diff options
14 files changed, 296 insertions, 65 deletions
diff --git a/tensorflow/python/keras/backend.py b/tensorflow/python/keras/backend.py index 13f52fbae7..7509ef9c59 100644 --- a/tensorflow/python/keras/backend.py +++ b/tensorflow/python/keras/backend.py @@ -2338,7 +2338,8 @@ def permute_dimensions(x, pattern): @tf_export('keras.backend.resize_images') -def resize_images(x, height_factor, width_factor, data_format): +def resize_images(x, height_factor, width_factor, data_format, + interpolation='nearest'): """Resizes the images contained in a 4D tensor. Arguments: @@ -2346,40 +2347,55 @@ def resize_images(x, height_factor, width_factor, data_format): height_factor: Positive integer. width_factor: Positive integer. data_format: One of `"channels_first"`, `"channels_last"`. + interpolation: A string, one of `nearest` or `bilinear`. Returns: A tensor. Raises: - ValueError: if `data_format` is neither - `channels_last` or `channels_first`. + ValueError: in case of incorrect value for + `data_format` or `interpolation`. """ if data_format == 'channels_first': - original_shape = int_shape(x) - new_shape = array_ops.shape(x)[2:] - new_shape *= constant_op.constant( - np.array([height_factor, width_factor]).astype('int32')) + rows, cols = 2, 3 + elif data_format == 'channels_last': + rows, cols = 1, 2 + else: + raise ValueError('Invalid `data_format` argument: %s' % (data_format,)) + + original_shape = int_shape(x) + new_shape = array_ops.shape(x)[rows:cols + 1] + new_shape *= constant_op.constant( + np.array([height_factor, width_factor], dtype='int32')) + + if data_format == 'channels_first': x = permute_dimensions(x, [0, 2, 3, 1]) + if interpolation == 'nearest': x = image_ops.resize_nearest_neighbor(x, new_shape) + elif interpolation == 'bilinear': + x = image_ops.resize_bilinear(x, new_shape) + else: + raise ValueError('interpolation should be one ' + 'of "nearest" or "bilinear".') + if data_format == 'channels_first': x = permute_dimensions(x, [0, 3, 1, 2]) - x.set_shape((None, None, original_shape[2] * height_factor - if original_shape[2] is not None else None, - original_shape[3] * width_factor - if original_shape[3] is not None else None)) - return x - elif data_format == 'channels_last': - original_shape = int_shape(x) - new_shape = array_ops.shape(x)[1:3] - new_shape *= constant_op.constant( - np.array([height_factor, width_factor]).astype('int32')) - x = image_ops.resize_nearest_neighbor(x, new_shape) - x.set_shape((None, original_shape[1] * height_factor - if original_shape[1] is not None else None, - original_shape[2] * width_factor - if original_shape[2] is not None else None, None)) - return x + + if original_shape[rows] is None: + new_height = None else: - raise ValueError('Invalid data_format: ' + str(data_format)) + new_height = original_shape[rows] * height_factor + + if original_shape[cols] is None: + new_width = None + else: + new_width = original_shape[cols] * width_factor + + if data_format == 'channels_first': + output_shape = (None, None, new_height, new_width) + else: + output_shape = (None, new_height, new_width, None) + x.set_shape(output_shape) + return x @tf_export('keras.backend.resize_volumes') diff --git a/tensorflow/python/keras/callbacks.py b/tensorflow/python/keras/callbacks.py index 3d6000f223..4c12c83a4c 100644 --- a/tensorflow/python/keras/callbacks.py +++ b/tensorflow/python/keras/callbacks.py @@ -24,6 +24,7 @@ from collections import Iterable from collections import OrderedDict import copy import csv +import io import json import math import os @@ -606,24 +607,28 @@ class EarlyStopping(Callback): """Stop training when a monitored quantity has stopped improving. Arguments: - monitor: quantity to be monitored. - min_delta: minimum change in the monitored quantity + monitor: Quantity to be monitored. + min_delta: Minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute change of less than min_delta, will count as no improvement. - patience: number of epochs with no improvement + patience: Number of epochs with no improvement after which training will be stopped. verbose: verbosity mode. - mode: one of {auto, min, max}. In `min` mode, + mode: One of `{"auto", "min", "max"}`. In `min` mode, training will stop when the quantity monitored has stopped decreasing; in `max` mode it will stop when the quantity monitored has stopped increasing; in `auto` mode, the direction