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Reintroduce API compatibility checks for Keras Preprocessing and Keras

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applications.
Export `tf.keras.preprocessing.image_dataset_from_directory` and
`tf.keras.preprocessing.timeseries_dataset_from_array` to the public API.

PiperOrigin-RevId: 305362523
Change-Id: Id131d4286b3b444ed1a38e2b11a9bd74fca83390
This commit is contained in:
Francois Chollet 2020-04-07 16:21:57 -07:00 committed by TensorFlower Gardener
parent 896efbf157
commit 756ba9c87e
56 changed files with 1444 additions and 67 deletions
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6
tensorflow/python/keras/preprocessing/BUILD
View File

@ -32,7 +32,7 @@ py_library(
name = "image",
srcs = [
"image.py",
"image_pipeline.py",
"image_dataset.py",
],
deps = [
"//tensorflow/python:util",
@ -87,9 +87,9 @@ tf_py_test(
)
tf_py_test(
name = "image_pipeline_test",
name = "image_dataset_test",
size = "small",
srcs = ["image_pipeline_test.py"],
srcs = ["image_dataset_test.py"],
python_version = "PY3",
deps = [
":image",

1
tensorflow/python/keras/preprocessing/__init__.py
View File

@ -26,6 +26,7 @@ from tensorflow.python.keras import backend
from tensorflow.python.keras.preprocessing import image
from tensorflow.python.keras.preprocessing import sequence
from tensorflow.python.keras.preprocessing import text
from tensorflow.python.keras.preprocessing import timeseries
from tensorflow.python.keras.utils import all_utils as utils
# This exists for compatibility with prior version of keras_preprocessing.

1
tensorflow/python/keras/preprocessing/image.py
View File

@ -28,6 +28,7 @@ except ImportError:
pass
from tensorflow.python.keras import backend
from tensorflow.python.keras.preprocessing.image_dataset import image_dataset_from_directory # pylint: disable=unused-import
from tensorflow.python.keras.utils import data_utils
from tensorflow.python.util import tf_inspect
from tensorflow.python.util.tf_export import keras_export

28
tensorflow/python/keras/preprocessing/image_pipeline.py → tensorflow/python/keras/preprocessing/image_dataset.py
View File

@ -35,20 +35,20 @@ from tensorflow.python.util.tf_export import keras_export
WHITELIST_FORMATS = ('.bmp', '.gif', '.jpeg', '.jpg', '.png')
@keras_export('keras.preprocessing.image.dataset_from_directory', v1=[])
def dataset_from_directory(directory,
labels='inferred',
label_mode='int',
class_names=None,
color_mode='rgb',
batch_size=32,
image_size=(256, 256),
shuffle=True,
seed=None,
validation_split=None,
subset=None,
interpolation='bilinear',
follow_links=False):
@keras_export('keras.preprocessing.image_dataset_from_directory', v1=[])
def image_dataset_from_directory(directory,
labels='inferred',
label_mode='int',
class_names=None,
color_mode='rgb',
batch_size=32,
image_size=(256, 256),
shuffle=True,
seed=None,
validation_split=None,
subset=None,
interpolation='bilinear',
follow_links=False):
"""Generates a Dataset from image files in a directory.
If your directory structure is:

69
tensorflow/python/keras/preprocessing/image_pipeline_test.py → tensorflow/python/keras/preprocessing/image_dataset_test.py
View File

@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for image_pipeline."""
"""Tests for image_dataset."""
from __future__ import absolute_import
from __future__ import division
@ -26,7 +26,7 @@ import numpy as np
from tensorflow.python.compat import v2_compat
from tensorflow.python.keras import keras_parameterized
from tensorflow.python.keras.preprocessing import image as image_preproc
from tensorflow.python.keras.preprocessing import image_pipeline
from tensorflow.python.keras.preprocessing import image_dataset
from tensorflow.python.platform import test
try:
@ -91,12 +91,12 @@ class DatasetFromDirectoryTest(keras_parameterized.TestCase):
i += 1
return temp_dir
def test_dataset_from_directory_binary(self):
def test_image_dataset_from_directory_binary(self):
if PIL is None:
return # Skip test if PIL is not available.
directory = self._prepare_directory(num_classes=2)
dataset = image_pipeline.dataset_from_directory(
dataset = image_dataset.image_dataset_from_directory(
directory, batch_size=8, image_size=(18, 18), label_mode='int')
batch = next(iter(dataset))
self.assertLen(batch, 2)
@ -105,7 +105,7 @@ class DatasetFromDirectoryTest(keras_parameterized.TestCase):
self.assertEqual(batch[1].shape, (8,))
self.assertEqual(batch[1].dtype.name, 'int32')
dataset = image_pipeline.dataset_from_directory(
dataset = image_dataset.image_dataset_from_directory(
directory, batch_size=8, image_size=(18, 18), label_mode='binary')
batch = next(iter(dataset))
self.assertLen(batch, 2)
@ -114,7 +114,7 @@ class DatasetFromDirectoryTest(keras_parameterized.TestCase):
self.assertEqual(batch[1].shape, (8, 1))
self.assertEqual(batch[1].dtype.name, 'float32')
dataset = image_pipeline.dataset_from_directory(
dataset = image_dataset.image_dataset_from_directory(
directory, batch_size=8, image_size=(18, 18), label_mode='categorical')
batch = next(iter(dataset))
self.assertLen(batch, 2)
@ -128,25 +128,25 @@ class DatasetFromDirectoryTest(keras_parameterized.TestCase):
return # Skip test if PIL is not available.
directory = self._prepare_directory(num_classes=4, count=15)
dataset = image_pipeline.dataset_from_directory(
dataset = image_dataset.image_dataset_from_directory(
directory, batch_size=8, image_size=(18, 18), label_mode=None)
sample_count = 0
for batch in dataset:
sample_count += batch.shape[0]
self.assertEqual(sample_count, 15)
def test_dataset_from_directory_multiclass(self):
def test_image_dataset_from_directory_multiclass(self):
if PIL is None:
return # Skip test if PIL is not available.
directory = self._prepare_directory(num_classes=4, count=15)
dataset = image_pipeline.dataset_from_directory(
dataset = image_dataset.image_dataset_from_directory(
directory, batch_size=8, image_size=(18, 18), label_mode=None)
batch = next(iter(dataset))
self.assertEqual(batch.shape, (8, 18, 18, 3))
dataset = image_pipeline.dataset_from_directory(
dataset = image_dataset.image_dataset_from_directory(
directory, batch_size=8, image_size=(18, 18), label_mode=None)
sample_count = 0
iterator = iter(dataset)
@ -154,7 +154,7 @@ class DatasetFromDirectoryTest(keras_parameterized.TestCase):
sample_count += next(iterator).shape[0]
self.assertEqual(sample_count, 15)
dataset = image_pipeline.dataset_from_directory(
dataset = image_dataset.image_dataset_from_directory(
directory, batch_size=8, image_size=(18, 18), label_mode='int')
batch = next(iter(dataset))
self.assertLen(batch, 2)
@ -163,7 +163,7 @@ class DatasetFromDirectoryTest(keras_parameterized.TestCase):
self.assertEqual(batch[1].shape, (8,))
self.assertEqual(batch[1].dtype.name, 'int32')
dataset = image_pipeline.dataset_from_directory(
dataset = image_dataset.image_dataset_from_directory(
directory, batch_size=8, image_size=(18, 18), label_mode='categorical')
batch = next(iter(dataset))
self.assertLen(batch, 2)
@ -172,12 +172,12 @@ class DatasetFromDirectoryTest(keras_parameterized.TestCase):
self.assertEqual(batch[1].shape, (8, 4))
self.assertEqual(batch[1].dtype.name, 'float32')
def test_dataset_from_directory_color_modes(self):
def test_image_dataset_from_directory_color_modes(self):
if PIL is None:
return # Skip test if PIL is not available.
directory = self._prepare_directory(num_classes=4, color_mode='rgba')
dataset = image_pipeline.dataset_from_directory(
dataset = image_dataset.image_dataset_from_directory(
directory, batch_size=8, image_size=(18, 18), color_mode='rgba')
batch = next(iter(dataset))
self.assertLen(batch, 2)
@ -185,50 +185,50 @@ class DatasetFromDirectoryTest(keras_parameterized.TestCase):
self.assertEqual(batch[0].dtype.name, 'float32')
directory = self._prepare_directory(num_classes=4, color_mode='grayscale')
dataset = image_pipeline.dataset_from_directory(
dataset = image_dataset.image_dataset_from_directory(
directory, batch_size=8, image_size=(18, 18), color_mode='grayscale')
batch = next(iter(dataset))
self.assertLen(batch, 2)
self.assertEqual(batch[0].shape, (8, 18, 18, 1))
self.assertEqual(batch[0].dtype.name, 'float32')
def test_dataset_from_directory_validation_split(self):
def test_image_dataset_from_directory_validation_split(self):
if PIL is None:
return # Skip test if PIL is not available.
directory = self._prepare_directory(num_classes=2, count=10)
dataset = image_pipeline.dataset_from_directory(
dataset = image_dataset.image_dataset_from_directory(
directory, batch_size=10, image_size=(18, 18),
validation_split=0.2, subset='training')
batch = next(iter(dataset))
self.assertLen(batch, 2)
self.assertEqual(batch[0].shape, (8, 18, 18, 3))
dataset = image_pipeline.dataset_from_directory(
dataset = image_dataset.image_dataset_from_directory(
directory, batch_size=10, image_size=(18, 18),
validation_split=0.2, subset='validation')
batch = next(iter(dataset))
self.assertLen(batch, 2)
self.assertEqual(batch[0].shape, (2, 18, 18, 3))
def test_dataset_from_directory_manual_labels(self):
def test_image_dataset_from_directory_manual_labels(self):
if PIL is None:
return # Skip test if PIL is not available.
directory = self._prepare_directory(num_classes=2, count=2)
dataset = image_pipeline.dataset_from_directory(
dataset = image_dataset.image_dataset_from_directory(
directory, batch_size=8, image_size=(18, 18),
labels=[0, 1], shuffle=False)
batch = next(iter(dataset))
self.assertLen(batch, 2)
self.assertAllClose(batch[1], [0, 1])
def test_dataset_from_directory_follow_links(self):
def test_image_dataset_from_directory_follow_links(self):
if PIL is None:
return # Skip test if PIL is not available.
directory = self._prepare_directory(num_classes=2, count=25,
nested_dirs=True)
dataset = image_pipeline.dataset_from_directory(
dataset = image_dataset.image_dataset_from_directory(
directory, batch_size=8, image_size=(18, 18), label_mode=None,
follow_links=True)
sample_count = 0
@ -236,48 +236,53 @@ class DatasetFromDirectoryTest(keras_parameterized.TestCase):
sample_count += batch.shape[0]
self.assertEqual(sample_count, 25)
def test_dataset_from_directory_errors(self):
def test_image_dataset_from_directory_errors(self):
if PIL is None:
return # Skip test if PIL is not available.
directory = self._prepare_directory(num_classes=3, count=5)
with self.assertRaisesRegex(ValueError, '`labels` argument should be'):
_ = image_pipeline.dataset_from_directory(
_ = image_dataset.image_dataset_from_directory(
directory, labels=None)
with self.assertRaisesRegex(ValueError, '`label_mode` argument must be'):
_ = image_pipeline.dataset_from_directory(directory, label_mode='other')
_ = image_dataset.image_dataset_from_directory(
directory, label_mode='other')
with self.assertRaisesRegex(ValueError, '`color_mode` must be one of'):
_ = image_pipeline.dataset_from_directory(directory, color_mode='other')
_ = image_dataset.image_dataset_from_directory(
directory, color_mode='other')
with self.assertRaisesRegex(
ValueError, 'only pass `class_names` if the labels are inferred'):
_ = image_pipeline.dataset_from_directory(
_ = image_dataset.image_dataset_from_directory(
directory, labels=[0, 0, 1, 1, 1],
class_names=['class_0', 'class_1', 'class_2'])
with self.assertRaisesRegex(
ValueError,
'Expected the lengths of `labels` to match the number of images'):
_ = image_pipeline.dataset_from_directory(directory, labels=[0, 0, 1, 1])
_ = image_dataset.image_dataset_from_directory(
directory, labels=[0, 0, 1, 1])
with self.assertRaisesRegex(
ValueError, '`class_names` passed did not match'):
_ = image_pipeline.dataset_from_directory(
_ = image_dataset.image_dataset_from_directory(
directory, class_names=['class_0', 'class_2'])
with self.assertRaisesRegex(ValueError, 'there must exactly 2 classes'):
_ = image_pipeline.dataset_from_directory(directory, label_mode='binary')
_ = image_dataset.image_dataset_from_directory(
directory, label_mode='binary')
with self.assertRaisesRegex(ValueError,
'`validation_split` must be between 0 and 1'):
_ = image_pipeline.dataset_from_directory(directory, validation_split=2)
_ = image_dataset.image_dataset_from_directory(
directory, validation_split=2)
with self.assertRaisesRegex(ValueError,
'`subset` must be either "training" or'):
_ = image_pipeline.dataset_from_directory(
_ = image_dataset.image_dataset_from_directory(
directory, validation_split=0.2, subset='other')

