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Tensorflow is a machine learning framework that is provided by Google. It is an open-source framework used in conjunction with Python to implement algorithms, deep learning applications and much more. It is used in research and for production purposes. It has optimization techniques that help in performing complicated mathematical operations quickly. This is because it uses NumPy and multi-dimensional arrays. These multi-dimensional arrays are also known as ‘tensors’. The framework supports working with deep neural network.

The ‘tensorflow’ package can be installed on Windows using the below line of code −

pip install tensorflow

Tensor is a data structure used in TensorFlow. It helps connect edges in a flow diagram. This flow diagram is known as the ‘Data flow graph’. Tensors are nothing but multidimensional array or a list. They can be identified using three main attributes −

**Rank** − It tells about the dimensionality of the tensor. It can be understood as the order of the tensor or the number of dimensions in the tensor that has been defined.

**Type** − It tells about the data type associated with the elements of the Tensor. It can be a one dimensional, two dimensional or n dimensional tensor.

**Shape** − It is the number of rows and columns together.

The ‘Fashion MNIST’ dataset contains images of clothing of different kinds. It contains grayscale images of more than 70 thousand clothes that belong to 10 different categories. These images are of low resolution (28 x 28 pixels).

We are using the Google Colaboratory to run the below code. Google Colab or Colaboratory helps run Python code over the browser and requires zero configuration and free access to GPUs (Graphical Processing Units). Colaboratory has been built on top of Jupyter Notebook.

Following is the code snippet −

print("An image from the test data is taken") img = test_images[26] print("The dimensions of the image are ") print(img.shape) print("The image is added to batch where it is the only entity") img = (np.expand_dims(img,0)) print("The dimensions of the image now ") print(img.shape) my_pred = probability_model.predict(img) print("The prediction made is ") print(my_pred) plot_value_array(1, my_pred[0], test_labels) _ = plt.xticks(range(10), class_names, rotation=45) np.argmax(my_pred[0])

Code credit − https://www.tensorflow.org/tutorials/keras/classification

An image from the test data is taken The dimensions of the image are (28, 28) The image is added to batch where it is the only entity The dimensions of the image now (1, 28, 28) The prediction made is [[8.0459216e-07 1.7074371e-09 2.6175227e-02 1.8855806e-07 1.7909618e-01 2.1126857e-06 7.9472500e-01 7.5104166e-11 4.7921480e-07 1.6657851e-10]] 6

The dimensions of the test image is displayed on the console

The ‘expand_dims’ is optimized to work on a batch or collection of examples simultaneously.

A single image is also added as a part of a list.

The predict function returns a list of lists, where every list corresponds to an image in the batch data.

The predictions for the image that we want is extracted and displayed on the console.

It is visualized using ‘matplotlib’ as a bar graph.

- Related Questions & Answers
- How can TensorFlow be used to build the model for Fashion MNIST dataset in Python?
- How can TensorFlow be used to train the model for Fashion MNIST dataset in Python?
- How can TensorFlow be used to make predictions for Fashion MNIST dataset in Python?
- How can Tensorflow be used to define a model for MNIST dataset?
- How can TensorFlow be used to download and explore the Fashion MNIST dataset using Python?
- How can TensorFlow be used to preprocess Fashion MNIST data in Python?
- How can Tensorflow be used to export the model so that it can be used later?
- How can TensorFlow be used to verify the predictions for Fashion MNIST in Python?
- How can Tensorflow be used with pre-trained model to build the training and validation dataset?
- How can Tensorflow and pre-trained model be used to configure the dataset for performance?
- How can Tensorflow be used with pre-trained model to determine the number of batches in the dataset?
- How can Tensorflow and pre-trained model be used to compile the model using Python?
- How can Tensorflow used with the pre-trained model to compile the model?
- How can Tensorflow be used with flower dataset to continue training the model?
- How can Tensorflow be used with abalone dataset to build a sequential model?

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