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