is automatically inferred from the name of the monitored quantity. - baseline: baseline value for the monitored quantity. + baseline: Baseline value for the monitored quantity. Training will stop if the model doesn't show improvement over the baseline. + restore_best_weights: Whether to restore model weights from + the epoch with the best value of the monitored quantity. + If False, the model weights obtained at the last step of + training are used. """ def __init__(self, @@ -632,7 +637,8 @@ class EarlyStopping(Callback): patience=0, verbose=0, mode='auto', - baseline=None): + baseline=None, + restore_best_weights=False): super(EarlyStopping, self).__init__() self.monitor = monitor @@ -642,6 +648,8 @@ class EarlyStopping(Callback): self.min_delta = abs(min_delta) self.wait = 0 self.stopped_epoch = 0 + self.restore_best_weights = restore_best_weights + self.best_weights = None if mode not in ['auto', 'min', 'max']: logging.warning('EarlyStopping mode %s is unknown, ' @@ -673,25 +681,37 @@ class EarlyStopping(Callback): self.best = np.Inf if self.monitor_op == np.less else -np.Inf def on_epoch_end(self, epoch, logs=None): - current = logs.get(self.monitor) + current = self.get_monitor_value(logs) if current is None: - logging.warning('Early stopping conditioned on metric `%s` ' - 'which is not available. Available metrics are: %s', - self.monitor, ','.join(list(logs.keys()))) return if self.monitor_op(current - self.min_delta, self.best): self.best = current self.wait = 0 + if self.restore_best_weights: + self.best_weights = self.model.get_weights() else: self.wait += 1 if self.wait >= self.patience: self.stopped_epoch = epoch self.model.stop_training = True + if self.restore_best_weights: + if self.verbose > 0: + print('Restoring model weights from the end of the best epoch.') + self.model.set_weights(self.best_weights) def on_train_end(self, logs=None): if self.stopped_epoch > 0 and self.verbose > 0: print('Epoch %05d: early stopping' % (self.stopped_epoch + 1)) + def get_monitor_value(self, logs): + logs = logs or {} + monitor_value = logs.get(self.monitor) + if monitor_value is None: + logging.warning('Early stopping conditioned on metric `%s` ' + 'which is not available. Available metrics are: %s', + self.monitor, ','.join(list(logs.keys()))) + return monitor_value + @tf_export('keras.callbacks.RemoteMonitor') class RemoteMonitor(Callback): @@ -839,6 +859,12 @@ class TensorBoard(Callback): `embeddings_layer_names`. Numpy array (if the model has a single input) or list of Numpy arrays (if the model has multiple inputs). Learn [more about embeddings](https://www.tensorflow.org/programmers_guide/embedding) + update_freq: `'batch'` or `'epoch'` or integer. When using `'batch'`, + writes the losses and metrics to TensorBoard after each batch. + The same applies for `'epoch'`. If using an integer, let's say `1000`, + the callback will write the metrics and losses to TensorBoard every + 1000 samples. Note that writing too frequently to TensorBoard + can slow down your training. Raises: ValueError: If histogram_freq is set and no validation data is provided. @@ -862,7 +888,8 @@ class TensorBoard(Callback): embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None, - embeddings_data=None): + embeddings_data=None, + update_freq='epoch'): super(TensorBoard, self).__init__() self.log_dir = log_dir self.histogram_freq = histogram_freq @@ -882,6 +909,12 @@ class TensorBoard(Callback): self.embeddings_layer_names = embeddings_layer_names self.embeddings_metadata = embeddings_metadata self.embeddings_data = embeddings_data + if update_freq == 'batch': + self.update_freq = 1 + else: + self.update_freq = update_freq + self._samples_seen = 0 + self._samples_seen_at_last_write = 0 def _init_writer(self): """Sets file writer.""" @@ -1045,13 +1078,17 @@ class TensorBoard(Callback): # use v2 summary ops with self.writer.as_default(), summary_ops_v2.always_record_summaries(): for name, value in logs.items(): - summary_ops_v2.scalar(name, value.item(), step=step) + if isinstance(value, np.ndarray): + value = value.item() + summary_ops_v2.scalar(name, value, step=step) else: # use FileWriter from v1 summary for name, value in logs.items(): + if isinstance(value, np.ndarray): + value = value.item() summary = tf_summary.Summary() summary_value = summary.value.add() - summary_value.simple_value = value.item() + summary_value.simple_value = value summary_value.tag = name self.writer.add_summary(summary, step) self.writer.flush() @@ -1076,10 +1113,14 @@ class TensorBoard(Callback): """Writes scalar summaries for metrics on every training batch.""" # Don't output batch_size and batch number as Tensorboard summaries logs = logs or {} - batch_logs = {('batch_' + k): v - for k, v in logs.items() - if k not in ['batch', 'size', 'num_steps']} - self._write_custom_summaries(self._total_batches_seen, batch_logs) + self._samples_seen += logs.get('size', 1) + samples_seen_since = self._samples_seen - self._samples_seen_at_last_write + if self.update_freq != 'epoch' and samples_seen_since >= self.update_freq: + batch_logs = {('batch_' + k): v + for k, v in logs.items() + if k not in ['batch', 'size', 'num_steps']} + self._write_custom_summaries(self._total_batches_seen, batch_logs) + self._samples_seen_at_last_write = self._samples_seen self._total_batches_seen += 1 def on_epoch_begin(self, epoch, logs=None): @@ -1103,7 +1144,11 @@ class TensorBoard(Callback): logs = {('epoch_' + k): v for k, v in logs.items() if k not in ['batch', 'size', 'num_steps']} - self._write_custom_summaries(epoch, logs) + if self.update_freq == 'epoch': + step = epoch + else: + step = self._samples_seen + self._write_custom_summaries(step, logs) # pop the histogram summary op after each epoch if self.histogram_freq: @@ -1309,7 +1354,12 @@ class CSVLogger(Callback): self.writer = None self.keys = None self.append_header = True - self.file_flags = 'b' if six.PY2 and os.name == 'nt' else '' + if six.PY2: + self.file_flags = 'b' + self._open_args = {} + else: + self.file_flags = '' + self._open_args = {'newline': '\n'} super(CSVLogger, self).__init__() def on_train_begin(self, logs=None): @@ -1317,9 +1367,12 @@ class CSVLogger(Callback): if os.path.exists(self.filename): with open(self.filename, 'r' + self.file_flags) as f: self.append_header = not bool(len(f.readline())) - self.csv_file = open(self.filename, 'a' + self.file_flags) + mode = 'a' else: - self.csv_file = open(self.filename, 'w' + self.file_flags) + mode = 'w' + self.csv_file = io.open(self.filename, + mode + self.file_flags, + **self._open_args) def on_epoch_end(self, epoch, logs=None): logs = logs or {} @@ -1345,9 +1398,13 @@ class CSVLogger(Callback): class CustomDialect(csv.excel): delimiter = self.sep + fieldnames = ['epoch'] + self.keys + if six.PY2: + fieldnames = [unicode(x) for x in fieldnames] + self.writer = csv.DictWriter( self.csv_file, - fieldnames=['epoch'] + self.keys, + fieldnames=fieldnames, dialect=CustomDialect) if self.append_header: self.writer.writeheader() diff --git a/tensorflow/python/keras/callbacks_test.py b/tensorflow/python/keras/callbacks_test.py index 467bc4cdc4..bb85347033 100644 --- a/tensorflow/python/keras/callbacks_test.py +++ b/tensorflow/python/keras/callbacks_test.py @@ -313,6 +313,42 @@ class KerasCallbacksTest(test.TestCase): hist = model.fit(data, labels, callbacks=[stopper], verbose=0, epochs=20) assert len(hist.epoch) >= patience + def test_EarlyStopping_final_weights_when_restoring_model_weights(self): + + class DummyModel(object): + + def __init__(self): + self.stop_training = False + self.weights = -1 + + def get_weights(self): + return self.weights + + def set_weights(self, weights): + self.weights = weights + + def set_weight_to_epoch(self, epoch): + self.weights = epoch + + early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', + patience=2, + restore_best_weights=True) + early_stop.model = DummyModel() + losses = [0.2, 0.15, 0.1, 0.11, 0.12] + # The best configuration is in the epoch 2 (loss = 0.1000). + epochs_trained = 0 + early_stop.on_train_begin() + for epoch in range(len(losses)): + epochs_trained += 1 + early_stop.model.set_weight_to_epoch(epoch=epoch) + early_stop.on_epoch_end(epoch, logs={'val_loss': losses[epoch]}) + if early_stop.model.stop_training: + break + # The best configuration is in epoch 2 (loss = 0.1000), + # and while patience = 2, we're restoring the best weights, + # so we end up at the epoch with the best weights, i.e. epoch 2 + self.assertEqual(early_stop.model.get_weights(), 2) + def test_RemoteMonitor(self): if requests is None: return @@ -534,11 +570,15 @@ class KerasCallbacksTest(test.TestCase): batch_size=BATCH_SIZE, validation_data=(x_test, y_test), callbacks=cbks, - epochs=1, + epochs=2, verbose=0) with