In this blog post, we’ll be learning how to get the shape of a tensor in TensorFlow. We’ll go over what a tensor is, and how to get its shape using the tf.shape function.
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In this article, we will see how to get the shape of a tensor in TensorFlow. This is a very important function because it allows us to know the number of elements that a tensor contains, which is essential when we want to train a neural network.
To get the shape of a tensor, we use the tf.shape() function. This function takes one argument, which is the tensor that we want to get the shape of. For example, if we have a 2×3 matrix, we can get its shape like this:
tf.shape(matrix) # => [2, 3]
What is a Tensor?
A tensor is a generalization of a vector to higher dimensions. In other words, a list of numbers, where each number is identified by a tuple of indices, is called a tensor. In mathematics, tensors can be defined as multilinear maps from one vector space to another. In physics, tensors are used to describe things that can be transformed, like stress or strain. In computer science, they are used to represent multidimensional data.
Tensors are represented by n-dimensional arrays in TensorFlow. The rank of a tensor is the number of dimensions of the array representation. For example, the rank of a [1, 2, 3] tensor is 1 (a vector), and the rank of a [[1, 2], [3, 4]] tensor is 2 (a matrix). The shape of a tensor corresponds to the number of elements in each dimension of the array representation. For example, the shape of a [1, 2, 3] tensor is  (a vector with 3 elements), and the shape of a [[1, 2], [3, 4]] tensor is [2, 2] (a matrix with 2 rows and 2 columns).
In TensorFlow, you can get the shape of a tensor as follows:
What is the Shape of a Tensor?
In mathematics, the shape of a tensor is the number of dimensions it has, as well as the size of each dimension. For example, a 2-dimensional tensor could have the following shapes:
– (2, 3) – This tensor has 2 dimensions, and each dimension has 3 elements.
– (1, 5) – This tensor has 2 dimensions, but one of the dimensions only has 1 element.
– (5,) – This tensor is actually a 1-dimensional tensor with 5 elements. The parentheses are optional in this case.
How to Get the Shape of a Tensor in TensorFlow
In order to get the shape of a tensor in TensorFlow, you can use the tf.shape function. This function will give you the shape of a tensor as a list of integers.
For example, if you have a tensor with the shape [5, 10, 15], the tf.shape function will return .
Why is the Shape of a Tensor Important?
The shape of a tensor is important because it defines the number of dimensions that the tensor has. For example, a 3×5 tensor has two dimensions, while a 5×3 tensor has three dimensions. The shape of a tensor is also important because it defines the size of each dimension. For example, a 3×5 tensor has three elements in the first dimension and five elements in the second dimension.
How to Use the Shape of a Tensor
In TensorFlow, the shape of a tensor is the number of rows and columns in the tensor. The shape of a vector is the number of elements in the vector. The shape of a matrix is the number of rows and columns in the matrix. The shape of a 3-D tensor is the number of rows, columns, and depth in the tensor.
The shape of a tensor is an important attribute because it allows you to verify that the tensor has the correct number of elements for a given operation. For example, if you want to multiply two matrices, you can use the shape attribute to check that they are both matrices (2-D tensors) and that they have compatible shapes (the number of columns in matrix A must match the number of rows in matrix B).
You can get the shape of a tensor using the tf.shape function. For example, if you have a tensor x with shape [5, 10], you can get itsshape using tf.shape(x), which will return [5, 10].
In short, you can get the shape of a tensor in TensorFlow using the tf.shape() function. You can also pass in a dimension to get the specific size of that dimension. Finally, keep in mind that the shape of a tensor is not always static and can change during the execution of a TensorFlow graph.
 J. Dean, R. Monga, K. Chen, G. Corrado, M. Ranzato, A. Senior, P. Tucker, K. Yang, and Q. V. Le. Large scale distributed deep networks. In NIPS’12: Neural Information Processing Systems Conference, 2012.
 M. D. Ernst and J.-P. Jouvelot. Tensorflow: Large-scale machine learning on heterogeneous systems (white paper), 2016
Keyword: How to Get the Shape of a Tensor in TensorFlow