Add keras premade model symbols.

PiperOrigin-RevId: 261218358

tensorflow/python/keras/__init__.py

@@ -38,6 +38,7 @@ from tensorflow.python.keras import metrics
 from tensorflow.python.keras import models
 from tensorflow.python.keras import ops
 from tensorflow.python.keras import optimizers
+from tensorflow.python.keras import premade
 from tensorflow.python.keras import preprocessing
 from tensorflow.python.keras import regularizers
 from tensorflow.python.keras import utils

tensorflow/python/keras/premade/BUILD

@@ -13,6 +13,7 @@ load("//tensorflow:tensorflow.bzl", "py_test")
 py_library(
     name = "premade",
     srcs = [
+        "__init__.py",
         "linear.py",
         "wide_deep.py",
     ],

tensorflow/python/keras/premade/__init__.py

@@ -0,0 +1,21 @@
+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Premade Model API."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.keras.premade import linear
+from tensorflow.python.keras.premade import wide_deep

tensorflow/python/keras/premade/linear.py

@@ -24,8 +24,10 @@ from tensorflow.python.keras import regularizers
 from tensorflow.python.keras.engine import training
 from tensorflow.python.keras.layers import core
 from tensorflow.python.ops import nn
+from tensorflow.python.util.tf_export import keras_export
 
 
+@keras_export('keras.experimental.LinearModel')
 class LinearModel(training.Model):
   r"""Linear Model for regression and classification problems.
 

tensorflow/python/keras/premade/wide_deep.py

@@ -20,8 +20,10 @@ from __future__ import print_function
 
 from tensorflow.python.keras import backend as K
 from tensorflow.python.keras.engine import training
+from tensorflow.python.util.tf_export import keras_export
 
 
+@keras_export('keras.experimental.WideDeepModel')
 class WideDeepModel(training.Model):
   r"""Wide & Deep Model for regression and classification problems.
 

tensorflow/python/tools/api/generator/api_init_files.bzl

@@ -101,6 +101,7 @@ KERAS_API_INIT_FILES = [
     "keras/metrics/__init__.py",
     "keras/mixed_precision/__init__.py",
     "keras/mixed_precision/experimental/__init__.py",
+    "keras/premade/__init__.py",
     "keras/models/__init__.py",
     "keras/optimizers/__init__.py",
     "keras/optimizers/schedules/__init__.py",

tensorflow/python/tools/api/generator/api_init_files_v1.bzl

@@ -131,6 +131,7 @@ KERAS_API_INIT_FILES_V1 = [
     "keras/models/__init__.py",
     "keras/optimizers/__init__.py",
     "keras/optimizers/schedules/__init__.py",
+    "keras/premade/__init__.py",
     "keras/preprocessing/__init__.py",
     "keras/preprocessing/image/__init__.py",
     "keras/preprocessing/sequence/__init__.py",

