Export EfficientNet models to the public API.
PiperOrigin-RevId: 306281184
Change-Id: I37257d5afdc0ffec6eacd218e8f2e86ad118fb89
diff --git a/tensorflow/python/keras/api/BUILD b/tensorflow/python/keras/api/BUILD
index 41a3f13..7dee9b1 100644
--- a/tensorflow/python/keras/api/BUILD
+++ b/tensorflow/python/keras/api/BUILD
@@ -17,6 +17,7 @@
"tensorflow.python.keras",
"tensorflow.python.keras.activations",
"tensorflow.python.keras.applications.densenet",
+ "tensorflow.python.keras.applications.efficientnet",
"tensorflow.python.keras.applications.imagenet_utils",
"tensorflow.python.keras.applications.inception_resnet_v2",
"tensorflow.python.keras.applications.inception_v3",
diff --git a/tensorflow/python/keras/applications/efficientnet.py b/tensorflow/python/keras/applications/efficientnet.py
index 0487450..28426d4 100644
--- a/tensorflow/python/keras/applications/efficientnet.py
+++ b/tensorflow/python/keras/applications/efficientnet.py
@@ -13,6 +13,7 @@
# limitations under the License.
# ==============================================================================
# pylint: disable=invalid-name
+# pylint: disable=missing-docstring
"""EfficientNet models for Keras.
Reference paper:
@@ -142,6 +143,53 @@
layers = VersionAwareLayers()
+BASE_DOCSTRING = """Instantiates the {name} architecture.
+
+ Reference paper:
+ - [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](
+ https://arxiv.org/abs/1905.11946) (ICML 2019)
+
+ Optionally loads weights pre-trained on ImageNet.
+ Note that the data format convention used by the model is
+ the one specified in your Keras config at `~/.keras/keras.json`.
+ If you have never configured it, it defaults to `"channels_last"`.
+
+ Arguments:
+ include_top: Whether to include the fully-connected
+ layer at the top of the network. Defaults to True.
+ weights: One of `None` (random initialization),
+ 'imagenet' (pre-training on ImageNet),
+ or the path to the weights file to be loaded. Defaults to 'imagenet'.
+ input_tensor: Optional Keras tensor
+ (i.e. output of `layers.Input()`)
+ to use as image input for the model.
+ input_shape: Optional shape tuple, only to be specified
+ if `include_top` is False.
+ It should have exactly 3 inputs channels.
+ pooling: Optional pooling mode for feature extraction
+ when `include_top` is `False`. Defaults to None.
+ - `None` means that the output of the model will be
+ the 4D tensor output of the
+ last convolutional layer.
+ - `avg` means that global average pooling
+ will be applied to the output of the
+ last convolutional layer, and thus
+ the output of the model will be a 2D tensor.
+ - `max` means that global max pooling will
+ be applied.
+ classes: Optional number of classes to classify images
+ into, only to be specified if `include_top` is True, and
+ if no `weights` argument is specified. Defaults to 1000 (number of
+ ImageNet classes).
+ classifier_activation: A `str` or callable. The activation function to use
+ on the "top" layer. Ignored unless `include_top=True`. Set
+ `classifier_activation=None` to return the logits of the "top" layer.
+ Defaults to 'softmax'.
+
+ Returns:
+ A `keras.Model` instance.
+"""
+
def EfficientNet(
width_coefficient,
@@ -163,8 +211,8 @@
"""Instantiates the EfficientNet architecture using given scaling coefficients.
Reference paper:
- - [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks]
- (https://arxiv.org/abs/1905.11946) (ICML 2019)
+ - [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](
+ https://arxiv.org/abs/1905.11946) (ICML 2019)
Optionally loads weights pre-trained on ImageNet.
