Emerging as a widely used platform, TensorFlow stands as the trending and competition among its associates. With all its capabilities, it eases the computations of the machine and deep learning. But also makes the process slow and adds a tinge of complexity.<\/p>\n
We list some advantages and disadvantages of working with TensorFlow below.<\/p>\n
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It is an open-source platform that makes it available to all the users around and ready for the development of any system on it.<\/p>\n
TensorFlow provides a better way of visualizing data with its graphical approach. It also allows easy debugging of nodes with the help of TensorBoard. This reduces the effort of visiting the whole code and effectively resolves the neural network.<\/p>\n
TensorFlow has compatibility with Keras, which allows its users to code some high-level functionality sections in it. Keras provides system-specific functionality to TensorFlow, such as pipelining, estimators, and eager execution.<\/p>\n
The Keras functional API supports a variety of topologies with different combinations of inputs, output, and layers.<\/p>\n
Almost every operation can be performed using this platform. With its characteristic of being deployed on every machine and graphical representation of a model allows its users to develop any kind of system using TensorFlow.<\/p>\n
Hence TensorFlow has been able to develop systems like Airbnb, Dropbox, Intel, Snapchat, etc.<\/p>\n
It is compatible with many languages such as C++, JavaScript, Python, C#, Ruby, and Swift. This allows a user to work in an environment they are comfortable in.<\/p>\n
TensorFlow finds its use as a hardware acceleration library due to the parallelism of work models. It uses different distribution strategies in GPU and CPU systems.<\/p>\n
A user can choose to run its code on either of the architecture based on the modeling rule. A system chooses a GPU if not specified. This process reduces the memory allocation to an extent.<\/p>\n
TensorFlow also has its architecture TPU, which performs computations faster than GPU and CPU. Models built using TPU can be easily deployed on a cloud at a cheaper rate and executed at a faster rate.<\/p>\n
Deep learning uses TensorFlow for its development as it allows building neural networks with the help of graphs that represent operations as nodes.<\/p>\n
TensorFlow acts in multiple domains such as image recognition, voice detection, motion detection, time series, etc hence it suits the requirement of a user.<\/p>\n
Noe when we have seen advantages of TensorFlow, let us see some disadvantages of it.<\/p>\n
TensorFlow releases different updates every 2-3 month, increasing the overhead for a user to install it and bind it with the existing system.<\/p>\n
TensorFlow provides homonyms that share similar names but different implementations, which makes it confusing to remember and use. For eg: tf.nn.conv2d, tf.nn.convolution, tf.layers.conv2d, tf.layers.Conv2d has varying meanings and often makes it inconsistent with its usability.<\/p>\n
TensorFlow’s architecture TPU only allows the execution of a model not to train it.<\/p>\n
Although TensorFlow reduces the length of code and makes it easier for a user to access it, it adds a level of complexity to its use. Every code needs to be executed using any platform for its support which increases the dependency for the execution.<\/p>\n
TensorFlow lags at providing the symbolic loops for indefinite sequences. It has its usage for definite sequences, which makes it a usable system. Hence it is referred to as a low-level API.<\/p>\n
TensorFlow has only NVIDIA support for GPU and python support for GPU programming. It does not have any other support.<\/p>\n
TensorFlow has low speed with respect to its competitors. It has less usability in comparison to other frameworks.<\/p>\n
TensorFlow does not provide much features for the Windows Operating System users. It opens a wide range of features for the Linux users. But still, Windows users can download TensorFlow using the anaconda prompt or using the pip package.<\/p>\n
So finally we have seen advantages and disadvantages of TensorFlow. TensorFlow provides many features that make it suitable for building systems based on machine learning and deep learning. But it drops some abilities to make it an ideal platform.<\/p>\n
The latest updates in the system look to cover all the setbacks it has. Hence, the system follows a lot more capabilities towards its users and steps to make it more user-friendly.<\/p>\n","protected":false},"excerpt":{"rendered":"
Emerging as a widely used platform, TensorFlow stands as the trending and competition among its associates. With all its capabilities, it eases the computations of the machine and deep learning. But also makes the...<\/p>\n","protected":false},"author":1,"featured_media":79525,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2618],"tags":[3058,3059,3060,3061],"class_list":["post-79489","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tensorflow","tag-advantages-and-disadvantages-of-tensorflow","tag-advantages-of-tensorflow","tag-disadvantages-of-tensorflow","tag-pros-and-cons-of-tensorflow"],"yoast_head":"\n