4
tensorflow/python/keras/preprocessing/timeseries.py
View File

@ -26,8 +26,8 @@ from tensorflow.python.ops import math_ops
from tensorflow.python.util.tf_export import keras_export
@keras_export('keras.preprocessing.timeseries.dataset_from_array', v1=[])
def dataset_from_array(
@keras_export('keras.preprocessing.timeseries_dataset_from_array', v1=[])
def timeseries_dataset_from_array(
data,
targets,
sequence_length,

34
tensorflow/python/keras/preprocessing/timeseries_test.py
View File

@ -31,7 +31,7 @@ class TimeseriesDatasetTest(test.TestCase):
# Test ordering, targets, sequence length, batch size
data = np.arange(100)
targets = data * 2
dataset = timeseries.dataset_from_array(
dataset = timeseries.timeseries_dataset_from_array(
data, targets, sequence_length=9, batch_size=5)
# Expect 19 batches
for i, batch in enumerate(dataset):
@ -50,7 +50,7 @@ class TimeseriesDatasetTest(test.TestCase):
def test_no_targets(self):
data = np.arange(50)
dataset = timeseries.dataset_from_array(
dataset = timeseries.timeseries_dataset_from_array(
data, None, sequence_length=10, batch_size=5)
# Expect 9 batches
i = None
@ -68,7 +68,7 @@ class TimeseriesDatasetTest(test.TestCase):
# Test cross-epoch random order and seed determinism
data = np.arange(10)
targets = data * 2
dataset = timeseries.dataset_from_array(
dataset = timeseries.timeseries_dataset_from_array(
data, targets, sequence_length=5, batch_size=1, shuffle=True, seed=123)
first_seq = None
for x, y in dataset.take(1):
@ -79,7 +79,7 @@ class TimeseriesDatasetTest(test.TestCase):
for x, _ in dataset.take(1):
self.assertNotAllClose(x, first_seq)
# Check determism with same seed
dataset = timeseries.dataset_from_array(
dataset = timeseries.timeseries_dataset_from_array(
data, targets, sequence_length=5, batch_size=1, shuffle=True, seed=123)
for x, _ in dataset.take(1):
self.assertAllClose(x, first_seq)
@ -87,7 +87,7 @@ class TimeseriesDatasetTest(test.TestCase):
def test_sampling_rate(self):
data = np.arange(100)
targets = data * 2
dataset = timeseries.dataset_from_array(
dataset = timeseries.timeseries_dataset_from_array(
data, targets, sequence_length=9, batch_size=5, sampling_rate=2)
for i, batch in enumerate(dataset):
self.assertLen(batch, 2)
@ -108,7 +108,7 @@ class TimeseriesDatasetTest(test.TestCase):
def test_sequence_stride(self):
data = np.arange(100)
targets = data * 2
dataset = timeseries.dataset_from_array(
dataset = timeseries.timeseries_dataset_from_array(
data, targets, sequence_length=9, batch_size=5, sequence_stride=3)
for i, batch in enumerate(dataset):
self.assertLen(batch, 2)
@ -129,7 +129,7 @@ class TimeseriesDatasetTest(test.TestCase):
def test_start_and_end_index(self):
data = np.arange(100)
dataset = timeseries.dataset_from_array(
dataset = timeseries.timeseries_dataset_from_array(
data, None,
sequence_length=9, batch_size=5, sequence_stride=3, sampling_rate=2,
start_index=10, end_index=90)
@ -141,23 +141,29 @@ class TimeseriesDatasetTest(test.TestCase):
# bad targets
with self.assertRaisesRegex(ValueError,
'data and targets to have the same number'):
_ = timeseries.dataset_from_array(np.arange(10), np.arange(9), 3)
_ = timeseries.timeseries_dataset_from_array(
np.arange(10), np.arange(9), 3)
# bad start index
with self.assertRaisesRegex(ValueError, 'start_index must be '):
_ = timeseries.dataset_from_array(np.arange(10), None, 3, start_index=-1)
_ = timeseries.timeseries_dataset_from_array(
np.arange(10), None, 3, start_index=-1)
with self.assertRaisesRegex(ValueError, 'start_index must be '):
_ = timeseries.dataset_from_array(np.arange(10), None, 3, start_index=11)
_ = timeseries.timeseries_dataset_from_array(
np.arange(10), None, 3, start_index=11)
# bad end index
with self.assertRaisesRegex(ValueError, 'end_index must be '):
_ = timeseries.dataset_from_array(np.arange(10), None, 3, end_index=-1)
_ = timeseries.timeseries_dataset_from_array(
np.arange(10), None, 3, end_index=-1)
with self.assertRaisesRegex(ValueError, 'end_index must be '):
_ = timeseries.dataset_from_array(np.arange(10), None, 3, end_index=11)
_ = timeseries.timeseries_dataset_from_array(
np.arange(10), None, 3, end_index=11)
# bad sampling_rate
with self.assertRaisesRegex(ValueError, 'sampling_rate must be '):
_ = timeseries.dataset_from_array(np.arange(10), None, 3, sampling_rate=0)
_ = timeseries.timeseries_dataset_from_array(
np.arange(10), None, 3, sampling_rate=0)
# bad sequence stride
with self.assertRaisesRegex(ValueError, 'sequence_stride must be '):
_ = timeseries.dataset_from_array(
_ = timeseries.timeseries_dataset_from_array(
np.arange(10), None, 3, sequence_stride=0)