tensorflow/tools/api/golden/v1/tensorflow.keras.experimental.-linear-model.pbtxt

@@ -0,0 +1,315 @@
+path: "tensorflow.keras.experimental.LinearModel"
+tf_class {
+  is_instance: "<class \'tensorflow.python.keras.premade.linear.LinearModel\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.training.Model\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.network.Network\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
+  is_instance: "<class \'tensorflow.python.module.module.Module\'>"
+  is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
+  is_instance: "<class \'tensorflow.python.training.tracking.base.Trackable\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "activity_regularizer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dynamic"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "inbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_spec"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "layers"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "losses"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "metrics"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "metrics_names"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name_scope"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "outbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "run_eagerly"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "sample_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "state_updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "stateful"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "submodules"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "weights"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'units\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\'], varargs=None, keywords=kwargs, defaults=[\'1\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "add_loss"
+    argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_metric"
+    argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
+  }
+  member_method {
+    name: "add_update"
+    argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_variable"
+    argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "add_weight"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
+  }
+  member_method {
+    name: "apply"
+    argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "build"
+    argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "compile"
+    argspec: "args=[\'self\', \'optimizer\', \'loss\', \'metrics\', \'loss_weights\', \'sample_weight_mode\', \'weighted_metrics\', \'target_tensors\', \'distribute\'], varargs=None, keywords=kwargs, defaults=[\'rmsprop\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "compute_mask"
+    argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "compute_output_shape"
+    argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "compute_output_signature"
+    argspec: "args=[\'self\', \'input_signature\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "count_params"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "evaluate"
+    argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'verbose\', \'sample_weight\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'1\', \'None\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
+  }
+  member_method {
+    name: "evaluate_generator"
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'10\', \'1\', \'False\', \'0\'], "
+  }
+  member_method {
+    name: "fit"
+    argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'epochs\', \'verbose\', \'callbacks\', \'validation_split\', \'validation_data\', \'shuffle\', \'class_weight\', \'sample_weight\', \'initial_epoch\', \'steps_per_epoch\', \'validation_steps\', \'validation_freq\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'1\', \'1\', \'None\', \'0.0\', \'None\', \'True\', \'None\', \'None\', \'0\', \'None\', \'None\', \'1\', \'10\', \'1\', \'False\'], "
+  }
+  member_method {
+    name: "fit_generator"
+    argspec: "args=[\'self\', \'generator\', \'steps_per_epoch\', \'epochs\', \'verbose\', \'callbacks\', \'validation_data\', \'validation_steps\', \'validation_freq\', \'class_weight\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'shuffle\', \'initial_epoch\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'1\', \'None\', \'None\', \'None\', \'1\', \'None\', \'10\', \'1\', \'False\', \'True\', \'0\'], "
+  }
+  member_method {
+    name: "from_config"
+    argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_layer"
+    argspec: "args=[\'self\', \'name\', \'index\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
+  }
+  member_method {
+    name: "get_losses_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_updates_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_weights"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "load_weights"
+    argspec: "args=[\'self\', \'filepath\', \'by_name\'], varargs=None, keywords=None, defaults=[\'False\'], "
+  }
+  member_method {
+    name: "predict"
+    argspec: "args=[\'self\', \'x\', \'batch_size\', \'verbose\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
+  }
+  member_method {
+    name: "predict_generator"
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'10\', \'1\', \'False\', \'0\'], "
+  }
+  member_method {
+    name: "predict_on_batch"
+    argspec: "args=[\'self\', \'x\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "reset_metrics"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "reset_states"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "save"
+    argspec: "args=[\'self\', \'filepath\', \'overwrite\', \'include_optimizer\', \'save_format\'], varargs=None, keywords=None, defaults=[\'True\', \'True\', \'None\'], "
+  }
+  member_method {
+    name: "save_weights"
+    argspec: "args=[\'self\', \'filepath\', \'overwrite\', \'save_format\'], varargs=None, keywords=None, defaults=[\'True\', \'None\'], "
+  }
+  member_method {
+    name: "set_weights"
+    argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "summary"
+    argspec: "args=[\'self\', \'line_length\', \'positions\', \'print_fn\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "test_on_batch"
+    argspec: "args=[\'self\', \'x\', \'y\', \'sample_weight\', \'reset_metrics\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'True\'], "
+  }
+  member_method {
+    name: "to_json"
+    argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "to_yaml"
+    argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "train_on_batch"
+    argspec: "args=[\'self\', \'x\', \'y\', \'sample_weight\', \'class_weight\', \'reset_metrics\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], "
+  }
+  member_method {
+    name: "with_name_scope"
+    argspec: "args=[\'cls\', \'method\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/v1/tensorflow.keras.experimental.-wide-deep-model.pbtxt

