GELU, is a function that activates a function. The Gaussian error linear unit (GELU) calculates x * P(X <= x), where P(X) ~ N(0, 1). In contrast to ReLU, the (GELU) nonlinearity weights inputs based on their value rather than their sign.
For the LSTM Cell class, the layers.lstmCell() function is utilized. It’s not the same as the RNN subclass.
Parameters: (The args object in the function has the following parameters.)
- recurrentactivation: It is used to make recurring steps active.
unitForgetBias: It’s a boolean used to enable the forget gate during initialization.
implementation: It’s either an integer or a string that specifies the implementation modes. MODE 1 organizes activities into a higher number of smaller dot products and additions. MODE 2 is used to consolidate the activities into fewer, larger ones.
units: It’s a number, whether an integer or the output space’s dimensions.
activation: It is used to perform the function.
useBias: The use of a bias vector by the layer is a boolean.
kernelInitializer: It’s used to turn the inputs into a linear format.
recurrentInitializer: It translates the recurrent state into a linear form.
biasInitializer: It’s what the bias vector is made of.
kernelRegularizer: It’s a string used by the Kernel Weights Matrix Regularizer function.
recurrentRegularizer: It’s a string that gets applied to the recurrent kernel weights matrix by the Regularizer function.
biasRegularizer: It’s a string that’s used to apply the Regularizer function to the bias vector.
kernelConstraint: It’s a string used by the kernel weights matrix’s Constraint function.
recurrentConstraint: It’s a string that the Constraint function uses to constrain the recurrentKernel weights matrix.
iasConstraint: It’s a string that’s used to apply the Constraint function to the bias vector.
dropout: It’s a number that ranges from 0 to 1. A fraction of the units should be dropped for the linear transformation of the inputs.
recurrentDropout: It’s a number that ranges from 0 to 1. The fraction of units decreases when the recurrent state is transformed linearly.
inputShape: It’s a number utilized to make an input layer that goes before this one.
batchInputShape: It’s a number used to make an input layer that’ll be placed before this one.
batchSize: It’s a number that’s utilized to make the batchInputShape.
dtype: Only input layers are affected by this option.
name: It’s a string used by the layer.
trainable: It’s a boolean value that indicates whether or not the weights of this layer can be updated by fit.
weights: It’s the layer’s initial weight values.
inputDType: It’s used to support legacy systems. It can’t be used for new codes.
TensorFlow includes a function called tf.transpose(). The input tensor is transposed using this function.
tf.transpose(input_tensor, perm, conjugate)
- input_tensor: The tensor is to be transposed, as the name implies.
- Type: Tensor
- perm: This option defines the permutation used to transpose the input_tensor.
- Type: Vector
- conjugate: If the input_tensor is type complex, this argument is set to True.
- Type: Boolean
How to check TensorFlow Version
A Python IDE or code editor is the simplest way to check the TensorFlow version in Python. Basic information can be displayed using the library’s built-in methods. Enter the following in Python to print the TensorFlow version:
import tensorflow as tf
The python command in the CLI can display the TensorFlow version by invoking Python. The -c option is used to run code. Use the python<version> command if your workstation has multiple Python instances installed.
The Jupyter Notebook executes commands and Python code in real time. In Jupyter Notebooks, there are two ways to check the TensorFlow version.
import tensorflow as tf
!pip show tensorflow
The Conv2D class in Tensorflow and Keras allows us to apply the convolution function on two-dimensional inputs. Keras Conv2D is a two-dimensional convolution layer that generates a tensor of outputs by winding a convolution kernel with the layer’s input. A bias vector is constructed and appended to the outputs if the used bias is True. Finally, if activation is None, the outputs are activated as well.
TensorFlow decode image
TensorFlow includes the tf.io and tf.image modules for reading and processing images. This function determines whether an image is a BMP, GIF, JPEG, or PNG. It also uses the appropriate procedure to convert the input bytes string into a Tensor of type dtype. It has decode bmp, decode gif, decode jpeg, and decode png functions.
The tensor of Boolean values for the two provided tensor values is returned by the tf.equal() function, which returns true if the first tensor value is equal to the second tensor value and false otherwise. Broadcasting is supported.
a: The first tensor in the input.
b: The tensor for the second input. Its values should be of the same data type as tensor “a.”
TensorFlow Question and Answer
TensorFlow is an open-source end-to-end platform that includes a library for various machine learning applications, and Keras is a high-level neural network library that operates on top of TensorFlow. Both offer high-level APIs for quickly creating and training models, while Keras is more user-friendly because of its Python integration.
You must first ensure that CUDA is installed on your PC before installing TensorFlow (this will only work if you have an NVIDIA GPU). You’ll also require cuDNN. After you’ve installed them, use pip install tensorflow==1.4 to install it.
The simplest way to install TensorFlow is to use one of the official releases available on the Python Package Index (PyPI). TensorFlow is available on three distinct processing platforms, with the key difference being the training speed of your neural network.