Note that the data format convention used by the model is
@@ -474,6 +522,7 @@
input_shape=None,
pooling=None,
classes=1000,
+ classifier_activation='softmax',
**kwargs):
return EfficientNet(
1.0,
@@ -487,6 +536,7 @@
input_shape=input_shape,
pooling=pooling,
classes=classes,
+ classifier_activation=classifier_activation,
**kwargs)
@@ -498,6 +548,7 @@
input_shape=None,
pooling=None,
classes=1000,
+ classifier_activation='softmax',
**kwargs):
return EfficientNet(
1.0,
@@ -511,6 +562,7 @@
input_shape=input_shape,
pooling=pooling,
classes=classes,
+ classifier_activation=classifier_activation,
**kwargs)
@@ -522,6 +574,7 @@
input_shape=None,
pooling=None,
classes=1000,
+ classifier_activation='softmax',
**kwargs):
return EfficientNet(
1.1,
@@ -535,6 +588,7 @@
input_shape=input_shape,
pooling=pooling,
classes=classes,
+ classifier_activation=classifier_activation,
**kwargs)
@@ -546,6 +600,7 @@
input_shape=None,
pooling=None,
classes=1000,
+ classifier_activation='softmax',
**kwargs):
return EfficientNet(
1.2,
@@ -559,6 +614,7 @@
input_shape=input_shape,
pooling=pooling,
classes=classes,
+ classifier_activation=classifier_activation,
**kwargs)
@@ -570,6 +626,7 @@
input_shape=None,
pooling=None,
classes=1000,
+ classifier_activation='softmax',
**kwargs):
return EfficientNet(
1.4,
@@ -583,6 +640,7 @@
input_shape=input_shape,
pooling=pooling,
classes=classes,
+ classifier_activation=classifier_activation,
**kwargs)
@@ -594,6 +652,7 @@
input_shape=None,
pooling=None,
classes=1000,
+ classifier_activation='softmax',
**kwargs):
return EfficientNet(
1.6,
@@ -607,6 +666,7 @@
input_shape=input_shape,
pooling=pooling,
classes=classes,
+ classifier_activation=classifier_activation,
**kwargs)
@@ -618,6 +678,7 @@
input_shape=None,
pooling=None,
classes=1000,
+ classifier_activation='softmax',
**kwargs):
return EfficientNet(
1.8,
@@ -631,6 +692,7 @@
input_shape=input_shape,
pooling=pooling,
classes=classes,
+ classifier_activation=classifier_activation,
**kwargs)
@@ -642,6 +704,7 @@
input_shape=None,
pooling=None,
classes=1000,
+ classifier_activation='softmax',
**kwargs):
return EfficientNet(
2.0,
@@ -655,9 +718,20 @@
input_shape=input_shape,
pooling=pooling,
classes=classes,
+ classifier_activation=classifier_activation,
**kwargs)
+EfficientNetB0.__doc__ = BASE_DOCSTRING.format(name='EfficientNetB0')
+EfficientNetB1.__doc__ = BASE_DOCSTRING.format(name='EfficientNetB1')
+EfficientNetB2.__doc__ = BASE_DOCSTRING.format(name='EfficientNetB2')
+EfficientNetB3.__doc__ = BASE_DOCSTRING.format(name='EfficientNetB3')
+EfficientNetB4.__doc__ = BASE_DOCSTRING.format(name='EfficientNetB4')
+EfficientNetB5.__doc__ = BASE_DOCSTRING.format(name='EfficientNetB5')
+EfficientNetB6.__doc__ = BASE_DOCSTRING.format(name='EfficientNetB6')
+EfficientNetB7.__doc__ = BASE_DOCSTRING.format(name='EfficientNetB7')
+
+
@keras_export('keras.applications.efficientnet.preprocess_input')
def preprocess_input(x, data_format=None): # pylint: disable=unused-argument
return x
diff --git a/tensorflow/python/tools/api/generator/api_init_files.bzl b/tensorflow/python/tools/api/generator/api_init_files.bzl
index 99981a5..13068a8 100644
--- a/tensorflow/python/tools/api/generator/api_init_files.bzl
+++ b/tensorflow/python/tools/api/generator/api_init_files.bzl
@@ -83,6 +83,7 @@
"keras/activations/__init__.py",
"keras/applications/__init__.py",
"keras/applications/densenet/__init__.py",
+ "keras/applications/efficientnet/__init__.py",
"keras/applications/imagenet_utils/__init__.py",
"keras/applications/inception_resnet_v2/__init__.py",
"keras/applications/inception_v3/__init__.py",
diff --git a/tensorflow/python/tools/api/generator/api_init_files_v1.bzl b/tensorflow/python/tools/api/generator/api_init_files_v1.bzl
index aa01dab..e5f0f46 100644
--- a/tensorflow/python/tools/api/generator/api_init_files_v1.bzl
+++ b/tensorflow/python/tools/api/generator/api_init_files_v1.bzl
@@ -103,6 +103,7 @@
"keras/activations/__init__.py",
"keras/applications/__init__.py",
"keras/applications/densenet/__init__.py",
+ "keras/applications/efficientnet/__init__.py",
"keras/applications/imagenet_utils/__init__.py",
"keras/applications/inception_resnet_v2/__init__.py",
"keras/applications/inception_v3/__init__.py",
diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.applications.efficientnet.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.applications.efficientnet.pbtxt