open(filepath) as csvfile: - output = ' '.join(csvfile.readlines()) + list_lines = csvfile.readlines() + for line in list_lines: + assert line.count(sep) == 4 + assert len(list_lines) == 5 + output = ' '.join(list_lines) assert len(re.findall('epoch', output)) == 1 os.remove(filepath) @@ -1115,11 +1155,11 @@ class KerasCallbacksTest(test.TestCase): temp_dir = self.get_temp_dir() self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True) - tb_cbk = keras.callbacks.TensorBoard(temp_dir) + tb_cbk = keras.callbacks.TensorBoard(temp_dir, update_freq='batch') tb_cbk.writer = FileWriterStub(temp_dir) for batch in range(5): - tb_cbk.on_batch_end(batch, {'acc': np.float32(batch)}) + 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) @@ -1147,14 +1187,17 @@ class KerasCallbacksTest(test.TestCase): temp_dir = self.get_temp_dir() self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True) - tb_cbk = keras.callbacks.TensorBoard(temp_dir) + tb_cbk = keras.callbacks.TensorBoard(temp_dir, update_freq='batch') tb_cbk.writer = FileWriterStub(temp_dir) - tb_cbk.on_batch_end(0, {'acc': np.float32(5.0)}) - tb_cbk.on_epoch_end(0, {'acc': np.float32(10.0)}) + 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 = keras.callbacks.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) @@ -1192,6 +1235,66 @@ class KerasCallbacksTest(test.TestCase): self.assertTrue(os.path.exists(temp_dir)) + 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 = keras.callbacks.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) + + # Batch mode + tb_cbk = keras.callbacks.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) + + # Integer mode + tb_cbk = keras.callbacks.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) + def test_RemoteMonitorWithJsonPayload(self): if requests is None: self.skipTest('`requests` required to run this test') @@ -1226,6 +1329,7 @@ class KerasCallbacksTest(test.TestCase): def test_fit_generator_with_callback(self): class TestCallback(keras.callbacks.Callback): + def set_model(self, model): # Check the model operations for the optimizer operations that # the _make_train_function adds under a named scope for the diff --git a/tensorflow/python/keras/layers/convolutional.py b/tensorflow/python/keras/layers/convolutional.py index 8f5872385c..58024677ee 100644 --- a/tensorflow/python/keras/layers/convolutional.py +++ b/tensorflow/python/keras/layers/convolutional.py @@ -1951,6 +1951,7 @@ class UpSampling2D(Layer): It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be "channels_last". + interpolation: A string, one of `nearest` or `bilinear`. Input shape: 4D tensor with shape: @@ -1967,10 +1968,18 @@ class UpSampling2D(Layer): `(batch, channels, upsampled_rows, upsampled_cols)` """ - def __init__(self, size=(2, 2), data_format=None, **kwargs): + def __init__(self, + size=(2, 2), + data_format=None, + interpolation='nearest', + **kwargs): super(UpSampling2D, self).__init__(**kwargs) self.data_format = conv_utils.normalize_data_format(data_format) self.size = conv_utils.normalize_tuple(size, 2, 'size') + if interpolation not in {'nearest', 'bilinear'}: + raise ValueError('`interpolation` argument should be one of `"nearest"` ' + 'or `"bilinear"`.') + self.interpolation = interpolation self.input_spec = InputSpec(ndim=4) def compute_output_shape(self, input_shape): @@ -1992,7 +2001,8 @@ class UpSampling2D(Layer): def call(self, inputs): return backend.resize_images( - inputs, self.size[0], self.size[1], self.data_format) + inputs, self.size[0], self.size[1], self.data_format, + interpolation=self.interpolation) def get_config(self): config = {'size': self.size, 'data_format': self.data_format} diff --git a/tensorflow/python/keras/layers/convolutional_test.py b/tensorflow/python/keras/layers/convolutional_test.py index f88d632ab5..bdc175b8b9 100644 --- a/tensorflow/python/keras/layers/convolutional_test.py +++ b/tensorflow/python/keras/layers/convolutional_test.py @@ -790,6 +790,42 @@ class UpSamplingTest(test.TestCase): np.testing.assert_allclose(np_output, expected_out) @tf_test_util.run_in_graph_and_eager_modes + def test_upsampling_2d_bilinear(self): + num_samples = 2 + stack_size = 2 + input_num_row = 11 + input_num_col = 12 + for data_format in ['channels_first', 'channels_last']: + if data_format == 'channels_first': + inputs = np.random.rand(num_samples, stack_size, input_num_row, + input_num_col) + else: + inputs = np.random.rand(num_samples, input_num_row, input_num_col, + stack_size) + + testing_utils.layer_test(keras.layers.UpSampling2D, + kwargs={'size': (2, 2), + 'data_format': data_format, + 'interpolation': 'bilinear'}, + input_shape=inputs.shape) + + if not context.executing_eagerly(): + for length_row in [2]: + for length_col in [2, 3]: + layer = keras.layers.UpSampling2D( + size=(length_row, length_col), + data_format=data_format) + layer.build(inputs.shape) + outputs = layer(keras.backend.variable(inputs)) + np_output = keras.backend.eval(outputs) + if data_format == 'channels_first': + self.assertEqual(np_output.shape[2], length_row * input_num_row) + self.assertEqual(np_output.shape[3], length_col * input_num_col) + else: + self.assertEqual(np_output.shape[1], length_row * input_num_row) + self.assertEqual(np_output.shape[2], length_col * input_num_col) + + @tf_test_util.run_in_graph_and_eager_modes def test_upsampling_3d(self): num_samples = 2 stack_size = 2 diff --git a/tensorflow/python/kernel_tests/rnn_test.py b/tensorflow/python/kernel_tests/rnn_test.py index 2f6963f6b8..907e1277a9 100644 --- a/tensorflow/python/kernel_tests/rnn_test.py +++ b/tensorflow/python/kernel_tests/rnn_test.py @@ -571,8 +571,8 @@ class RNNTest(test.TestCase): cell.set_weights(tf_weights) [tf_out, tf_state] = sess.run([tf_out, tf_state], {inputs: x_train}) - self.assertAllClose(tf_out, k_out) - self.assertAllClose(tf_state, k_state) + self.assertAllClose(tf_out, k_out, atol=1e-5) + self.assertAllClose(tf_state, k_state, atol=1e-5) def testBasicLSTMCellInterchangeWithLSTMCell(self): with self.session(graph=ops_lib.Graph()) as sess: diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.backend.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.backend.pbtxt index 9feb7c09b8..5f0dfd7ae7 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.keras.backend.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.backend.pbtxt @@ -386,7 +386,7 @@ tf_module { } member_method { name: "resize_images" - argspec: "args=[\'x\', \'height_factor\', \'width_factor\', \'data_format\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'x\', \'height_factor\', \'width_factor\', \'data_format\', \'interpolation\'], varargs=None, keywords=None, defaults=[\'nearest\'], " } member_method { name: "resize_volumes" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-early-stopping.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-early-stopping.pbtxt index f71292856c..ed0f37647f 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-early-stopping.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-early-stopping.pbtxt @@ -5,7 +5,11 @@ tf_class { is_instance: "<type \'object\'>" member_method { name: "__init__" - argspec: "args=[\'self\', \'monitor\', \'min_delta\', \'patience\', \'verbose\', \'mode\', \'baseline\'], varargs=None, keywords=None, defaults=[\'val_loss\', \'0\', \'0\', \'0\', \'auto\', \'None\'], " + argspec: "args=[\'self\', \'monitor\', \'min_delta\', \'patience\', \'verbose\', \'mode\', \'baseline\', \'restore_best_weights\'], varargs=None, keywords=None, defaults=[\'val_loss\', \'0\', \'0\', \'0\', \'auto\', \'None\', \'False\'], " + } + member_method { + name: "get_monitor_value" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=None" } member_method { name: "on_batch_begin" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-tensor-board.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-tensor-board.pbtxt index e58ba18c1c..e9d53b7225 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-tensor-board.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-tensor-board.pbtxt @@ -5,7 +5,7 @@ tf_class { is_instance: "<type \'object\'>" member_method { name: "__init__" - argspec: "args=[\'self\', \'log_dir\', \'histogram_freq\', \'batch_size\', \'write_graph\', \'write_grads\', \'write_images\', \'embeddings_freq\', \'embeddings_layer_names\', \'embeddings_metadata\', \'embeddings_data\'], varargs=None, keywords=None, defaults=[\'./logs\', \'0\', \'32\', \'True\', \'False\', \'False\', \'0\', \'None\', \'None\', \'None\'], " + argspec: "args=[\'self\', \'log_dir\', \'histogram_freq\', \'batch_size\', \'write_graph\', \'write_grads\', \'write_images\', \'embeddings_freq\', \'embeddings_layer_names\', \'embeddings_metadata\', \'embeddings_data\', \'update_freq\'], varargs=None, keywords=None, defaults=[\'./logs\', \'0\', \'32\', \'True\', \'False\', \'False\', \'0\', \'None\', \'None\', \'None\', \'epoch\'], " } member_method { name: "on_batch_begin" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-up-sampling2-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-up-sampling2-d.pbtxt index 40a56a0c94..b05e5ec84d 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-up-sampling2-d.