@@ -0,0 +1,315 @@
+path: "tensorflow.keras.experimental.WideDeepModel"
+tf_class {
+  is_instance: "<class \'tensorflow.python.keras.premade.wide_deep.WideDeepModel\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.training.Model\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.network.Network\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
+  is_instance: "<class \'tensorflow.python.module.module.Module\'>"
+  is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
+  is_instance: "<class \'tensorflow.python.training.tracking.base.Trackable\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "activity_regularizer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dynamic"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "inbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_spec"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "layers"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "losses"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "metrics"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "metrics_names"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name_scope"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "outbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "run_eagerly"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "sample_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "state_updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "stateful"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "submodules"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "weights"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'linear_model\', \'dnn_model\', \'activation\'], varargs=None, keywords=kwargs, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_loss"
+    argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_metric"
+    argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
+  }
+  member_method {
+    name: "add_update"
+    argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_variable"
+    argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "add_weight"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
+  }
+  member_method {
+    name: "apply"
+    argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "build"
+    argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "compile"
+    argspec: "args=[\'self\', \'optimizer\', \'loss\', \'metrics\', \'loss_weights\', \'sample_weight_mode\', \'weighted_metrics\', \'target_tensors\', \'distribute\'], varargs=None, keywords=kwargs, defaults=[\'rmsprop\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "compute_mask"
+    argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "compute_output_shape"
+    argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "compute_output_signature"
+    argspec: "args=[\'self\', \'input_signature\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "count_params"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "evaluate"
+    argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'verbose\', \'sample_weight\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'1\', \'None\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
+  }
+  member_method {
+    name: "evaluate_generator"
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'10\', \'1\', \'False\', \'0\'], "
+  }
+  member_method {
+    name: "fit"
+    argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'epochs\', \'verbose\', \'callbacks\', \'validation_split\', \'validation_data\', \'shuffle\', \'class_weight\', \'sample_weight\', \'initial_epoch\', \'steps_per_epoch\', \'validation_steps\', \'validation_freq\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'1\', \'1\', \'None\', \'0.0\', \'None\', \'True\', \'None\', \'None\', \'0\', \'None\', \'None\', \'1\', \'10\', \'1\', \'False\'], "
+  }
+  member_method {
+    name: "fit_generator"
+    argspec: "args=[\'self\', \'generator\', \'steps_per_epoch\', \'epochs\', \'verbose\', \'callbacks\', \'validation_data\', \'validation_steps\', \'validation_freq\', \'class_weight\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'shuffle\', \'initial_epoch\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'1\', \'None\', \'None\', \'None\', \'1\', \'None\', \'10\', \'1\', \'False\', \'True\', \'0\'], "
+  }
+  member_method {
+    name: "from_config"
+    argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_layer"
+    argspec: "args=[\'self\', \'name\', \'index\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
+  }
+  member_method {
+    name: "get_losses_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_updates_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_weights"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "load_weights"
+    argspec: "args=[\'self\', \'filepath\', \'by_name\'], varargs=None, keywords=None, defaults=[\'False\'], "
+  }
+  member_method {
+    name: "predict"
+    argspec: "args=[\'self\', \'x\', \'batch_size\', \'verbose\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
+  }
+  member_method {
+    name: "predict_generator"
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'10\', \'1\', \'False\', \'0\'], "
+  }
+  member_method {
+    name: "predict_on_batch"
+    argspec: "args=[\'self\', \'x\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "reset_metrics"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "reset_states"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "save"
+    argspec: "args=[\'self\', \'filepath\', \'overwrite\', \'include_optimizer\', \'save_format\'], varargs=None, keywords=None, defaults=[\'True\', \'True\', \'None\'], "
+  }
+  member_method {
+    name: "save_weights"
+    argspec: "args=[\'self\', \'filepath\', \'overwrite\', \'save_format\'], varargs=None, keywords=None, defaults=[\'True\', \'None\'], "
+  }
+  member_method {
+    name: "set_weights"
+    argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "summary"
+    argspec: "args=[\'self\', \'line_length\', \'positions\', \'print_fn\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "test_on_batch"
+    argspec: "args=[\'self\', \'x\', \'y\', \'sample_weight\', \'reset_metrics\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'True\'], "
+  }
+  member_method {
+    name: "to_json"
+    argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "to_yaml"
+    argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "train_on_batch"
+    argspec: "args=[\'self\', \'x\', \'y\', \'sample_weight\', \'class_weight\', \'reset_metrics\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], "
+  }
+  member_method {
+    name: "with_name_scope"
+    argspec: "args=[\'cls\', \'method\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/v1/tensorflow.keras.experimental.pbtxt

@@ -12,6 +12,10 @@ tf_module {
     name: "LinearCosineDecay"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "LinearModel"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "NoisyLinearCosineDecay"
     mtype: "<type \'type\'>"
@@ -24,6 +28,10 @@ tf_module {
     name: "SequenceFeatures"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "WideDeepModel"
+    mtype: "<type \'type\'>"
+  }
   member_method {
     name: "export_saved_model"
     argspec: "args=[\'model\', \'saved_model_path\', \'custom_objects\', \'as_text\', \'input_signature\', \'serving_only\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\', \'False\'], "

