How can Tensorflow be used to load the flower dataset and model off the disk using Python?

Tensorflow can be used to load the flower dataset and model off the disk using the ‘image_dataset_from_directory’ method.

Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?

A neural network that contains at least one layer is known as a convolutional layer. We can use the Convolutional Neural Network to build learning model. 

The intuition behind transfer learning for image classification is, if a model is trained on a large and general dataset, this model can be used to effectively serve as a generic model for the visual world. It would have learned the feature maps, which means the user won’t have to start from scratch by training a large model on a large dataset.

TensorFlow Hub is a repository that contains pre-trained TensorFlow models. TensorFlow can be used to fine-tune learning models.

We will understand how to use models from TensorFlow Hub with tf.keras, use an image classification model from TensorFlow Hub.  Once this is done, transfer learning can be performed to fine-tune a model for customized image classes. This is done by using a pretrained classifier model to take an image and predict what it is. This can be done without needing any training.  

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.


print("The flower dataset")
data_root = tf.keras.utils.get_file(
print("Load data into the model using images off disk with image_dataset_from_directory")
batch_size = 32
img_height = 224
img_width = 224
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
image_size=(img_height, img_width),

Code credit −


The flower dataset
Downloading data from
228818944/228813984 [==============================] - 4s 0us/step
Load data into the model using images off disk with image_dataset_from_directory
Found 3670 files belonging to 5 classes.
Using 2936 files for training.


  • If we need to train the model with different classes, a model from TFHub can be used.
  • This will help train a custom image classier by retraining the top layer of the model.
  • This will help in recognizing the classes in our dataset.
  • We will be using the iris dataset for this.
  • The model istrained using images off disk using image_dataset_from_directory.