23
tensorflow/tools/api/golden/v1/tensorflow.keras.applications.densenet.pbtxt Normal file
View File

@ -0,0 +1,23 @@
path: "tensorflow.keras.applications.densenet"
tf_module {
member_method {
name: "DenseNet121"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "DenseNet169"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "DenseNet201"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "decode_predictions"
argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], "
}
member_method {
name: "preprocess_input"
argspec: "args=[\'x\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
}

11
tensorflow/tools/api/golden/v1/tensorflow.keras.applications.imagenet_utils.pbtxt Normal file
View File

@ -0,0 +1,11 @@
path: "tensorflow.keras.applications.imagenet_utils"
tf_module {
member_method {
name: "decode_predictions"
argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], "
}
member_method {
name: "preprocess_input"
argspec: "args=[\'x\', \'data_format\', \'mode\'], varargs=None, keywords=None, defaults=[\'None\', \'caffe\'], "
}
}

15
tensorflow/tools/api/golden/v1/tensorflow.keras.applications.inception_resnet_v2.pbtxt Normal file
View File

@ -0,0 +1,15 @@
path: "tensorflow.keras.applications.inception_resnet_v2"
tf_module {
member_method {
name: "InceptionResNetV2"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=kwargs, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "decode_predictions"
argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], "
}
member_method {
name: "preprocess_input"
argspec: "args=[\'x\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
}

15
tensorflow/tools/api/golden/v1/tensorflow.keras.applications.inception_v3.pbtxt Normal file
View File

@ -0,0 +1,15 @@
path: "tensorflow.keras.applications.inception_v3"
tf_module {
member_method {
name: "InceptionV3"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "decode_predictions"
argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], "
}
member_method {
name: "preprocess_input"
argspec: "args=[\'x\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
}

15
tensorflow/tools/api/golden/v1/tensorflow.keras.applications.mobilenet.pbtxt Normal file
View File

@ -0,0 +1,15 @@
path: "tensorflow.keras.applications.mobilenet"
tf_module {
member_method {
name: "MobileNet"
argspec: "args=[\'input_shape\', \'alpha\', \'depth_multiplier\', \'dropout\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'1.0\', \'1\', \'0.001\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "decode_predictions"
argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], "
}
member_method {
name: "preprocess_input"
argspec: "args=[\'x\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
}

15
tensorflow/tools/api/golden/v1/tensorflow.keras.applications.mobilenet_v2.pbtxt Normal file
View File

@ -0,0 +1,15 @@
path: "tensorflow.keras.applications.mobilenet_v2"
tf_module {
member_method {
name: "MobileNetV2"
argspec: "args=[\'input_shape\', \'alpha\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'1.0\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "decode_predictions"
argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], "
}
member_method {
name: "preprocess_input"
argspec: "args=[\'x\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
}

19
tensorflow/tools/api/golden/v1/tensorflow.keras.applications.nasnet.pbtxt Normal file
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@ -0,0 +1,19 @@
path: "tensorflow.keras.applications.nasnet"
tf_module {
member_method {
name: "NASNetLarge"
argspec: "args=[\'input_shape\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "NASNetMobile"
argspec: "args=[\'input_shape\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "decode_predictions"
argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], "
}
member_method {
name: "preprocess_input"
argspec: "args=[\'x\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
}

127
tensorflow/tools/api/golden/v1/tensorflow.keras.applications.pbtxt Normal file
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@ -0,0 +1,127 @@
path: "tensorflow.keras.applications"
tf_module {
member {
name: "densenet"
mtype: "<type \'module\'>"
}
member {
name: "imagenet_utils"
mtype: "<type \'module\'>"
}
member {
name: "inception_resnet_v2"
mtype: "<type \'module\'>"
}
member {
name: "inception_v3"
mtype: "<type \'module\'>"
}
member {
name: "mobilenet"
mtype: "<type \'module\'>"
}
member {
name: "mobilenet_v2"
mtype: "<type \'module\'>"
}
member {
name: "nasnet"
mtype: "<type \'module\'>"
}
member {
name: "resnet"
mtype: "<type \'module\'>"
}
member {
name: "resnet50"
mtype: "<type \'module\'>"
}
member {
name: "resnet_v2"
mtype: "<type \'module\'>"
}
member {
name: "vgg16"
mtype: "<type \'module\'>"
}
member {
name: "vgg19"
mtype: "<type \'module\'>"
}
member {
name: "xception"
mtype: "<type \'module\'>"
}
member_method {
name: "DenseNet121"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "DenseNet169"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "DenseNet201"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "InceptionResNetV2"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=kwargs, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "InceptionV3"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "MobileNet"
argspec: "args=[\'input_shape\', \'alpha\', \'depth_multiplier\', \'dropout\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'1.0\', \'1\', \'0.001\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "MobileNetV2"
argspec: "args=[\'input_shape\', \'alpha\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'1.0\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "NASNetLarge"
argspec: "args=[\'input_shape\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "NASNetMobile"
argspec: "args=[\'input_shape\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "ResNet101"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=kwargs, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "ResNet101V2"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "ResNet152"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=kwargs, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "ResNet152V2"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "ResNet50"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=kwargs, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "ResNet50V2"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "VGG16"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "VGG19"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "Xception"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
}

23
tensorflow/tools/api/golden/v1/tensorflow.keras.applications.resnet.pbtxt Normal file
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@ -0,0 +1,23 @@
path: "tensorflow.keras.applications.resnet"
tf_module {
member_method {
name: "ResNet101"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=kwargs, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "ResNet152"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=kwargs, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "ResNet50"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=kwargs, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "decode_predictions"
argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], "
}
member_method {
name: "preprocess_input"
argspec: "args=[\'x\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
}

15
tensorflow/tools/api/golden/v1/tensorflow.keras.applications.resnet50.pbtxt Normal file
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@ -0,0 +1,15 @@
path: "tensorflow.keras.applications.resnet50"
tf_module {
member_method {
name: "ResNet50"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=kwargs, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "decode_predictions"
argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], "
}
member_method {
name: "preprocess_input"
argspec: "args=[\'x\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
}

23
tensorflow/tools/api/golden/v1/tensorflow.keras.applications.resnet_v2.pbtxt Normal file
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@ -0,0 +1,23 @@
path: "tensorflow.keras.applications.resnet_v2"
tf_module {
member_method {
name: "ResNet101V2"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "ResNet152V2"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "ResNet50V2"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "decode_predictions"
argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], "
}
member_method {
name: "preprocess_input"
argspec: "args=[\'x\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
}