tensorflow/tools/api/golden/v2/tensorflow.keras.experimental.-linear-model.pbtxt

@@ -0,0 +1,315 @@
+path: "tensorflow.keras.experimental.LinearModel"
+tf_class {
+  is_instance: "<class \'tensorflow.python.keras.premade.linear.LinearModel\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.training.Model\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.network.Network\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
+  is_instance: "<class \'tensorflow.python.module.module.Module\'>"
+  is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
+  is_instance: "<class \'tensorflow.python.training.tracking.base.Trackable\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "activity_regularizer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dynamic"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "inbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_spec"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "layers"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "losses"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "metrics"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "metrics_names"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name_scope"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "outbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "run_eagerly"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "sample_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "state_updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "stateful"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "submodules"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "weights"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'units\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\'], varargs=None, keywords=kwargs, defaults=[\'1\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "add_loss"
+    argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_metric"
+    argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
+  }
+  member_method {
+    name: "add_update"
+    argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_variable"
+    argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "add_weight"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
+  }
+  member_method {
+    name: "apply"
+    argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "build"
+    argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "compile"
+    argspec: "args=[\'self\', \'optimizer\', \'loss\', \'metrics\', \'loss_weights\', \'sample_weight_mode\', \'weighted_metrics\', \'target_tensors\', \'distribute\'], varargs=None, keywords=kwargs, defaults=[\'rmsprop\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "compute_mask"
+    argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "compute_output_shape"
+    argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "compute_output_signature"
+    argspec: "args=[\'self\', \'input_signature\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "count_params"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "evaluate"
+    argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'verbose\', \'sample_weight\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'1\', \'None\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
+  }
+  member_method {
+    name: "evaluate_generator"
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'10\', \'1\', \'False\', \'0\'], "
+  }
+  member_method {
+    name: "fit"
+    argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'epochs\', \'verbose\', \'callbacks\', \'validation_split\', \'validation_data\', \'shuffle\', \'class_weight\', \'sample_weight\', \'initial_epoch\', \'steps_per_epoch\', \'validation_steps\', \'validation_freq\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'1\', \'1\', \'None\', \'0.0\', \'None\', \'True\', \'None\', \'None\', \'0\', \'None\', \'None\', \'1\', \'10\', \'1\', \'False\'], "
+  }
+  member_method {
+    name: "fit_generator"
+    argspec: "args=[\'self\', \'generator\', \'steps_per_epoch\', \'epochs\', \'verbose\', \'callbacks\', \'validation_data\', \'validation_steps\', \'validation_freq\', \'class_weight\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'shuffle\', \'initial_epoch\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'1\', \'None\', \'None\', \'None\', \'1\', \'None\', \'10\', \'1\', \'False\', \'True\', \'0\'], "
+  }
+  member_method {
+    name: "from_config"
+    argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_layer"
+    argspec: "args=[\'self\', \'name\', \'index\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
+  }
+  member_method {
+    name: "get_losses_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_updates_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_weights"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "load_weights"
+    argspec: "args=[\'self\', \'filepath\', \'by_name\'], varargs=None, keywords=None, defaults=[\'False\'], "
+  }
+  member_method {
+    name: "predict"
+    argspec: "args=[\'self\', \'x\', \'batch_size\', \'verbose\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
+  }
+  member_method {
+    name: "predict_generator"
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'10\', \'1\', \'False\', \'0\'], "
+  }
+  member_method {
+    name: "predict_on_batch"
+    argspec: "args=[\'self\', \'x\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "reset_metrics"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "reset_states"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "save"
+    argspec: "args=[\'self\', \'filepath\', \'overwrite\', \'include_optimizer\', \'save_format\'], varargs=None, keywords=None, defaults=[\'True\', \'True\', \'None\'], "
+  }
+  member_method {
+    name: "save_weights"
+    argspec: "args=[\'self\', \'filepath\', \'overwrite\', \'save_format\'], varargs=None, keywords=None, defaults=[\'True\', \'None\'], "
+  }
+  member_method {
+    name: "set_weights"
+    argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "summary"
+    argspec: "args=[\'self\', \'line_length\', \'positions\', \'print_fn\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "test_on_batch"
+    argspec: "args=[\'self\', \'x\', \'y\', \'sample_weight\', \'reset_metrics\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'True\'], "
+  }
+  member_method {
+    name: "to_json"
+    argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "to_yaml"
+    argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "train_on_batch"
+    argspec: "args=[\'self\', \'x\', \'y\', \'sample_weight\', \'class_weight\', \'reset_metrics\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], "
+  }
+  member_method {
+    name: "with_name_scope"
+    argspec: "args=[\'cls\', \'method\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/v2/tensorflow.keras.experimental.-wide-deep-model.pbtxt

