image_size | Get image width | Computer Vision library

The parameter shuffle of tf.keras.preprocessing.image_dataset_from_directory is set to True by default, if you want deterministic results, maybe try setting it to False:

🧸💬 Hi, from the document image_dataset_from_directory it specifically required a label as inferred and none when used but the directory structures are specific to the label name. I am using the cats and dogs image to categorize where cats are labeled '0' and dog is the next label.

[ Sample ]:

The problem is that you are using train_ds.take(1) to take exactly one batch from your dataset, which has the BATCH_SIZE = 5. If you want to display 9 images in a 3x3 plot, then simply change your BATCH_SIZE to 9. Alternatively, you can adjust the number of subplots you want to create like this:

You can try using tf.py_function to integrate PIL operations in graph mode. Here is an example with a batch size of 1 to keep it simple (you can change the batch size afterwards):

Before

No neurons in the last layer should be same as the number of classes you want to classify (it should be 3 if you are trying to classify 3 types of flowers not 32) . Added a few convolution layers and pooling layers to improve the performance too.

This kind of model does not work with labels. Try running:

You can use tf.data.Dataset.skip and tf.data.Dataset.take:

You can create a zero-filled block of data with calloc(), then write that data to the file in one call (and remember to free the data afterwards):

In the train folder you have two folders NORMAL and PNEUMONIA ? If so then you need to use flow_from_directory instead of flow_from_dataframe: