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) = test_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 = test_utils.get_small_sequential_mlp(num_hidden=NUM_HIDDEN, num_classes=NUM_CLASSES, input_dim=INPUT_DIM) model.compile( loss="binary_crossentropy", optimizer=tf.compat.v1.train.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: 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 with tf.Graph().as_default(): 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, [0.0, 1.0, 2.0, 3.0, 4.0]) 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 tf.Graph().as_default(), self.cached_session(): model = test_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_epoch_and_batch_logging(self): class FileWriterStub: 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 with tf.Graph().as_default(): 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}) tb_cbk.on_train_end() 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}) tb_cbk.on_train_end() 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_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) = test_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 = test_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 = [ 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_update_freq(self): class FileWriterStub: 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 with tf.Graph().as_default(): 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.assertLen(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.assertLen(tb_cbk.writer.batch_summaries, 1) tb_cbk.on_batch_end(0, {"acc": 5.0, "size": 1}) self.assertLen(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.assertLen(tb_cbk.writer.batch_summaries, 1) tb_cbk.on_batch_end(0, {"acc": 5.0, "size": 10}) self.assertLen(tb_cbk.writer.batch_summaries, 1) tb_cbk.on_batch_end(0, {"acc": 5.0, "size": 10}) self.assertLen(tb_cbk.writer.batch_summaries, 2) tb_cbk.on_batch_end(0, {"acc": 10.0, "size": 10}) self.assertLen(tb_cbk.writer.batch_summaries, 2) self.assertFalse(tb_cbk.writer.epoch_summaries) tb_cbk.on_train_end()
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) = test_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 tf.Graph().as_default(), self.cached_session(): model = sequential.Sequential() model.add( layers.Dense(NUM_HIDDEN, input_dim=INPUT_DIM, activation="relu")) # non_trainable_weights: moving_variance, moving_mean model.add(layers.BatchNormalization()) model.add(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: def __init__(self, logdir, graph=None): self.logdir = logdir self.graph = graph self.steps_seen = [] def add_summary(self, summary, global_step): summary_obj = tf.compat.v1.Summary() # ensure a valid Summary proto is being sent if isinstance(summary, bytes): summary_obj.ParseFromString(summary) else: assert isinstance(summary, tf.compat.v1.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) = test_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 tf.Graph().as_default(), self.cached_session(): model = sequential.Sequential() model.add( layers.Dense(NUM_HIDDEN, input_dim=INPUT_DIM, activation="relu")) # non_trainable_weights: moving_variance, moving_mean model.add(layers.BatchNormalization()) model.add(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])