@@ -0,0 +1,315 @@
+path: "tensorflow.keras.experimental.WideDeepModel"
+tf_class {
+  is_instance: "<class \'tensorflow.python.keras.premade.wide_deep.WideDeepModel\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.training.Model\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.network.Network\'>"
+  is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
+  is_instance: "<class \'tensorflow.python.module.module.Module\'>"
+  is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
+  is_instance: "<class \'tensorflow.python.training.tracking.base.Trackable\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "activity_regularizer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dynamic"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "inbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_spec"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "layers"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "losses"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "metrics"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "metrics_names"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name_scope"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "outbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "run_eagerly"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "sample_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "state_updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "stateful"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "submodules"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "weights"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'linear_model\', \'dnn_model\', \'activation\'], varargs=None, keywords=kwargs, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_loss"
+    argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_metric"
+    argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
+  }
+  member_method {
+    name: "add_update"
+    argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_variable"
+    argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "add_weight"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
+  }
+  member_method {
+    name: "apply"
+    argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "build"
+    argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "compile"
+    argspec: "args=[\'self\', \'optimizer\', \'loss\', \'metrics\', \'loss_weights\', \'sample_weight_mode\', \'weighted_metrics\', \'target_tensors\', \'distribute\'], varargs=None, keywords=kwargs, defaults=[\'rmsprop\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "compute_mask"
+    argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "compute_output_shape"
+    argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "compute_output_signature"
+    argspec: "args=[\'self\', \'input_signature\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "count_params"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "evaluate"
+    argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'verbose\', \'sample_weight\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'1\', \'None\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
+  }
+  member_method {
+    name: "evaluate_generator"
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'10\', \'1\', \'False\', \'0\'], "
+  }
+  member_method {
+    name: "fit"
+    argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'epochs\', \'verbose\', \'callbacks\', \'validation_split\', \'validation_data\', \'shuffle\', \'class_weight\', \'sample_weight\', \'initial_epoch\', \'steps_per_epoch\', \'validation_steps\', \'validation_freq\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'1\', \'1\', \'None\', \'0.0\', \'None\', \'True\', \'None\', \'None\', \'0\', \'None\', \'None\', \'1\', \'10\', \'1\', \'False\'], "
+  }
+  member_method {
+    name: "fit_generator"
+    argspec: "args=[\'self\', \'generator\', \'steps_per_epoch\', \'epochs\', \'verbose\', \'callbacks\', \'validation_data\', \'validation_steps\', \'validation_freq\', \'class_weight\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'shuffle\', \'initial_epoch\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'1\', \'None\', \'None\', \'None\', \'1\', \'None\', \'10\', \'1\', \'False\', \'True\', \'0\'], "
+  }
+  member_method {
+    name: "from_config"
+    argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_layer"
+    argspec: "args=[\'self\', \'name\', \'index\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
+  }
+  member_method {
+    name: "get_losses_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_updates_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_weights"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "load_weights"
+    argspec: "args=[\'self\', \'filepath\', \'by_name\'], varargs=None, keywords=None, defaults=[\'False\'], "
+  }
+  member_method {
+    name: "predict"
+    argspec: "args=[\'self\', \'x\', \'batch_size\', \'verbose\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
+  }
+  member_method {
+    name: "predict_generator"
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'10\', \'1\', \'False\', \'0\'], "
+  }
+  member_method {
+    name: "predict_on_batch"
+    argspec: "args=[\'self\', \'x\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "reset_metrics"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "reset_states"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "save"
+    argspec: "args=[\'self\', \'filepath\', \'overwrite\', \'include_optimizer\', \'save_format\'], varargs=None, keywords=None, defaults=[\'True\', \'True\', \'None\'], "
+  }
+  member_method {
+    name: "save_weights"
+    argspec: "args=[\'self\', \'filepath\', \'overwrite\', \'save_format\'], varargs=None, keywords=None, defaults=[\'True\', \'None\'], "
+  }
+  member_method {
+    name: "set_weights"
+    argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "summary"
+    argspec: "args=[\'self\', \'line_length\', \'positions\', \'print_fn\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "test_on_batch"
+    argspec: "args=[\'self\', \'x\', \'y\', \'sample_weight\', \'reset_metrics\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'True\'], "
+  }
+  member_method {
+    name: "to_json"
+    argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "to_yaml"
+    argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "train_on_batch"
+    argspec: "args=[\'self\', \'x\', \'y\', \'sample_weight\', \'class_weight\', \'reset_metrics\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], "
+  }
+  member_method {
+    name: "with_name_scope"
+    argspec: "args=[\'cls\', \'method\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/v2/tensorflow.keras.experimental.pbtxt

@@ -12,6 +12,10 @@ tf_module {
     name: "LinearCosineDecay"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "LinearModel"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "NoisyLinearCosineDecay"
     mtype: "<type \'type\'>"
@@ -24,6 +28,10 @@ tf_module {
     name: "SequenceFeatures"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "WideDeepModel"
+    mtype: "<type \'type\'>"
+  }
   member_method {
     name: "export_saved_model"
     argspec: "args=[\'model\', \'saved_model_path\', \'custom_objects\', \'as_text\', \'input_signature\', \'serving_only\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\', \'False\'], "