What are the TensorFlow Placeholders?
Posted On: Feb 22, 2018
Answered by FarheenPlaceholders in TensorFlow is simply a variable that is assigned to the data. It allows to create the operations and build the computational graph without needing the data. Placeholder in Tensorflow is used to feed the actual training data. They can be of integer, float, etc.
Example
labels_placeholder = tf.placeholder(tf.int32, shape=(batch_size))
1 Answer Written
Related Questions
Subscribe Our NewsLetter
Never Miss an Articles from us.
Recent Articles
Featured Categories
- Common Interview Questions
- Python Flask Interview Questions
- NoSQL Interview Questions
- jQuery Interview Questions
- C Programming Interview Questions
- AngularJs Interview Questions
- Node JS Interview Questions
- JavaScript Interview Questions
- Core Java Interview Questions
- HTML Interview Questions
- Laravel Interview Questions
- Wordpress Interview Questions
- PHP Interview Questions
TensorFlow Interview Questions
What is TensorFlow? Please Explain
TensorFlow is a platform where one can learn machine learning / deep learning/multilayer neural networks from the Google library. Libraries that use data science are helpful to describe complex networ...
TensorFlow Interview Questions
Enlist few major features of the TensorFlow.
Below are major features of TensorFlow:TensorFlow has the biggest ability is to build neural networks using which machines can develop logical thinking and learning analogous to humans. It is one...
TensorFlow Interview Questions
What are the tensors ? How many types of tensors are available?
Tensors can be thought of as vectors and matrices of higher dimensions. They represent n-dimensional arrays of base data-type. Each element of a tensor is of the same data-type which is always known. ...