def test_Tensorboard_epoch_and_batch_logging(self):
class FileWriterStub(object):
def __init__(self, logdir, graph=None):
self.logdir = logdir
self.graph = graph
def add_summary(self, summary, step):
if 'batch_' in summary.value[0].tag:
self.batch_summary = (step, summary)
elif 'epoch_' in summary.value[0].tag:
self.epoch_summary = (step, summary)
def flush(self):
pass
def close(self):
pass
temp_dir = self.get_temp_dir()
self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True)
tb_cbk = callbacks_v1.TensorBoard(temp_dir, update_freq='batch')
tb_cbk.writer = FileWriterStub(temp_dir)
tb_cbk.on_batch_end(0, {'acc': 5.0})
batch_step, batch_summary = tb_cbk.writer.batch_summary
self.assertEqual(batch_step, 0)
self.assertEqual(batch_summary.value[0].simple_value, 5.0)
tb_cbk = callbacks_v1.TensorBoard(temp_dir, update_freq='epoch')
tb_cbk.writer = FileWriterStub(temp_dir)
tb_cbk.on_epoch_end(0, {'acc': 10.0})
epoch_step, epoch_summary = tb_cbk.writer.epoch_summary
self.assertEqual(epoch_step, 0)
self.assertEqual(epoch_summary.value[0].simple_value, 10.0)
def test_TensorBoard_update_freq(self):
class FileWriterStub(object):
def __init__(self, logdir, graph=None):
self.logdir = logdir
self.graph = graph
self.batch_summaries = []
self.epoch_summaries = []
def add_summary(self, summary, step):
if 'batch_' in summary.value[0].tag:
self.batch_summaries.append((step, summary))
elif 'epoch_' in summary.value[0].tag:
self.epoch_summaries.append((step, summary))
def flush(self):
pass
def close(self):
pass
temp_dir = self.get_temp_dir()
self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True)
# Epoch mode
tb_cbk = callbacks_v1.TensorBoard(temp_dir, update_freq='epoch')
tb_cbk.writer = FileWriterStub(temp_dir)
tb_cbk.on_batch_end(0, {'acc': 5.0, 'size': 1})
self.assertEqual(tb_cbk.writer.batch_summaries, [])
tb_cbk.on_epoch_end(0, {'acc': 10.0, 'size': 1})
self.assertEqual(len(tb_cbk.writer.epoch_summaries), 1)
tb_cbk.on_train_end()
# Batch mode
tb_cbk = callbacks_v1.TensorBoard(temp_dir, update_freq='batch')
tb_cbk.writer = FileWriterStub(temp_dir)
tb_cbk.on_batch_end(0, {'acc': 5.0, 'size': 1})
self.assertEqual(len(tb_cbk.writer.batch_summaries), 1)
tb_cbk.on_batch_end(0, {'acc': 5.0, 'size': 1})
self.assertEqual(len(tb_cbk.writer.batch_summaries), 2)
self.assertFalse(tb_cbk.writer.epoch_summaries)
tb_cbk.on_train_end()
# Integer mode
tb_cbk = callbacks_v1.TensorBoard(temp_dir, update_freq=20)
tb_cbk.writer = FileWriterStub(temp_dir)
tb_cbk.on_batch_end(0, {'acc': 5.0, 'size': 10})
self.assertFalse(tb_cbk.writer.batch_summaries)
tb_cbk.on_batch_end(0, {'acc': 5.0, 'size': 10})
self.assertEqual(len(tb_cbk.writer.batch_summaries), 1)
tb_cbk.on_batch_end(0, {'acc': 5.0, 'size': 10})
self.assertEqual(len(tb_cbk.writer.batch_summaries), 1)
tb_cbk.on_batch_end(0, {'acc': 5.0, 'size': 10})
self.assertEqual(len(tb_cbk.writer.batch_summaries), 2)
tb_cbk.on_batch_end(0, {'acc': 10.0, 'size': 10})
self.assertEqual(len(tb_cbk.writer.batch_summaries), 2)
self.assertFalse(tb_cbk.writer.epoch_summaries)
tb_cbk.on_train_end()
def test_Tensorboard_eager(self):
temp_dir = tempfile.mkdtemp(dir=self.get_temp_dir())
self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True)
(x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
train_samples=TRAIN_SAMPLES,
test_samples=TEST_SAMPLES,
input_shape=(INPUT_DIM, ),
num_classes=NUM_CLASSES)
y_test = np_utils.to_categorical(y_test)
y_train = np_utils.to_categorical(y_train)
model = testing_utils.get_small_sequential_mlp(num_hidden=NUM_HIDDEN,
num_classes=NUM_CLASSES,
input_dim=INPUT_DIM)
model.compile(loss='binary_crossentropy',
optimizer=adam.AdamOptimizer(0.01),
metrics=['accuracy'])
