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from .tests_tqdm import importorskip, mark
pytestmark = mark.slow
@mark.filterwarnings("ignore:.*:DeprecationWarning")
def test_keras(capsys):
"""Test tqdm.keras.TqdmCallback"""
TqdmCallback = importorskip('tqdm.keras').TqdmCallback
np = importorskip('numpy')
try:
import keras as K
except ImportError:
K = importorskip('tensorflow.keras')
# 1D autoencoder
dtype = np.float32
model = K.models.Sequential([
K.layers.InputLayer((1, 1), dtype=dtype), K.layers.Conv1D(1, 1)])
model.compile("adam", "mse")
x = np.random.rand(100, 1, 1).astype(dtype)
batch_size = 10
batches = len(x) / batch_size
epochs = 5
# just epoch (no batch) progress
model.fit(
x,
x,
epochs=epochs,
batch_size=batch_size,
verbose=False,
callbacks=[
TqdmCallback(
epochs,
desc="training",
data_size=len(x),
batch_size=batch_size,
verbose=0)])
_, res = capsys.readouterr()
assert "training: " in res
assert f"{epochs}/{epochs}" in res
assert f"{batches}/{batches}" not in res
# full (epoch and batch) progress
model.fit(
x,
x,
epochs=epochs,
batch_size=batch_size,
verbose=False,
callbacks=[
TqdmCallback(
epochs,
desc="training",
data_size=len(x),
batch_size=batch_size,
verbose=2)])
_, res = capsys.readouterr()
assert "training: " in res
assert f"{epochs}/{epochs}" in res
assert f"{batches}/{batches}" in res
# auto-detect epochs and batches
model.fit(
x,
x,
epochs=epochs,
batch_size=batch_size,
verbose=False,
callbacks=[TqdmCallback(desc="training", verbose=2)])
_, res = capsys.readouterr()
assert "training: " in res
assert f"{epochs}/{epochs}" in res
assert f"{batches}/{batches}" in res
# continue training (start from epoch != 0)
initial_epoch = 3
model.fit(
x,
x,
initial_epoch=initial_epoch,
epochs=epochs,
batch_size=batch_size,
verbose=False,
callbacks=[TqdmCallback(desc="training", verbose=0,
miniters=1, mininterval=0, maxinterval=0)])
_, res = capsys.readouterr()
assert "training: " in res
assert f"{initial_epoch - 1}/{initial_epoch - 1}" not in res
assert f"{epochs}/{epochs}" in res
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