15
tensorflow/tools/api/golden/v1/tensorflow.keras.applications.vgg16.pbtxt Normal file
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@ -0,0 +1,15 @@
path: "tensorflow.keras.applications.vgg16"
tf_module {
member_method {
name: "VGG16"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "decode_predictions"
argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], "
}
member_method {
name: "preprocess_input"
argspec: "args=[\'x\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
}

15
tensorflow/tools/api/golden/v1/tensorflow.keras.applications.vgg19.pbtxt Normal file
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@ -0,0 +1,15 @@
path: "tensorflow.keras.applications.vgg19"
tf_module {
member_method {
name: "VGG19"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "decode_predictions"
argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], "
}
member_method {
name: "preprocess_input"
argspec: "args=[\'x\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
}

15
tensorflow/tools/api/golden/v1/tensorflow.keras.applications.xception.pbtxt Normal file
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@ -0,0 +1,15 @@
path: "tensorflow.keras.applications.xception"
tf_module {
member_method {
name: "Xception"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "decode_predictions"
argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], "
}
member_method {
name: "preprocess_input"
argspec: "args=[\'x\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
}

45
tensorflow/tools/api/golden/v1/tensorflow.keras.preprocessing.image.-directory-iterator.pbtxt Normal file
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@ -0,0 +1,45 @@
path: "tensorflow.keras.preprocessing.image.DirectoryIterator"
tf_class {
is_instance: "<class \'tensorflow.python.keras.preprocessing.image.DirectoryIterator\'>"
is_instance: "<class \'keras_preprocessing.image.directory_iterator.DirectoryIterator\'>"
member {
name: "allowed_class_modes"
mtype: "<type \'set\'>"
}
member {
name: "filepaths"
mtype: "<type \'property\'>"
}
member {
name: "labels"
mtype: "<type \'property\'>"
}
member {
name: "sample_weight"
mtype: "<type \'property\'>"
}
member {
name: "white_list_formats"
mtype: "<type \'tuple\'>"
}
member_method {
name: "__init__"
argspec: "args=[\'self\', \'directory\', \'image_data_generator\', \'target_size\', \'color_mode\', \'classes\', \'class_mode\', \'batch_size\', \'shuffle\', \'seed\', \'data_format\', \'save_to_dir\', \'save_prefix\', \'save_format\', \'follow_links\', \'subset\', \'interpolation\', \'dtype\'], varargs=None, keywords=None, defaults=[\'(256, 256)\', \'rgb\', \'None\', \'categorical\', \'32\', \'True\', \'None\', \'None\', \'None\', \'\', \'png\', \'False\', \'None\', \'nearest\', \'None\'], "
}
member_method {
name: "next"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "on_epoch_end"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "reset"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "set_processing_attrs"
argspec: "args=[\'self\', \'image_data_generator\', \'target_size\', \'color_mode\', \'data_format\', \'save_to_dir\', \'save_prefix\', \'save_format\', \'subset\', \'interpolation\'], varargs=None, keywords=None, defaults=None"
}
}

41
tensorflow/tools/api/golden/v1/tensorflow.keras.preprocessing.image.-image-data-generator.pbtxt Normal file
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@ -0,0 +1,41 @@
path: "tensorflow.keras.preprocessing.image.ImageDataGenerator"
tf_class {
is_instance: "<class \'tensorflow.python.keras.preprocessing.image.ImageDataGenerator\'>"
is_instance: "<class \'keras_preprocessing.image.image_data_generator.ImageDataGenerator\'>"
member_method {
name: "__init__"
argspec: "args=[\'self\', \'featurewise_center\', \'samplewise_center\', \'featurewise_std_normalization\', \'samplewise_std_normalization\', \'zca_whitening\', \'zca_epsilon\', \'rotation_range\', \'width_shift_range\', \'height_shift_range\', \'brightness_range\', \'shear_range\', \'zoom_range\', \'channel_shift_range\', \'fill_mode\', \'cval\', \'horizontal_flip\', \'vertical_flip\', \'rescale\', \'preprocessing_function\', \'data_format\', \'validation_split\', \'dtype\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'False\', \'False\', \'False\', \'1e-06\', \'0\', \'0.0\', \'0.0\', \'None\', \'0.0\', \'0.0\', \'0.0\', \'nearest\', \'0.0\', \'False\', \'False\', \'None\', \'None\', \'None\', \'0.0\', \'None\'], "
}
member_method {
name: "apply_transform"
argspec: "args=[\'self\', \'x\', \'transform_parameters\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "fit"
argspec: "args=[\'self\', \'x\', \'augment\', \'rounds\', \'seed\'], varargs=None, keywords=None, defaults=[\'False\', \'1\', \'None\'], "
}
member_method {
name: "flow"
argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'shuffle\', \'sample_weight\', \'seed\', \'save_to_dir\', \'save_prefix\', \'save_format\', \'subset\'], varargs=None, keywords=None, defaults=[\'None\', \'32\', \'True\', \'None\', \'None\', \'None\', \'\', \'png\', \'None\'], "
}
member_method {
name: "flow_from_dataframe"
argspec: "args=[\'self\', \'dataframe\', \'directory\', \'x_col\', \'y_col\', \'weight_col\', \'target_size\', \'color_mode\', \'classes\', \'class_mode\', \'batch_size\', \'shuffle\', \'seed\', \'save_to_dir\', \'save_prefix\', \'save_format\', \'subset\', \'interpolation\', \'validate_filenames\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'filename\', \'class\', \'None\', \'(256, 256)\', \'rgb\', \'None\', \'categorical\', \'32\', \'True\', \'None\', \'None\', \'\', \'png\', \'None\', \'nearest\', \'True\'], "
}
member_method {
name: "flow_from_directory"
argspec: "args=[\'self\', \'directory\', \'target_size\', \'color_mode\', \'classes\', \'class_mode\', \'batch_size\', \'shuffle\', \'seed\', \'save_to_dir\', \'save_prefix\', \'save_format\', \'follow_links\', \'subset\', \'interpolation\'], varargs=None, keywords=None, defaults=[\'(256, 256)\', \'rgb\', \'None\', \'categorical\', \'32\', \'True\', \'None\', \'None\', \'\', \'png\', \'False\', \'None\', \'nearest\'], "
}
member_method {
name: "get_random_transform"
argspec: "args=[\'self\', \'img_shape\', \'seed\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "random_transform"
argspec: "args=[\'self\', \'x\', \'seed\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "standardize"
argspec: "args=[\'self\', \'x\'], varargs=None, keywords=None, defaults=None"
}
}

25
tensorflow/tools/api/golden/v1/tensorflow.keras.preprocessing.image.-iterator.pbtxt Normal file
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@ -0,0 +1,25 @@
path: "tensorflow.keras.preprocessing.image.Iterator"
tf_class {
is_instance: "<class \'tensorflow.python.keras.preprocessing.image.Iterator\'>"
is_instance: "<class \'keras_preprocessing.image.iterator.Iterator\'>"
member {
name: "white_list_formats"
mtype: "<type \'tuple\'>"
}
member_method {
name: "__init__"
argspec: "args=[\'self\', \'n\', \'batch_size\', \'shuffle\', \'seed\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "next"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "on_epoch_end"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "reset"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
}

25
tensorflow/tools/api/golden/v1/tensorflow.keras.preprocessing.image.-numpy-array-iterator.pbtxt Normal file
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@ -0,0 +1,25 @@
path: "tensorflow.keras.preprocessing.image.NumpyArrayIterator"
tf_class {
is_instance: "<class \'tensorflow.python.keras.preprocessing.image.NumpyArrayIterator\'>"
is_instance: "<class \'keras_preprocessing.image.numpy_array_iterator.NumpyArrayIterator\'>"
member {
name: "white_list_formats"
mtype: "<type \'tuple\'>"
}
member_method {
name: "__init__"
argspec: "args=[\'self\', \'x\', \'y\', \'image_data_generator\', \'batch_size\', \'shuffle\', \'sample_weight\', \'seed\', \'data_format\', \'save_to_dir\', \'save_prefix\', \'save_format\', \'subset\', \'dtype\'], varargs=None, keywords=None, defaults=[\'32\', \'False\', \'None\', \'None\', \'None\', \'None\', \'\', \'png\', \'None\', \'None\'], "
}
member_method {
name: "next"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "on_epoch_end"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "reset"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
}

71
tensorflow/tools/api/golden/v1/tensorflow.keras.preprocessing.image.pbtxt Normal file
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@ -0,0 +1,71 @@
path: "tensorflow.keras.preprocessing.image"
tf_module {
member {
name: "DirectoryIterator"
mtype: "<type \'type\'>"
}
member {
name: "ImageDataGenerator"
mtype: "<type \'type\'>"
}
member {
name: "Iterator"
mtype: "<type \'type\'>"
}
member {
name: "NumpyArrayIterator"
mtype: "<type \'type\'>"
}
member_method {
name: "apply_affine_transform"
argspec: "args=[\'x\', \'theta\', \'tx\', \'ty\', \'shear\', \'zx\', \'zy\', \'row_axis\', \'col_axis\', \'channel_axis\', \'fill_mode\', \'cval\', \'order\'], varargs=None, keywords=None, defaults=[\'0\', \'0\', \'0\', \'0\', \'1\', \'1\', \'0\', \'1\', \'2\', \'nearest\', \'0.0\', \'1\'], "
}
member_method {
name: "apply_brightness_shift"
argspec: "args=[\'x\', \'brightness\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "apply_channel_shift"
argspec: "args=[\'x\', \'intensity\', \'channel_axis\'], varargs=None, keywords=None, defaults=[\'0\'], "
}
member_method {
name: "array_to_img"
argspec: "args=[\'x\', \'data_format\', \'scale\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'None\'], "
}
member_method {
name: "img_to_array"
argspec: "args=[\'img\', \'data_format\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "load_img"
argspec: "args=[\'path\', \'grayscale\', \'color_mode\', \'target_size\', \'interpolation\'], varargs=None, keywords=None, defaults=[\'False\', \'rgb\', \'None\', \'nearest\'], "
}
member_method {
name: "random_brightness"
argspec: "args=[\'x\', \'brightness_range\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "random_channel_shift"
argspec: "args=[\'x\', \'intensity_range\', \'channel_axis\'], varargs=None, keywords=None, defaults=[\'0\'], "
}
member_method {
name: "random_rotation"
argspec: "args=[\'x\', \'rg\', \'row_axis\', \'col_axis\', \'channel_axis\', \'fill_mode\', \'cval\', \'interpolation_order\'], varargs=None, keywords=None, defaults=[\'1\', \'2\', \'0\', \'nearest\', \'0.0\', \'1\'], "
}
member_method {
name: "random_shear"
argspec: "args=[\'x\', \'intensity\', \'row_axis\', \'col_axis\', \'channel_axis\', \'fill_mode\', \'cval\', \'interpolation_order\'], varargs=None, keywords=None, defaults=[\'1\', \'2\', \'0\', \'nearest\', \'0.0\', \'1\'], "
}
member_method {
name: "random_shift"
argspec: "args=[\'x\', \'wrg\', \'hrg\', \'row_axis\', \'col_axis\', \'channel_axis\', \'fill_mode\', \'cval\', \'interpolation_order\'], varargs=None, keywords=None, defaults=[\'1\', \'2\', \'0\', \'nearest\', \'0.0\', \'1\'], "
}
member_method {
name: "random_zoom"
argspec: "args=[\'x\', \'zoom_range\', \'row_axis\', \'col_axis\', \'channel_axis\', \'fill_mode\', \'cval\', \'interpolation_order\'], varargs=None, keywords=None, defaults=[\'1\', \'2\', \'0\', \'nearest\', \'0.0\', \'1\'], "
}
member_method {
name: "save_img"
argspec: "args=[\'path\', \'x\', \'data_format\', \'file_format\', \'scale\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'True\'], "
}
}

15
tensorflow/tools/api/golden/v1/tensorflow.keras.preprocessing.pbtxt Normal file
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@ -0,0 +1,15 @@
path: "tensorflow.keras.preprocessing"
tf_module {
member {
name: "image"
mtype: "<type \'module\'>"
}
member {
name: "sequence"
mtype: "<type \'module\'>"
}
member {
name: "text"
mtype: "<type \'module\'>"
}
}

21
tensorflow/tools/api/golden/v1/tensorflow.keras.preprocessing.sequence.-timeseries-generator.pbtxt Normal file
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@ -0,0 +1,21 @@
path: "tensorflow.keras.preprocessing.sequence.TimeseriesGenerator"
tf_class {
is_instance: "<class \'tensorflow.python.keras.preprocessing.sequence.TimeseriesGenerator\'>"
is_instance: "<class \'keras_preprocessing.sequence.TimeseriesGenerator\'>"
member_method {
name: "__init__"
argspec: "args=[\'self\', \'data\', \'targets\', \'length\', \'sampling_rate\', \'stride\', \'start_index\', \'end_index\', \'shuffle\', \'reverse\', \'batch_size\'], varargs=None, keywords=None, defaults=[\'1\', \'1\', \'0\', \'None\', \'False\', \'False\', \'128\'], "
}
member_method {
name: "get_config"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "on_epoch_end"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "to_json"
argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None"
}
}

19
tensorflow/tools/api/golden/v1/tensorflow.keras.preprocessing.sequence.pbtxt Normal file
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@ -0,0 +1,19 @@
path: "tensorflow.keras.preprocessing.sequence"
tf_module {
member {
name: "TimeseriesGenerator"
mtype: "<type \'type\'>"
}
member_method {
name: "make_sampling_table"
argspec: "args=[\'size\', \'sampling_factor\'], varargs=None, keywords=None, defaults=[\'1e-05\'], "
}
member_method {
name: "pad_sequences"
argspec: "args=[\'sequences\', \'maxlen\', \'dtype\', \'padding\', \'truncating\', \'value\'], varargs=None, keywords=None, defaults=[\'None\', \'int32\', \'pre\', \'pre\', \'0.0\'], "
}
member_method {
name: "skipgrams"
argspec: "args=[\'sequence\', \'vocabulary_size\', \'window_size\', \'negative_samples\', \'shuffle\', \'categorical\', \'sampling_table\', \'seed\'], varargs=None, keywords=None, defaults=[\'4\', \'1.0\', \'True\', \'False\', \'None\', \'None\'], "
}
}

48
tensorflow/tools/api/golden/v1/tensorflow.keras.preprocessing.text.-tokenizer.pbtxt Normal file
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@ -0,0 +1,48 @@
path: "tensorflow.keras.preprocessing.text.Tokenizer"
tf_class {
is_instance: "<class \'keras_preprocessing.text.Tokenizer\'>"
member_method {
name: "__init__"
argspec: "args=[\'self\', \'num_words\', \'filters\', \'lower\', \'split\', \'char_level\', \'oov_token\', \'document_count\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n\', \'True\', \' \', \'False\', \'None\', \'0\'], "
}
member_method {
name: "fit_on_sequences"
argspec: "args=[\'self\', \'sequences\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "fit_on_texts"
argspec: "args=[\'self\', \'texts\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_config"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "sequences_to_matrix"
argspec: "args=[\'self\', \'sequences\', \'mode\'], varargs=None, keywords=None, defaults=[\'binary\'], "
}
member_method {
name: "sequences_to_texts"
argspec: "args=[\'self\', \'sequences\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "sequences_to_texts_generator"
argspec: "args=[\'self\', \'sequences\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "texts_to_matrix"
argspec: "args=[\'self\', \'texts\', \'mode\'], varargs=None, keywords=None, defaults=[\'binary\'], "
}
member_method {
name: "texts_to_sequences"
argspec: "args=[\'self\', \'texts\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "texts_to_sequences_generator"
argspec: "args=[\'self\', \'texts\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "to_json"
argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None"
}
}