cbks = [callbacks_v1.TensorBoard(log_dir=temp_dir)]
model.fit(x_train,
y_train,
batch_size=BATCH_SIZE,
validation_data=(x_test, y_test),
callbacks=cbks,
epochs=2,
verbose=0)
self.assertTrue(os.path.exists(temp_dir))
def test_Tensorboard_batch_logging(self):
class FileWriterStub(object):
def __init__(self, logdir, graph=None):
self.logdir = logdir
self.graph = graph
self.batches_logged = []
self.summary_values = []
self.summary_tags = []
def add_summary(self, summary, step):
self.summary_values.append(summary.value[0].simple_value)
self.summary_tags.append(summary.value[0].tag)
self.batches_logged.append(step)
def flush(self):
pass
def close(self):
pass
temp_dir = self.get_temp_dir()
self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True)
tb_cbk = callbacks_v1.TensorBoard(temp_dir, update_freq='batch')
tb_cbk.writer = FileWriterStub(temp_dir)
for batch in range(5):
tb_cbk.on_batch_end(batch, {'acc': batch})
self.assertEqual(tb_cbk.writer.batches_logged, [0, 1, 2, 3, 4])
self.assertEqual(tb_cbk.writer.summary_values, [0., 1., 2., 3., 4.])
self.assertEqual(tb_cbk.writer.summary_tags, ['batch_acc'] * 5)
def callbacks_factory(histogram_freq):
return [
callbacks_v1.TensorBoard(log_dir=filepath,
histogram_freq=histogram_freq,
write_images=True,
write_grads=True,
batch_size=5)
]
def test_Tensorboard_histogram_summaries_with_generator(self):
np.random.seed(1337)
tmpdir = self.get_temp_dir()
self.addCleanup(shutil.rmtree, tmpdir, ignore_errors=True)
def generator():
x = np.random.randn(10, 100).astype(np.float32)
y = np.random.randn(10, 10).astype(np.float32)
while True:
yield x, y
with self.cached_session():
model = testing_utils.get_small_sequential_mlp(num_hidden=10,
num_classes=10,
input_dim=100)
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
tsb = callbacks_v1.TensorBoard(log_dir=tmpdir,
histogram_freq=1,
write_images=True,
write_grads=True,
batch_size=5)
cbks = [tsb]
# fit with validation generator
model.fit_generator(generator(),
steps_per_epoch=2,
epochs=2,
validation_data=generator(),
validation_steps=2,
callbacks=cbks,
verbose=0)
with self.assertRaises(ValueError):
# fit with validation generator but no
# validation_steps
model.fit_generator(generator(),
steps_per_epoch=2,
epochs=2,
validation_data=generator(),
callbacks=cbks,
verbose=0)
self.assertTrue(os.path.exists(tmpdir))
def test_TensorBoard_with_ReduceLROnPlateau(self):
with self.cached_session():
temp_dir = self.get_temp_dir()
self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True)
(x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
train_samples=TRAIN_SAMPLES,
test_samples=TEST_SAMPLES,
input_shape=(INPUT_DIM, ),
num_classes=NUM_CLASSES)
y_test = np_utils.to_categorical(y_test)
y_train = np_utils.to_categorical(y_train)
model = testing_utils.get_small_sequential_mlp(
num_hidden=NUM_HIDDEN,
num_classes=NUM_CLASSES,
input_dim=INPUT_DIM)
model.compile(loss='binary_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
cbks = [
keras.callbacks.ReduceLROnPlateau(monitor='val_loss',
factor=0.5,
patience=4,
verbose=1),
callbacks_v1.TensorBoard(log_dir=temp_dir)
]
model.fit(x_train,
y_train,
batch_size=BATCH_SIZE,
validation_data=(x_test, y_test),
callbacks=cbks,
epochs=2,
verbose=0)
assert os.path.exists(temp_dir)
def test_TensorBoard(self):
np.random.seed(1337)
temp_dir = self.get_temp_dir()
self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True)
(x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
train_samples=TRAIN_SAMPLES,
test_samples=TEST_SAMPLES,
input_shape=(INPUT_DIM, ),
num_classes=NUM_CLASSES)
y_test = np_utils.to_categorical(y_test)
y_train = np_utils.to_categorical(y_train)
def data_generator(train):
if train:
