In the previous article, we learnt how to use Google Colaboratory to get started with TensorFlow. In this tutorial we will cover tensors in TensorFlow.
Tensors are multi-dimensional arrays having a uniform type that is called a
Similar to Python strings and numbers, tensors are immutable. A tensor's contents can never be changed, only a new tensor can be created.
Let's get started with creating a tensor in TensorFlow. We would be using Google Colaboratory to write the code, if you don't know how to use Google Colaboratory refer tensorflow with google colab to get started.
Create a New notebook in Colaboratory. Write below code in the cell and run the cell.
import tensorflow as tf t1 = tf.constant(6) t1
tf.Tensorhas the following properties:
t1is having data type as
t1is having shape
t1.shape, that means its a scalar or rank 0 tensor.
numpyfunction associated with tensor returns A NumPy array of the same shape and dtype.
dtype property of Tensor represents the type of the elements in a Tensor. Following are some of the
DType's defined in TensorFlow.
For a complete list of
DType's refer this link.
Let's have a look to some os the important methods available in
tf.dtype.is_compatible_with is used to check if a Tensor of the `other` `DType` will be implicitly converted to this `DType`. To understand this have a look to the below code snippet.
import tensorflow as tf import numpy as np t1 = tf.constant(6, dtype=np.int16) t2 = tf.constant(6, dtype=tf.int16) t3 = tf.constant(6.0, dtype=tf.float16) print(t1) print(t2) print(t3) print(t1.dtype.is_compatible_with(t2.dtype)) ptint(t2.dtype.is_compatible_with(t3.dtype))
tf.dtype.limits returns the tuple containing min, max intensity limits of
import tensorflow as tf t1 = tf.constant(6) print(t1) print(t1.dtype.limits)
True if the dtype of tensor is (non-quantized) integer type.
import tensorflow as tf t1 = tf.constant(6) print(t1.dtype.is_integer)
True if the dtype of tensor is (non-quantized, real) floating point type.
import tensorflow as tf t1 = tf.constant(6.0) print(t1.dtype.is_floating)
True if the dtype of tesnor is a boolean data type.
import tensorflow as tf t1 = tf.constant(True, dtype=tf.bool) print(t1.dtype.is_bool) t2 = tf.constant(False, dtype=tf.bool) print(t2.dtype.is_bool)