23
tensorflow/tools/api/golden/v1/tensorflow.keras.preprocessing.text.pbtxt Normal file
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@ -0,0 +1,23 @@
path: "tensorflow.keras.preprocessing.text"
tf_module {
member {
name: "Tokenizer"
mtype: "<type \'type\'>"
}
member_method {
name: "hashing_trick"
argspec: "args=[\'text\', \'n\', \'hash_function\', \'filters\', \'lower\', \'split\'], varargs=None, keywords=None, defaults=[\'None\', \'!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n\', \'True\', \' \'], "
}
member_method {
name: "one_hot"
argspec: "args=[\'input_text\', \'n\', \'filters\', \'lower\', \'split\'], varargs=None, keywords=None, defaults=[\'!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n\', \'True\', \' \'], "
}
member_method {
name: "text_to_word_sequence"
argspec: "args=[\'input_text\', \'filters\', \'lower\', \'split\'], varargs=None, keywords=None, defaults=[\'!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n\', \'True\', \' \'], "
}
member_method {
name: "tokenizer_from_json"
argspec: "args=[\'json_string\'], varargs=None, keywords=None, defaults=None"
}
}

23
tensorflow/tools/api/golden/v2/tensorflow.keras.applications.densenet.pbtxt Normal file
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@ -0,0 +1,23 @@
path: "tensorflow.keras.applications.densenet"
tf_module {
member_method {
name: "DenseNet121"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "DenseNet169"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "DenseNet201"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "decode_predictions"
argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], "
}
member_method {
name: "preprocess_input"
argspec: "args=[\'x\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
}

11
tensorflow/tools/api/golden/v2/tensorflow.keras.applications.imagenet_utils.pbtxt Normal file
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@ -0,0 +1,11 @@
path: "tensorflow.keras.applications.imagenet_utils"
tf_module {
member_method {
name: "decode_predictions"
argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], "
}
member_method {
name: "preprocess_input"
argspec: "args=[\'x\', \'data_format\', \'mode\'], varargs=None, keywords=None, defaults=[\'None\', \'caffe\'], "
}
}

15
tensorflow/tools/api/golden/v2/tensorflow.keras.applications.inception_resnet_v2.pbtxt Normal file
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@ -0,0 +1,15 @@
path: "tensorflow.keras.applications.inception_resnet_v2"
tf_module {
member_method {
name: "InceptionResNetV2"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=kwargs, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "decode_predictions"
argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], "
}
member_method {
name: "preprocess_input"
argspec: "args=[\'x\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
}

15
tensorflow/tools/api/golden/v2/tensorflow.keras.applications.inception_v3.pbtxt Normal file
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@ -0,0 +1,15 @@
path: "tensorflow.keras.applications.inception_v3"
tf_module {
member_method {
name: "InceptionV3"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "decode_predictions"
argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], "
}
member_method {
name: "preprocess_input"
argspec: "args=[\'x\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
}

15
tensorflow/tools/api/golden/v2/tensorflow.keras.applications.mobilenet.pbtxt Normal file
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@ -0,0 +1,15 @@
path: "tensorflow.keras.applications.mobilenet"
tf_module {
member_method {
name: "MobileNet"
argspec: "args=[\'input_shape\', \'alpha\', \'depth_multiplier\', \'dropout\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'1.0\', \'1\', \'0.001\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "decode_predictions"
argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], "
}
member_method {
name: "preprocess_input"
argspec: "args=[\'x\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
}

15
tensorflow/tools/api/golden/v2/tensorflow.keras.applications.mobilenet_v2.pbtxt Normal file
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@ -0,0 +1,15 @@
path: "tensorflow.keras.applications.mobilenet_v2"
tf_module {
member_method {
name: "MobileNetV2"
argspec: "args=[\'input_shape\', \'alpha\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'1.0\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "decode_predictions"
argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], "
}
member_method {
name: "preprocess_input"
argspec: "args=[\'x\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
}

19
tensorflow/tools/api/golden/v2/tensorflow.keras.applications.nasnet.pbtxt Normal file
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@ -0,0 +1,19 @@
path: "tensorflow.keras.applications.nasnet"
tf_module {
member_method {
name: "NASNetLarge"
argspec: "args=[\'input_shape\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "NASNetMobile"
argspec: "args=[\'input_shape\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "decode_predictions"
argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], "
}
member_method {
name: "preprocess_input"
argspec: "args=[\'x\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
}

127
tensorflow/tools/api/golden/v2/tensorflow.keras.applications.pbtxt Normal file
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@ -0,0 +1,127 @@
path: "tensorflow.keras.applications"
tf_module {
member {
name: "densenet"
mtype: "<type \'module\'>"
}
member {
name: "imagenet_utils"
mtype: "<type \'module\'>"
}
member {
name: "inception_resnet_v2"
mtype: "<type \'module\'>"
}
member {
name: "inception_v3"
mtype: "<type \'module\'>"
}
member {
name: "mobilenet"
mtype: "<type \'module\'>"
}
member {
name: "mobilenet_v2"
mtype: "<type \'module\'>"
}
member {
name: "nasnet"
mtype: "<type \'module\'>"
}
member {
name: "resnet"
mtype: "<type \'module\'>"
}
member {
name: "resnet50"
mtype: "<type \'module\'>"
}
member {
name: "resnet_v2"
mtype: "<type \'module\'>"
}
member {
name: "vgg16"
mtype: "<type \'module\'>"
}
member {
name: "vgg19"
mtype: "<type \'module\'>"
}
member {
name: "xception"
mtype: "<type \'module\'>"
}
member_method {
name: "DenseNet121"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "DenseNet169"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "DenseNet201"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "InceptionResNetV2"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=kwargs, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "InceptionV3"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "MobileNet"
argspec: "args=[\'input_shape\', \'alpha\', \'depth_multiplier\', \'dropout\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'1.0\', \'1\', \'0.001\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "MobileNetV2"
argspec: "args=[\'input_shape\', \'alpha\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'1.0\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "NASNetLarge"
argspec: "args=[\'input_shape\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "NASNetMobile"
argspec: "args=[\'input_shape\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "ResNet101"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=kwargs, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "ResNet101V2"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "ResNet152"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=kwargs, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "ResNet152V2"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "ResNet50"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=kwargs, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "ResNet50V2"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "VGG16"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "VGG19"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "Xception"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
}

23
tensorflow/tools/api/golden/v2/tensorflow.keras.applications.resnet.pbtxt Normal file
View File

@ -0,0 +1,23 @@
path: "tensorflow.keras.applications.resnet"
tf_module {
member_method {
name: "ResNet101"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=kwargs, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "ResNet152"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=kwargs, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "ResNet50"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=kwargs, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "decode_predictions"
argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], "
}
member_method {
name: "preprocess_input"
argspec: "args=[\'x\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
}

15
tensorflow/tools/api/golden/v2/tensorflow.keras.applications.resnet50.pbtxt Normal file
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@ -0,0 +1,15 @@
path: "tensorflow.keras.applications.resnet50"
tf_module {
member_method {
name: "ResNet50"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=kwargs, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
}
member_method {
name: "decode_predictions"
argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], "
}
member_method {
name: "preprocess_input"
argspec: "args=[\'x\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
}

23
tensorflow/tools/api/golden/v2/tensorflow.keras.applications.resnet_v2.pbtxt Normal file
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@ -0,0 +1,23 @@
path: "tensorflow.keras.applications.resnet_v2"
tf_module {
member_method {
name: "ResNet101V2"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "ResNet152V2"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "ResNet50V2"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "decode_predictions"
argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], "
}
member_method {
name: "preprocess_input"
argspec: "args=[\'x\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
}

15
tensorflow/tools/api/golden/v2/tensorflow.keras.applications.vgg16.pbtxt Normal file
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@ -0,0 +1,15 @@
path: "tensorflow.keras.applications.vgg16"
tf_module {
member_method {
name: "VGG16"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "decode_predictions"
argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], "
}
member_method {
name: "preprocess_input"
argspec: "args=[\'x\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
}

15
tensorflow/tools/api/golden/v2/tensorflow.keras.applications.vgg19.pbtxt Normal file
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@ -0,0 +1,15 @@
path: "tensorflow.keras.applications.vgg19"
tf_module {
member_method {
name: "VGG19"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "decode_predictions"
argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], "
}
member_method {
name: "preprocess_input"
argspec: "args=[\'x\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
}

15
tensorflow/tools/api/golden/v2/tensorflow.keras.applications.xception.pbtxt Normal file
View File

@ -0,0 +1,15 @@
path: "tensorflow.keras.applications.xception"
tf_module {
member_method {
name: "Xception"
argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\', \'classifier_activation\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\', \'softmax\'], "
}
member_method {
name: "decode_predictions"
argspec: "args=[\'preds\', \'top\'], varargs=None, keywords=None, defaults=[\'5\'], "
}
member_method {
name: "preprocess_input"
argspec: "args=[\'x\', \'data_format\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
}

