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])