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-up-sampling2-d.pbtxt @@ -82,7 +82,7 @@ tf_class { } member_method { name: "__init__" - argspec: "args=[\'self\', \'size\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'(2, 2)\', \'None\'], " + argspec: "args=[\'self\', \'size\', \'data_format\', \'interpolation\'], varargs=None, keywords=kwargs, defaults=[\'(2, 2)\', \'None\', \'nearest\'], " } member_method { name: "add_loss" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.backend.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.backend.pbtxt index 9feb7c09b8..5f0dfd7ae7 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.keras.backend.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.backend.pbtxt @@ -386,7 +386,7 @@ tf_module { } member_method { name: "resize_images" - argspec: "args=[\'x\', \'height_factor\', \'width_factor\', \'data_format\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'x\', \'height_factor\', \'width_factor\', \'data_format\', \'interpolation\'], varargs=None, keywords=None, defaults=[\'nearest\'], " } member_method { name: "resize_volumes" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-early-stopping.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-early-stopping.pbtxt index f71292856c..ed0f37647f 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-early-stopping.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-early-stopping.pbtxt @@ -5,7 +5,11 @@ tf_class { is_instance: "<type \'object\'>" member_method { name: "__init__" - argspec: "args=[\'self\', \'monitor\', \'min_delta\', \'patience\', \'verbose\', \'mode\', \'baseline\'], varargs=None, keywords=None, defaults=[\'val_loss\', \'0\', \'0\', \'0\', \'auto\', \'None\'], " + argspec: "args=[\'self\', \'monitor\', \'min_delta\', \'patience\', \'verbose\', \'mode\', \'baseline\', \'restore_best_weights\'], varargs=None, keywords=None, defaults=[\'val_loss\', \'0\', \'0\', \'0\', \'auto\', \'None\', \'False\'], " + } + member_method { + name: "get_monitor_value" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=None" } member_method { name: "on_batch_begin" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-tensor-board.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-tensor-board.pbtxt index e58ba18c1c..e9d53b7225 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-tensor-board.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-tensor-board.pbtxt @@ -5,7 +5,7 @@ tf_class { is_instance: "<type \'object\'>" member_method { name: "__init__" - argspec: "args=[\'self\', \'log_dir\', \'histogram_freq\', \'batch_size\', \'write_graph\', \'write_grads\', \'write_images\', \'embeddings_freq\', \'embeddings_layer_names\', \'embeddings_metadata\', \'embeddings_data\'], varargs=None, keywords=None, defaults=[\'./logs\', \'0\', \'32\', \'True\', \'False\', \'False\', \'0\', \'None\', \'None\', \'None\'], " + argspec: "args=[\'self\', \'log_dir\', \'histogram_freq\', \'batch_size\', \'write_graph\', \'write_grads\', \'write_images\', \'embeddings_freq\', \'embeddings_layer_names\', \'embeddings_metadata\', \'embeddings_data\', \'update_freq\'], varargs=None, keywords=None, defaults=[\'./logs\', \'0\', \'32\', \'True\', \'False\', \'False\', \'0\', \'None\', \'None\', \'None\', \'epoch\'], " } member_method { name: "on_batch_begin" diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-up-sampling2-d.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-up-sampling2-d.pbtxt index 40a56a0c94..b05e5ec84d 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-up-sampling2-d.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-up-sampling2-d.pbtxt @@ -82,7 +82,7 @@ tf_class { } member_method { name: "__init__" - argspec: "args=[\'self\', \'size\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'(2, 2)\', \'None\'], " + argspec: "args=[\'self\', \'size\', \'data_format\', \'interpolation\'], varargs=None, keywords=kwargs, defaults=[\'(2, 2)\', \'None\', \'nearest\'], " } member_method { name: "add_loss" |