Adding upstream version 4.64.1.

Signed-off-by: Daniel Baumann <daniel@debian.org>

tests/tests_keras.py

@@ -0,0 +1,93 @@
+from __future__ import division
+
+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 "{epochs}/{epochs}".format(epochs=epochs) in res
+    assert "{batches}/{batches}".format(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 "{epochs}/{epochs}".format(epochs=epochs) in res
+    assert "{batches}/{batches}".format(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 "{epochs}/{epochs}".format(epochs=epochs) in res
+    assert "{batches}/{batches}".format(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 "{epochs}/{epochs}".format(epochs=initial_epoch - 1) not in res
+    assert "{epochs}/{epochs}".format(epochs=epochs) in res