max_batch_index = len(x_train) // BATCH_SIZE
else:
max_batch_index = len(x_test) // BATCH_SIZE
i = 0
while 1:
if train:
yield (x_train[i * BATCH_SIZE:(i + 1) * BATCH_SIZE],
y_train[i * BATCH_SIZE:(i + 1) * BATCH_SIZE])
else:
yield (x_test[i * BATCH_SIZE:(i + 1) * BATCH_SIZE],
y_test[i * BATCH_SIZE:(i + 1) * BATCH_SIZE])
i += 1
i %= max_batch_index
# case: Sequential
with self.cached_session():
model = keras.models.Sequential()
model.add(
keras.layers.Dense(NUM_HIDDEN,
input_dim=INPUT_DIM,
activation='relu'))
# non_trainable_weights: moving_variance, moving_mean
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.Dense(NUM_CLASSES, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
tsb = callbacks_v1.TensorBoard(log_dir=temp_dir,
histogram_freq=1,
write_images=True,
write_grads=True,
batch_size=5)
cbks = [tsb]
# fit with validation data
model.fit(x_train,
y_train,
batch_size=BATCH_SIZE,
validation_data=(x_test, y_test),
callbacks=cbks,
epochs=3,
verbose=0)
# fit with validation data and accuracy
model.fit(x_train,
y_train,
batch_size=BATCH_SIZE,
validation_data=(x_test, y_test),
callbacks=cbks,
epochs=2,
verbose=0)
# fit generator with validation data
model.fit_generator(data_generator(True),
len(x_train),
epochs=2,
validation_data=(x_test, y_test),
callbacks=cbks,
verbose=0)
# fit generator without validation data
# histogram_freq must be zero
tsb.histogram_freq = 0
model.fit_generator(data_generator(True),
len(x_train),
epochs=2,
callbacks=cbks,
verbose=0)
# fit generator with validation data and accuracy
tsb.histogram_freq = 1
model.fit_generator(data_generator(True),
len(x_train),
epochs=2,
validation_data=(x_test, y_test),
callbacks=cbks,
verbose=0)
# fit generator without validation data and accuracy
tsb.histogram_freq = 0
model.fit_generator(data_generator(True),
len(x_train),
epochs=2,
callbacks=cbks)
assert os.path.exists(temp_dir)
def test_Tensorboard_histogram_summaries_in_test_function(self):
class FileWriterStub(object):
def __init__(self, logdir, graph=None):
self.logdir = logdir
self.graph = graph
self.steps_seen = []
def add_summary(self, summary, global_step):
summary_obj = summary_pb2.Summary()
# ensure a valid Summary proto is being sent
if isinstance(summary, bytes):
summary_obj.ParseFromString(summary)
else:
assert isinstance(summary, summary_pb2.Summary)
summary_obj = summary
# keep track of steps seen for the merged_summary op,
# which contains the histogram summaries
if len(summary_obj.value) > 1:
self.steps_seen.append(global_step)
def flush(self):
pass
def close(self):
pass
def _init_writer(obj, _):
obj.writer = FileWriterStub(obj.log_dir)
np.random.seed(1337)
tmpdir = self.get_temp_dir()
self.addCleanup(shutil.rmtree, tmpdir, ignore_errors=True)
(x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
train_samples=TRAIN_SAMPLES,
test_samples=TEST_SAMPLES,
input_shape=(INPUT_DIM, ),
num_classes=NUM_CLASSES)
y_test = np_utils.to_categorical(y_test)
y_train = np_utils.to_categorical(y_train)
with self.cached_session():
model = keras.models.Sequential()
model.add(
keras.layers.Dense(NUM_HIDDEN,
input_dim=INPUT_DIM,
activation='relu'))
# non_trainable_weights: moving_variance, moving_mean
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.Dense(NUM_CLASSES, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
callbacks_v1.TensorBoard._init_writer = _init_writer
tsb = callbacks_v1.TensorBoard(log_dir=tmpdir,
histogram_freq=1,
write_images=True,
write_grads=True,
batch_size=5)
cbks = [tsb]
# fit with validation data
model.fit(x_train,
y_train,
batch_size=BATCH_SIZE,
validation_data=(x_test, y_test),
callbacks=cbks,
epochs=3,
verbose=0)
self.assertAllEqual(tsb.writer.steps_seen, [0, 1, 2, 3, 4, 5])