45
tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.image.-directory-iterator.pbtxt Normal file
View File

@ -0,0 +1,45 @@
path: "tensorflow.keras.preprocessing.image.DirectoryIterator"
tf_class {
is_instance: "<class \'tensorflow.python.keras.preprocessing.image.DirectoryIterator\'>"
is_instance: "<class \'keras_preprocessing.image.directory_iterator.DirectoryIterator\'>"
member {
name: "allowed_class_modes"
mtype: "<type \'set\'>"
}
member {
name: "filepaths"
mtype: "<type \'property\'>"
}
member {
name: "labels"
mtype: "<type \'property\'>"
}
member {
name: "sample_weight"
mtype: "<type \'property\'>"
}
member {
name: "white_list_formats"
mtype: "<type \'tuple\'>"
}
member_method {
name: "__init__"
argspec: "args=[\'self\', \'directory\', \'image_data_generator\', \'target_size\', \'color_mode\', \'classes\', \'class_mode\', \'batch_size\', \'shuffle\', \'seed\', \'data_format\', \'save_to_dir\', \'save_prefix\', \'save_format\', \'follow_links\', \'subset\', \'interpolation\', \'dtype\'], varargs=None, keywords=None, defaults=[\'(256, 256)\', \'rgb\', \'None\', \'categorical\', \'32\', \'True\', \'None\', \'None\', \'None\', \'\', \'png\', \'False\', \'None\', \'nearest\', \'None\'], "
}
member_method {
name: "next"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "on_epoch_end"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "reset"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "set_processing_attrs"
argspec: "args=[\'self\', \'image_data_generator\', \'target_size\', \'color_mode\', \'data_format\', \'save_to_dir\', \'save_prefix\', \'save_format\', \'subset\', \'interpolation\'], varargs=None, keywords=None, defaults=None"
}
}

41
tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.image.-image-data-generator.pbtxt Normal file
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@ -0,0 +1,41 @@
path: "tensorflow.keras.preprocessing.image.ImageDataGenerator"
tf_class {
is_instance: "<class \'tensorflow.python.keras.preprocessing.image.ImageDataGenerator\'>"
is_instance: "<class \'keras_preprocessing.image.image_data_generator.ImageDataGenerator\'>"
member_method {
name: "__init__"
argspec: "args=[\'self\', \'featurewise_center\', \'samplewise_center\', \'featurewise_std_normalization\', \'samplewise_std_normalization\', \'zca_whitening\', \'zca_epsilon\', \'rotation_range\', \'width_shift_range\', \'height_shift_range\', \'brightness_range\', \'shear_range\', \'zoom_range\', \'channel_shift_range\', \'fill_mode\', \'cval\', \'horizontal_flip\', \'vertical_flip\', \'rescale\', \'preprocessing_function\', \'data_format\', \'validation_split\', \'dtype\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'False\', \'False\', \'False\', \'1e-06\', \'0\', \'0.0\', \'0.0\', \'None\', \'0.0\', \'0.0\', \'0.0\', \'nearest\', \'0.0\', \'False\', \'False\', \'None\', \'None\', \'None\', \'0.0\', \'None\'], "
}
member_method {
name: "apply_transform"
argspec: "args=[\'self\', \'x\', \'transform_parameters\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "fit"
argspec: "args=[\'self\', \'x\', \'augment\', \'rounds\', \'seed\'], varargs=None, keywords=None, defaults=[\'False\', \'1\', \'None\'], "
}
member_method {
name: "flow"
argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'shuffle\', \'sample_weight\', \'seed\', \'save_to_dir\', \'save_prefix\', \'save_format\', \'subset\'], varargs=None, keywords=None, defaults=[\'None\', \'32\', \'True\', \'None\', \'None\', \'None\', \'\', \'png\', \'None\'], "
}
member_method {
name: "flow_from_dataframe"
argspec: "args=[\'self\', \'dataframe\', \'directory\', \'x_col\', \'y_col\', \'weight_col\', \'target_size\', \'color_mode\', \'classes\', \'class_mode\', \'batch_size\', \'shuffle\', \'seed\', \'save_to_dir\', \'save_prefix\', \'save_format\', \'subset\', \'interpolation\', \'validate_filenames\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'filename\', \'class\', \'None\', \'(256, 256)\', \'rgb\', \'None\', \'categorical\', \'32\', \'True\', \'None\', \'None\', \'\', \'png\', \'None\', \'nearest\', \'True\'], "
}
member_method {
name: "flow_from_directory"
argspec: "args=[\'self\', \'directory\', \'target_size\', \'color_mode\', \'classes\', \'class_mode\', \'batch_size\', \'shuffle\', \'seed\', \'save_to_dir\', \'save_prefix\', \'save_format\', \'follow_links\', \'subset\', \'interpolation\'], varargs=None, keywords=None, defaults=[\'(256, 256)\', \'rgb\', \'None\', \'categorical\', \'32\', \'True\', \'None\', \'None\', \'\', \'png\', \'False\', \'None\', \'nearest\'], "
}
member_method {
name: "get_random_transform"
argspec: "args=[\'self\', \'img_shape\', \'seed\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "random_transform"
argspec: "args=[\'self\', \'x\', \'seed\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "standardize"
argspec: "args=[\'self\', \'x\'], varargs=None, keywords=None, defaults=None"
}
}

25
tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.image.-iterator.pbtxt Normal file
View File

@ -0,0 +1,25 @@
path: "tensorflow.keras.preprocessing.image.Iterator"
tf_class {
is_instance: "<class \'tensorflow.python.keras.preprocessing.image.Iterator\'>"
is_instance: "<class \'keras_preprocessing.image.iterator.Iterator\'>"
member {
name: "white_list_formats"
mtype: "<type \'tuple\'>"
}
member_method {
name: "__init__"
argspec: "args=[\'self\', \'n\', \'batch_size\', \'shuffle\', \'seed\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "next"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "on_epoch_end"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "reset"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
}

25
tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.image.-numpy-array-iterator.pbtxt Normal file
View File

@ -0,0 +1,25 @@
path: "tensorflow.keras.preprocessing.image.NumpyArrayIterator"
tf_class {
is_instance: "<class \'tensorflow.python.keras.preprocessing.image.NumpyArrayIterator\'>"
is_instance: "<class \'keras_preprocessing.image.numpy_array_iterator.NumpyArrayIterator\'>"
member {
name: "white_list_formats"
mtype: "<type \'tuple\'>"
}
member_method {
name: "__init__"
argspec: "args=[\'self\', \'x\', \'y\', \'image_data_generator\', \'batch_size\', \'shuffle\', \'sample_weight\', \'seed\', \'data_format\', \'save_to_dir\', \'save_prefix\', \'save_format\', \'subset\', \'dtype\'], varargs=None, keywords=None, defaults=[\'32\', \'False\', \'None\', \'None\', \'None\', \'None\', \'\', \'png\', \'None\', \'None\'], "
}
member_method {
name: "next"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "on_epoch_end"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "reset"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
}

71
tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.image.pbtxt Normal file
View File