new file mode 100644
index 0000000..f4103c5
--- /dev/null
+++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.applications.efficientnet.pbtxt
@@ -0,0 +1,43 @@
+path: "tensorflow.keras.applications.efficientnet"
+tf_module {
+ member_method {
+ name: "EfficientNetB0"
+ 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: "EfficientNetB1"
+ 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: "EfficientNetB2"
+ 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: "EfficientNetB3"
+ 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: "EfficientNetB4"
+ 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: "EfficientNetB5"
+ 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: "EfficientNetB6"
+ 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: "EfficientNetB7"
+ 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\'], "
+ }
+}
diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.applications.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.applications.pbtxt
index 0728c80..900df84 100644
--- a/tensorflow/tools/api/golden/v1/tensorflow.keras.applications.pbtxt
+++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.applications.pbtxt
@@ -5,6 +5,10 @@
mtype: "<type \'module\'>"
}
member {
+ name: "efficientnet"
+ mtype: "<type \'module\'>"
+ }
+ member {
name: "imagenet_utils"
mtype: "<type \'module\'>"
}
@@ -65,6 +69,38 @@
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: "EfficientNetB0"
+ 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: "EfficientNetB1"
+ 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: "EfficientNetB2"
+ 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: "EfficientNetB3"
+ 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: "EfficientNetB4"
+ 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: "EfficientNetB5"
+ 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: "EfficientNetB6"
+ 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: "EfficientNetB7"
+ 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: "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\'], "
}
diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.applications.efficientnet.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.applications.efficientnet.pbtxt
new file mode 100644
index 0000000..f4103c5
--- /dev/null
+++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.applications.efficientnet.pbtxt
@@ -0,0 +1,43 @@
+path: "tensorflow.keras.applications.efficientnet"
+tf_module {
+ member_method {
+ name: "EfficientNetB0"
+ 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: "EfficientNetB1"
+ 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: "EfficientNetB2"
+ 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: "EfficientNetB3"
+ 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: "EfficientNetB4"
+ 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: "EfficientNetB5"
+ 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: "EfficientNetB6"
+ 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: "EfficientNetB7"
+ 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\'], "
+ }
+}
diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.applications.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.applications.pbtxt
index 0728c80..900df84 100644
--- a/tensorflow/tools/api/golden/v2/tensorflow.keras.applications.pbtxt
+++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.applications.pbtxt
@@ -5,6 +5,10 @@
mtype: "<type \'module\'>"
}
member {
+ name: "efficientnet"
+ mtype: "<type \'module\'>"
+ }
+ member {
name: "imagenet_utils"
mtype: "<type \'module\'>"
}
@@ -65,6 +69,38 @@
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: "EfficientNetB0"
+ 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: "EfficientNetB1"
+ 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: "EfficientNetB2"
+ 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: "EfficientNetB3"
+ 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: "EfficientNetB4"
+ 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: "EfficientNetB5"
+ 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: "EfficientNetB6"
+ 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: "EfficientNetB7"
+ 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: "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\'], "
}
