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KerasAPIReference.txt

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Arguments
target: Total number of steps expected, None if unknown.
width: Progress bar width on screen.
verbose: Verbosity mode, 0 (silent), 1 (verbose), 2 (semi-verbose)
stateful_metrics: Iterable of string names of metrics that should not be averaged over time. Metrics in this list will be displayed as-is. All others will be averaged by the progbar before display.
interval: Minimum visual progress update interval (in seconds).
unit_name: Display name for step counts (usually "step" or "sample").
Sequence class
tf.keras.utils.Sequence()
Base object for fitting to a sequence of data, such as a dataset.
Every Sequence must implement the __getitem__ and the __len__ methods. If you want to modify your dataset between epochs you may implement on_epoch_end. The method __getitem__ should return a complete batch.
Notes:
Sequence are a safer way to do multiprocessing. This structure guarantees that the network will only train once on each sample per epoch which is not the case with generators.
Examples
from skimage.io import imread
from skimage.transform import resize
import numpy as np
import math
# Here, `x_set` is list of path to the images
# and `y_set` are the associated classes.
class CIFAR10Sequence(Sequence):
def __init__(self, x_set, y_set, batch_size):
self.x, self.y = x_set, y_set
self.batch_size = batch_size
def __len__(self):
return math.ceil(len(self.x) / self.batch_size)
def __getitem__(self, idx):
batch_x = self.x[idx * self.batch_size:(idx + 1) *
self.batch_size]
batch_y = self.y[idx * self.batch_size:(idx + 1) *
self.batch_size]
return np.array([
resize(imread(file_name), (200, 200))
for file_name in batch_x]), np.array(batch_y)