@ -0,0 +1,71 @@
path: "tensorflow.keras.preprocessing.image"
tf_module {
member {
name: "DirectoryIterator"
mtype: "<type \'type\'>"
}
member {
name: "ImageDataGenerator"
mtype: "<type \'type\'>"
}
member {
name: "Iterator"
mtype: "<type \'type\'>"
}
member {
name: "NumpyArrayIterator"
mtype: "<type \'type\'>"
}
member_method {
name: "apply_affine_transform"
argspec: "args=[\'x\', \'theta\', \'tx\', \'ty\', \'shear\', \'zx\', \'zy\', \'row_axis\', \'col_axis\', \'channel_axis\', \'fill_mode\', \'cval\', \'order\'], varargs=None, keywords=None, defaults=[\'0\', \'0\', \'0\', \'0\', \'1\', \'1\', \'0\', \'1\', \'2\', \'nearest\', \'0.0\', \'1\'], "
}
member_method {
name: "apply_brightness_shift"
argspec: "args=[\'x\', \'brightness\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "apply_channel_shift"
argspec: "args=[\'x\', \'intensity\', \'channel_axis\'], varargs=None, keywords=None, defaults=[\'0\'], "
}
member_method {
name: "array_to_img"
argspec: "args=[\'x\', \'data_format\', \'scale\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'None\'], "
}
member_method {
name: "img_to_array"
argspec: "args=[\'img\', \'data_format\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "load_img"
argspec: "args=[\'path\', \'grayscale\', \'color_mode\', \'target_size\', \'interpolation\'], varargs=None, keywords=None, defaults=[\'False\', \'rgb\', \'None\', \'nearest\'], "
}
member_method {
name: "random_brightness"
argspec: "args=[\'x\', \'brightness_range\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "random_channel_shift"
argspec: "args=[\'x\', \'intensity_range\', \'channel_axis\'], varargs=None, keywords=None, defaults=[\'0\'], "
}
member_method {
name: "random_rotation"
argspec: "args=[\'x\', \'rg\', \'row_axis\', \'col_axis\', \'channel_axis\', \'fill_mode\', \'cval\', \'interpolation_order\'], varargs=None, keywords=None, defaults=[\'1\', \'2\', \'0\', \'nearest\', \'0.0\', \'1\'], "
}
member_method {
name: "random_shear"
argspec: "args=[\'x\', \'intensity\', \'row_axis\', \'col_axis\', \'channel_axis\', \'fill_mode\', \'cval\', \'interpolation_order\'], varargs=None, keywords=None, defaults=[\'1\', \'2\', \'0\', \'nearest\', \'0.0\', \'1\'], "
}
member_method {
name: "random_shift"
argspec: "args=[\'x\', \'wrg\', \'hrg\', \'row_axis\', \'col_axis\', \'channel_axis\', \'fill_mode\', \'cval\', \'interpolation_order\'], varargs=None, keywords=None, defaults=[\'1\', \'2\', \'0\', \'nearest\', \'0.0\', \'1\'], "
}
member_method {
name: "random_zoom"
argspec: "args=[\'x\', \'zoom_range\', \'row_axis\', \'col_axis\', \'channel_axis\', \'fill_mode\', \'cval\', \'interpolation_order\'], varargs=None, keywords=None, defaults=[\'1\', \'2\', \'0\', \'nearest\', \'0.0\', \'1\'], "
}
member_method {
name: "save_img"
argspec: "args=[\'path\', \'x\', \'data_format\', \'file_format\', \'scale\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'True\'], "
}
}

23
tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.pbtxt Normal file
View File

@ -0,0 +1,23 @@
path: "tensorflow.keras.preprocessing"
tf_module {
member {
name: "image"
mtype: "<type \'module\'>"
}
member {
name: "sequence"
mtype: "<type \'module\'>"
}
member {
name: "text"
mtype: "<type \'module\'>"
}
member_method {
name: "image_dataset_from_directory"
argspec: "args=[\'directory\', \'labels\', \'label_mode\', \'class_names\', \'color_mode\', \'batch_size\', \'image_size\', \'shuffle\', \'seed\', \'validation_split\', \'subset\', \'interpolation\', \'follow_links\'], varargs=None, keywords=None, defaults=[\'inferred\', \'int\', \'None\', \'rgb\', \'32\', \'(256, 256)\', \'True\', \'None\', \'None\', \'None\', \'bilinear\', \'False\'], "
}
member_method {
name: "timeseries_dataset_from_array"
argspec: "args=[\'data\', \'targets\', \'sequence_length\', \'sequence_stride\', \'sampling_rate\', \'batch_size\', \'shuffle\', \'seed\', \'start_index\', \'end_index\'], varargs=None, keywords=None, defaults=[\'1\', \'1\', \'128\', \'False\', \'None\', \'None\', \'None\'], "
}
}

21
tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.sequence.-timeseries-generator.pbtxt Normal file
View File

@ -0,0 +1,21 @@
path: "tensorflow.keras.preprocessing.sequence.TimeseriesGenerator"
tf_class {
is_instance: "<class \'tensorflow.python.keras.preprocessing.sequence.TimeseriesGenerator\'>"
is_instance: "<class \'keras_preprocessing.sequence.TimeseriesGenerator\'>"
member_method {
name: "__init__"
argspec: "args=[\'self\', \'data\', \'targets\', \'length\', \'sampling_rate\', \'stride\', \'start_index\', \'end_index\', \'shuffle\', \'reverse\', \'batch_size\'], varargs=None, keywords=None, defaults=[\'1\', \'1\', \'0\', \'None\', \'False\', \'False\', \'128\'], "
}
member_method {
name: "get_config"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "on_epoch_end"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "to_json"
argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None"
}
}

19
tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.sequence.pbtxt Normal file
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@ -0,0 +1,19 @@
path: "tensorflow.keras.preprocessing.sequence"
tf_module {
member {
name: "TimeseriesGenerator"
mtype: "<type \'type\'>"
}
member_method {
name: "make_sampling_table"
argspec: "args=[\'size\', \'sampling_factor\'], varargs=None, keywords=None, defaults=[\'1e-05\'], "
}
member_method {
name: "pad_sequences"
argspec: "args=[\'sequences\', \'maxlen\', \'dtype\', \'padding\', \'truncating\', \'value\'], varargs=None, keywords=None, defaults=[\'None\', \'int32\', \'pre\', \'pre\', \'0.0\'], "
}
member_method {
name: "skipgrams"
argspec: "args=[\'sequence\', \'vocabulary_size\', \'window_size\', \'negative_samples\', \'shuffle\', \'categorical\', \'sampling_table\', \'seed\'], varargs=None, keywords=None, defaults=[\'4\', \'1.0\', \'True\', \'False\', \'None\', \'None\'], "
}
}

48
tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.text.-tokenizer.pbtxt Normal file
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@ -0,0 +1,48 @@
path: "tensorflow.keras.preprocessing.text.Tokenizer"
tf_class {
is_instance: "<class \'keras_preprocessing.text.Tokenizer\'>"
member_method {
name: "__init__"
argspec: "args=[\'self\', \'num_words\', \'filters\', \'lower\', \'split\', \'char_level\', \'oov_token\', \'document_count\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n\', \'True\', \' \', \'False\', \'None\', \'0\'], "
}
member_method {
name: "fit_on_sequences"
argspec: "args=[\'self\', \'sequences\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "fit_on_texts"
argspec: "args=[\'self\', \'texts\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_config"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "sequences_to_matrix"
argspec: "args=[\'self\', \'sequences\', \'mode\'], varargs=None, keywords=None, defaults=[\'binary\'], "
}
member_method {
name: "sequences_to_texts"
argspec: "args=[\'self\', \'sequences\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "sequences_to_texts_generator"
argspec: "args=[\'self\', \'sequences\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "texts_to_matrix"
argspec: "args=[\'self\', \'texts\', \'mode\'], varargs=None, keywords=None, defaults=[\'binary\'], "
}
member_method {
name: "texts_to_sequences"
argspec: "args=[\'self\', \'texts\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "texts_to_sequences_generator"
argspec: "args=[\'self\', \'texts\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "to_json"
argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None"
}
}

23
tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.text.pbtxt Normal file
View File

@ -0,0 +1,23 @@
path: "tensorflow.keras.preprocessing.text"
tf_module {
member {
name: "Tokenizer"
mtype: "<type \'type\'>"
}
member_method {
name: "hashing_trick"
argspec: "args=[\'text\', \'n\', \'hash_function\', \'filters\', \'lower\', \'split\'], varargs=None, keywords=None, defaults=[\'None\', \'!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n\', \'True\', \' \'], "
}
member_method {
name: "one_hot"
argspec: "args=[\'input_text\', \'n\', \'filters\', \'lower\', \'split\'], varargs=None, keywords=None, defaults=[\'!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n\', \'True\', \' \'], "
}
member_method {
name: "text_to_word_sequence"
argspec: "args=[\'input_text\', \'filters\', \'lower\', \'split\'], varargs=None, keywords=None, defaults=[\'!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n\', \'True\', \' \'], "
}
member_method {
name: "tokenizer_from_json"
argspec: "args=[\'json_string\'], varargs=None, keywords=None, defaults=None"
}
}

2
tensorflow/tools/common/public_api.py
View File

@ -75,8 +75,6 @@ class PublicAPIVisitor(object):
'tf.app': ['flags'],
# Imported for compatibility between py2/3.
'tf.test': ['mock'],
# Externalized modules of the Keras API.
'tf.keras': ['applications', 'preprocessing']
}
@property