TensorFlow 2.8 or later is required for Python 3.10 support. TensorFlow 2.5 or later is required for Python 3.9 support.
TensorFlow is straightforward to install with Anaconda, allowing you to use it in your data science, machine learning, and artificial intelligence workflows.
- Download and install Anaconda or Miniconda, whichever is smaller.
- Open an Anaconda Command Prompt from the Start menu on Windows. Open a terminal window on macOS or Linux. On macOS or Linux, use the default bash shell.
- Give your TensorFlow environment a name, such as “tf.”
- Install the latest CPU-only TensorFlow release, which is recommended for beginners:
To install GPU
TensorFlow on Linux or Windows, follow these instructions: TensorFlow is now up and running and ready to use.
TensorFlow is a Google-developed open-source library for deep learning applications. Traditional machine learning is also supported. TensorFlow was created with big numerical computations, rather than deep learning.
A placeholder is a variable to which data will be assigned at a later time. It enables us to generate operations and construct a computation graph without the requirement for data. We then inject data into the graph through these placeholders in TensorFlow terminology.
Python versions 3.4 and up are recommended for TensorFlow installation.
A tensor is a higher-dimensional generalization of vectors and matrices. TensorFlow represents tensors as n-dimensional arrays of base datatypes. The tf$Tensor object is the main object you manipulate and pass around while constructing a TensorFlow program.
TensorFlow Lite is a suite of tools that allows developers to run their models on mobile, embedded, and edge devices, enabling on-device machine learning.
TensorFlow GPU support necessitates a number of different drivers and libraries.
TensorFlow is CUDA® 11.2 compatible.
Tensorflow does not currently support Python 3.8. Python 3.7 is the most recent version that is supported. Installing Python 3.7 as a workaround will not affect your code because Python 3.7 and 3.8 are similar.
TensorFlow allows you to design dataflow graphs and structures to express how data goes through a graph by taking inputs as a multi-dimensional array called Tensor. It allows you to draw a flowchart of possible processes on these inputs, which flow in one way and another.
- From the PyCharm menu, select File > Settings > Project.
- Choose your most recent project.
- Within your project tab, click the Python Interpreter tab.
- To add a new library to the project, click the little + sign.
- Now, without quotes, write in the library to be installed, “TensorFlow,” and select Install Package.
- Once the installation has finished, shut all popup windows.
If you used pip to install TensorFlow, you can quickly check the version by typing pip show TensorFlow in your command prompt.
TO RUN TENSORFLOW WITHOUT THE GPU, USE tenserflow.compat.v1.ConfigProto().
The save weights() method can be used to save the weights of all the layers. It keeps track of the weights of the model’s layers. When saving a model using tensorflow, it is recommended to use the save() method rather than the save weights() method.
There are two techniques to train a machine learning model in TensorFlow.js:
Using LayersModel.fit() or LayersModel.fitDataset() from the Layers API ().
Using the Core API in conjunction with Optimizer.
If you’re using the anaconda shell, simply type “conda remove” to remove it from the shell.
Uninstall the previous version of Tensorflow.
Install the tensorflow-gpu pip package. Install the tensorflow-gpu package.
Nvidia Graphics Card & Drivers Installation
CUDA is available for download and installation.
cuDNN can be downloaded and installed.
Check with a simple program.
Keras is a high-level neural network library that works on top of TensorFlow. TensorFlow is an open-source end-to-end platform and library for different machine learning tasks. Both offer high-level APIs for quickly creating and training models, while Keras is more user-friendly because to its Python integration.
TensorFlow is an open-source machine learning platform that runs from start to finish. It features a robust ecosystem of tools, libraries, and community resources that enable researchers to advance the state-of-the-art in machine learning and developers to quickly build and deploy ML-powered apps.
The implementations of TensorFlow and PyTorch are both equally accurate. TensorFlow, on the other hand, has a much longer training time but uses less memory. TensorFlow may be a better alternative if custom features in the neural network are required. PyTorch allows for faster prototyping than TensorFlow, but TensorFlow may be a better option if special features are required in the neural network.
According to the StackOverflow Developers Survey 2020, ensorFlow is one of the most popular frameworks among developers. Around 65% of those polled expressed an interest in continuing to construct models with TensorFlow.
The biggest difference between this and what we did in Lesson 1 is that you’ll need TensorFlow with GPU support for your system. However, before you can use TensorFlow in this environment, you must first set up your machine to use CUDA and CuDNN.
Machine Learning Foundations is a free training course that teaches you the essentials of using TensorFlow to build machine learning models.
The Google Brain team created TensorFlow for internal Google use in research and production. In 2015, the first version was released under the Apache License 2.0.
TensorFlow is an open-source framework for running machine learning, deep learning, and other statistical and predictive analytics workloads developed by Google researchers.
Google designed and distributed TensorFlow, a Python library for fast numerical computing.