Where by class numbers will be replaced by the class names. Predictions = for k in predicted_class_indices] If we make predictions, classes (as detected by the.
Here, the generator will report Found x images belonging to 1 classes (since there is only one subfolder). Each subfolder in C:/kerasimages/pred/ is interpreted as one class by the generator. Next step is I want the name of the classes: labels = (train_generator.class_indices) It is important to respect the logic of the data generator, so the subfolder /images/ is required. Keep in mind that base64 encoding will make the image file larger by approx 30.
This data can then be used in your CSS files which saves the browser from having to make additional HTTP requests for the external resources, and can therefore increase page loading speed. Predicted_class_indices=np.argmax(pred,axis=1) The Data URI Generator will produce base64 encoded data from an image file. In my case it was 4 classes, so class numbers were 0,1,2 and 3.
Running the above code will give output in probabilities so at first I need to convert them to class number. Pred=cnn.predict_generator(test_generator,verbose=1,steps=306/batch_size) Then ran the following code: test_generator = test_datagen.flow_from_directory(Īnd most importantly you've to write the following code: So in my case I made another folder inside test folder and named it all_classes. Now instead of 58 steps per epoch you have 116.So first of all the test images should be placed inside a separate folder inside the test folder. You can simply increase the steps_per_epoch beyond number of samples // batch_size by multiplying by some factor: history = model.fit(ġ/116 - ETA: 12:11 - loss: 1.5885
How to use this generator correctly with function fit to have allĭata in my training set, including original, non-augmented images andĪugmented images, and to cycle through it several times/step? You will notice that the progress bar looks like this because steps_per_epoch has not been explicitly defined: Epoch 1/10Īnd if you add this parameter, you will see the steps in the progress bar: history = model.fit(ģ/58 - ETA: 3:19 - loss: 4.1357 Lambda: img_gen.flow_from_directory(flowers, batch_size=BATCH_SIZE, shuffle=True, class_mode='sparse'), If you wrap flow_from_directory with tf._generator like this: train_ds = tf._generator( The ImageDataGenerator class is used to do this. Tf.2D(32, 3, padding='same', activation='relu'), When training a model, the Keras deep learning package allows you to employ data augmentation automatically. Train_ds = img_gen.flow_from_directory(flowers, batch_size=BATCH_SIZE, shuffle=True, class_mode='sparse') Using the following example, you can check that this is the case (on TF 2.7) by looking at the steps per epoch in the progress bar: import tensorflow as tf Generally, the flow_from_directory() method allows you to read the images directly from a directory and augment them while your model is being trained and as already stated here, it iterates for every sample in each folder every epoch. For example, in this post, the user is describing the exact behavior you are expecting. I think the documentation can be quite confusing and I imagine the behavior is different depending on your Tensorflow and Keras version. So basically, my question is how to use this generator correctly with function fit to have all data in my training set, including original, non-augmented images and augmented images, and to cycle through it several times/steps (right now it seems it does only one step per epoch)? Does it somehow infer number of steps? Also, does it use only augmented data, or it also uses non-augmented images in batch? When passing an infinitely repeating dataset, you must specify the steps_per_epoch argument. I am a bit confused how it works, because train_aug_ds is generator, so it should give infinitely big dataset. Train_aug_ds = train_aug.flow_from_directory(Īnd to train my model I did the following: model.fit(Īnd it worked.
So, I loaded my data like this: train_aug = ImageDataGenerator( I also checked few blogs about using it, but they don't answer all my questions. I learned the hard way it is actually a generator, not iterator (because type(train_aug_ds) gives I thought it is an iterator). (The standard Keras ImageDataGenerator module processes batch images then trains. I am playing with augmentation of data in Keras lately and I am using basic ImageDataGenerator. Generate batches of tensor image data with real-time data augmentation.