If you are reading this article, I am sure that we share similar interests and are/will be in similar industries. So let's connect via Linkedin! Please do not hesitate to send a contact request! In this post, we will dive into the details of TensorFlow Tensors. Tensors are TensorFlow's multi-dimensional arrays with uniform type.

If you are reading this article, I am sure that we share similar interests and are/will be in similar industries. So let's connect via Linkedin! Please do not hesitate to send a contact request! In this post, we will dive into the details of TensorFlow Tensors. Tensors are TensorFlow's multi-dimensional arrays with uniform type.

We start by importing TensorFlow as tf. Then we print the version of TensorFlow that we are using. We are using TensorFlow 1.5.0. In this video, we're going to use tf.reshape to change the shape of a TensorFlow tensor as long as the number of elements stay the same. We will do three examples to show how reshape works. Let's start out with an initial TensorFlow constant tensor shaped 2x3x4 with numerical integer values between 1 and 24, all of whom have the data type of int32.

We start by importing TensorFlow as tf. Then we print out the version of TensorFlow that we are using. We are using TensorFlow 1.5.0. In this video, we're going to multiply two matrices by using tf.matmul operation. The first matrix will be a TensorFlow tensor shaped 3x3 with min values of 1, max values of 10, and the data type will be int32.

If TensorFlow is your primary framework, and you are looking for a simple & high-level model definition interface to make your life easier, this tutorial is for you. Keras layers and models are fully compatible with pure-TensorFlow tensors, and as a result, Keras makes a great model definition add-on for TensorFlow, and can even be used alongside other TensorFlow libraries. Note that this tutorial assumes that you have configured Keras to use the TensorFlow backend (instead of Theano). Here are instructions on how to do this. Let's start with a simple example: MNIST digits classification.