I am using tf.keras.utils.image_dataset_from_directory
to load a dataset of 4575 images. While this function allows to split the data into two subsets (with the validation_split
parameter), I want to split it into training, testing, and validation subsets.
I have tried using dataset.skip()
and dataset.take()
to further split one of the resulting subsets, but these functions return a SkipDataset
and a TakeDataset
respectively (by the way, contrary to the documentation, where it is claimed that these functions return a Dataset
). This leads to problems when fitting the model - the metrics calculated on validation sets (val_loss, val_accuracy) disappear from model history.
So, my question is: is there a way to split a Dataset
into three subsets for training, validation and testing, so that all three subsets are also Dataset
objects?
Code used to load the data
def load_data_tf(data_path: str, img_shape=(256,256), batch_size: int=8):
train_ds = tf.keras.utils.image_dataset_from_directory(
data_path,
validation_split=0.2,
subset="training",
label_mode='categorical',
seed=123,
image_size=img_shape,
batch_size=batch_size)
val_ds = tf.keras.utils.image_dataset_from_directory(
data_path,
validation_split=0.3,
subset="validation",
label_mode='categorical',
seed=123,
image_size=img_shape,
batch_size=batch_size)
return train_ds, val_ds
train_dataset, test_val_ds = load_data_tf('data_folder', img_shape = (256,256), batch_size=8)
test_dataset = test_val_ds.take(686)
val_dataset = test_val_ds.skip(686)
Model compilation and fitting
model.compile(optimizer='sgd',
loss=tf.keras.losses.CategoricalCrossentropy(from_logits=False),
metrics=['accuracy'])
history = model.fit(train_dataset, epochs=50, validation_data=val_dataset, verbose=1)
When using a normal Dataset
, val_accuracy
and val_loss
are present in the history of the model:
But when using a SkipDataset
, they are not:
The issue is that you are not taking and skipping samples when you do test_val_ds.take(686)
and test_val_ds.skip(686)
, but actually batches. Try running print(val_dataset.cardinality())
and you will see how many batches you really have reserved for validation. I am guessing val_dataset
is empty, because you do not have 686 batches for validation. Here is a working example:
import tensorflow as tf
import pathlib
dataset_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)
batch_size = 32
train_ds = tf.keras.utils.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="training",
seed=123,
image_size=(180, 180),
batch_size=batch_size)
val_ds = tf.keras.utils.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="validation",
seed=123,
image_size=(180, 180),
batch_size=batch_size)
test_dataset = val_ds.take(5)
val_ds = val_ds.skip(5)
print('Batches for testing -->', test_dataset.cardinality())
print('Batches for validating -->', val_ds.cardinality())
model = tf.keras.Sequential([
tf.keras.layers.Rescaling(1./255, input_shape=(180, 180, 3)),
tf.keras.layers.Conv2D(16, 3, padding='same', activation='relu'),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Conv2D(32, 3, padding='same', activation='relu'),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Conv2D(64, 3, padding='same', activation='relu'),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(5)
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
epochs=1
history = model.fit(
train_ds,
validation_data=val_ds,
epochs=1
)
Found 3670 files belonging to 5 classes.
Using 2936 files for training.
Found 3670 files belonging to 5 classes.
Using 734 files for validation.
Batches for testing --> tf.Tensor(5, shape=(), dtype=int64)
Batches for validating --> tf.Tensor(18, shape=(), dtype=int64)
92/92 [==============================] - 96s 1s/step - loss: 1.3516 - accuracy: 0.4489 - val_loss: 1.1332 - val_accuracy: 0.5645
In this example, with a batch_size
of 32, you can clearly see that the validation set reserved 23 batches. Afterwards, 5 batches were given to the test set and 18 batches remained for the validation set.