This tutorial will illustrate **how to convert dictionary to tensor TensorFlow by using Python**. and also we will look at some examples of how we can use the** tf.convert_to_tensor()** function in ** TensorFlow**. And we will cover these topics.

- How to convert dictionary to tensor tensorflow
- How to convert array to tensor Pytorch
- How to convert tensor to float tensorflow
- How to convert image to tensor tensorflow
- TensorFlow empty tensor

**Table of Contents**show

## How to convert dictionary to tensor tensorflow

- This section will discuss how to convert dictionary to tensor TensorFlow.
- To do this task, we are going to use the for-loop method and in this example first, we initialized a dictionary and assign an element in the form of a key-value pair element.
- Next, we want to convert the given dictionary into a tensor and for this, we will use the for loop and set the condition if n > m then it will store in the result.

**Example**:

Let’s take an example and check how to convert the dictionary to tensor in Python TensorFlow.

**Source Code**:

```
City_name_USA = {'New York': {'3': 17, '2': 17, '2': 18},
'Los Angeles': {'3': 45, '1': 78, '2': 78}}
num = 0
for m, new_val in City_name_USA.items():
for n in new_val.keys():
if int(n) > int(num):
num = int(n)
result = []
for new_val in City_name_USA.values():
lst = [0] * (num+1)
for n, val2 in sorted(new_val.items()):
lst[int(n)] = val2
result.append(lst)
print(result)
```

You can refer to the below Screenshot.

This is how to convert the dictionary to tensor in Python TensorFlow.

Read: Tensorflow convert sparse tensor to tensor

## How to convert array to tensor Pytorch

- Here we will discuss how to convert the array to Pytorch tensor in Python.
- Similar to
is a PyTorch tensor. The distinction between these two is that a tensor makes use of the GPUs to speed up computations involving numbers.*numpy.ndarray* - The
is used to transform a*torch.from*into a PyTorch tensor(). And the*numpy.ndarray*method converts a tensor to a*numpy()*.*numpy.ndarray* - First, we have to require the torch and Numpy library and then convert an array to Pytorch tensor by using the
.*torch.from_numpy()*

**Syntax**:

Let’s look at the Syntax and understand the working of a ** torch.from_numpy()** function.

`torch.from_numpy(ndarray) `

Note: This function takes only one parameter ndarray and that specifies the input array which we want to convert with tensor.

Example:

```
import torch
import numpy as np
Population_in_USA = np.array([13,45,7,78,90])
# Convert Numpy array to torch.Tensor
result = torch.from_numpy(Population_in_USA)
print(result)
```

In the following given code, we created an array by using the ** np.array()** function and within this function, we assign the integer values as an argument. Next, we will use the

**function and within this function, we assigned the dictionary as an argument.**

*torch.from_numpy()*Here is the implementation of the following given code.

As you can see in the Screenshot we converted the NumPy array to Pytorch Tensor.

Read: Module ‘tensorflow’ has no attribute ‘sparse_placeholder’

## How to convert tensor to float tensorflow

- This section will discuss how to convert the tensorflow tensor to float in Python.
- To perform this task we are going to use the
**tf.cast()**function this function is used to cast the given input tensor to a new datatype. This function takes two main parameters which are the input tensor that is being cast.

**Syntax**:

Here is the Syntax of** tf.cast()** function in Python TensorFlow

```
tf.cast(
x, dtype, name=None
)
```

- It consists of a few parameters
**x**: This parameter defines the input tensor and a numeric type Tensor, SparseTensor, or IndexedSlices. It might be an**int8**,**int16**,**int32**,**int64**,**float16**,**float32**,**float64**,**complex64**,**complex128**, or**bfloat16**. It might also be an**uint8, int8, int16, int32, or int64**.**dtype**: This parameter specifies the data type of the input tensor.**name**: By default, it takes none value and specifies the name of the operation.

**Example**:

```
import tensorflow as tf
input_tens = tf.constant([23,45,67,8])
result=tf.cast(input_tens, tf.float32)
print(result)
```

In the above code, we have used the ** tf.constant()** function for the creation of the input tensor. And then we used the

**for converting the integer datatype to float datatype.**

*tf.cast()*Here is the implementation of the following given code.

This is how to convert the input tensor to float value by using TensorFlow.

Read: Module ‘tensorflow’ has no attribute ‘div’

## How to convert image to tensor tensorflow

- In this section, we will discuss how to convert an image to a tensor and for typical axis order for an image tensor.
- The image must be a PIL image or a numpy image. ndarray (HxWxC) in the [0, 255] range. Here, H, W, and C stand for the image’s height, breadth, and number of channels.
- In the given example h is the height and w is the width image, c is the channel image.

**Example**:

```
import tensorflow as tf
image = tf.io.read_file("tiger.png")
input_tensor = tf.io.decode_image(image, channels=3, dtype=tf.dtypes.float32)
tensor = tf.image.resize(input_tensor, [224, 224])
result = tf.expand_dims(tensor, axis=0)
print(result)
```

Here is the execution of the following given code.

In this example, we have converted the image to a tensor.

Read: Module ‘tensorflow’ has no attribute ‘truncated_normal’

## TensorFlow empty tensor

- Let us discuss how to create an empty tensor in Python TensorFlow.
- To perform this task we are going to use the
**tf.experimental.numpy.empty()**function and this function variants the empty’s value.

**Syntax**:

Here is the Syntax of **tf.experimental.numpy.empty()** function in Python TensorFlow

```
tf.experimental.numpy.empty(
shape, dtype=float
)
```

- It consists of a few parameters
**shape**: This parameter defines the shape of the empty tensor.**dtype:**By default, it takes the float value and specifies the data type of the input tensor.

**Example**:

```
import tensorflow as tf
result= tf.experimental.numpy.empty(shape=(2,2),dtype=float)
print(result)
```

Here is the Screenshot of the following given code

Also, take a look at some more Python TensorFlow tutorials.

- TensorFlow Fully Connected Layer
- Batch Normalization TensorFlow
- Tensorflow custom loss function
- TensorFlow Sparse Tensor

In this tutorial, we have understood how to convert a dictionary to a tensor by using Python TensorFlow. and also we will look at some examples of how we can use the** tf.convert_to_tensor()** function in ** TensorFlow**. And we will cover these topics.

- Convert dictionary to tensor tensorflow
- Convert array to tensor Pytorch
- Convert tensor to float tensorflow
- Convert image to tensor tensorflow
- TensorFlow empty tensor

Python is one of the most popular languages in the United States of America. I have been working with Python for a long time and I have expertise in working with various libraries on Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etc… I have experience in working with various clients in countries like United States, Canada, United Kingdom, Australia, New Zealand, etc. Check out my profile.