History = model.fit_generator(generator=train_generator, # and now fit the model with 16 worker threads reading the images Model = Model(inputs=base_model.input, outputs=predictions) X = Dense(len(train_generator.class_indices), activation='softmax', name='predictions')(x) # our custom fc_layer(top) with the number of classes we want # now we create the model loading the 300x300 efficientnet with imagenet weights, include_top= false drops the last fc layer, due we want to use our own.īase_model = EfficientNetB3(include_top=False, weights='imagenet') Validation_generator=train_datagen.flow_from_dataframe(dataframe=df, Train_generator=train_datagen.flow_from_dataframe(dataframe=df, # now we create a training and a test generator from a pandas dataframe, where x_col is the absolute path to the image file and y_col is the column with the label, disabling validate_filenames and drop duplicates speeds up everything for large data sets. Validation_split=0.1 # here we can split the data into test and validation and use it later on Rotation_range=30, # The image data generator offers a lot of convinience features the augment the data Rescale=1./255, # we scale the colors down to 8 bit per channel In this example we use the Keras efficientNet on imagenet with custom labels. In keras this is achieved by utilizing the ImageDataGenerator class. GPU utilization in nvidia-smi Training with keras’ ImageDataGeneratorįirst let’s take a look at the code, where we use a dataframe to feed the network with data. If you are working on windows, don’t look trust the performance charts in the windows built-in task manager, they are not very accurate. The GPU utilization translates direct to training time, more GPU utilization means more parallel execution, means more speed. The GPU-utilization shows how much your GPU is used and can be observed by either nvidia-smi in the command line or with GPU-Z. When training a neural net on the GPU the first thing to look at is the GPU Utilization. Finally, I will show how to build a TFRecord data set and use it in keras to achieve comparable results. I will show that it is not a problem of keras itself, but a problem of how the preprocessing works and a bug in older versions of keras-preprocessing. In this post I will show an example, where tensorflow is 10x times faster than keras. If you use this software, please cite it using the metadata from this CITATION.If you ever trained a CNN with keras on your GPU with a lot of images, you might have noticed that the performance is not as good as in tensorflow on comparable tasks. Keep safe_triplet default False value but be careful with choosing the batch_size so you do not end up with a last batch containing a single class (or a single sample)īatch size is calculated by multiplying num_classes_per_batch and num_images_per_class.If you are to use this generator with TripletLoss, your should either: This however guarantee that both num_classes_per_batch and num_images_per_class are fixed for all batches including later ones. But as sampling weighted per class, every epoch will include a very high percentage of the dataset and should approach 100% as dataset size increases. Setting safe_triplet to True does not guarantee that every epoch will include all different samples from the dataset. Setting safe_triplet to False (Default) makes sure that every image is seen exactly one time per epoch but it does not guarantee a fixed num_classes_per_batch or num_images_per_class in later batches. If there is no enough samples remaining from the choosen class, it is skipped and another class is choosen (This behaviour can be disabled and we indefinitely repeat the datasets) Your directory structure should be: main_directory/īehind the scenes, this API creates a different dataset for every class and by using weighted random sampling, a number of classes is drawn (num_classes_per_batch) and a specific number of images is selected from every choosen class (num_images_per_class) as long as there are enough samples from this class. Generates a balanced per batch tf.data.Dataset from image files in a directory. Usage from kerasgen.balanced_image_dataset import balanced_image_dataset_from_directory train_ds = balanced_image_dataset_from_directory ( directory, num_classes_per_batch = 2, num_images_per_class = 5, image_size = ( 256, 256 ), validation_split = 0.2, subset = 'training', seed = 555, safe_triplet = True ) val_ds = balanced_image_dataset_from_directory ( directory, num_classes_per_batch = 2, num_images_per_class = 5, image_size = ( 256, 256 ), validation_split = 0.2, subset = 'validation', seed = 555, safe_triplet = True ) This datagenerator is compatible with TripletLoss as it guarantees the existence of postive pairs in every batch. A Keras/Tensorflow compatible image data generator for creating balanced batches.
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