From 51c182c4a35e55b969f9934f9ba85d840cfb4b92 Mon Sep 17 00:00:00 2001
From: Mark Daoust <markdaoust@google.com>
Date: Thu, 20 Feb 2020 10:01:00 -0800
Subject: [PATCH] Add a `classifier_activation` option to keras.applications
Defaults to `softmax` (the current behavior) but now users have the option of turning it off, or setting a different activation function.
PiperOrigin-RevId: 296235461
Change-Id: Iea01b8a2b260ece5e91b4dc1e6e42526136a2066
---
.../python/keras/applications/densenet.py | 29 ++++--
.../python/keras/applications/efficientnet.py | 45 +++++----
.../keras/applications/imagenet_utils.py | 25 +++++
.../keras/applications/inception_resnet_v2.py | 12 ++-
.../python/keras/applications/inception_v3.py | 26 +++--
.../python/keras/applications/mobilenet.py | 13 ++-
.../python/keras/applications/mobilenet_v2.py | 14 ++-
.../python/keras/applications/nasnet.py | 40 +++++---
.../python/keras/applications/resnet.py | 13 ++-
.../python/keras/applications/resnet_v2.py | 98 ++++++++++++++-----
tensorflow/python/keras/applications/vgg16.py | 31 ++++--
tensorflow/python/keras/applications/vgg19.py | 26 +++--
.../python/keras/applications/xception.py | 26 +++--
13 files changed, 285 insertions(+), 113 deletions(-)
diff --git a/tensorflow/python/keras/applications/densenet.py b/tensorflow/python/keras/applications/densenet.py
index 237202ff429..9a7be9a3b7a 100644
--- a/tensorflow/python/keras/applications/densenet.py
+++ b/tensorflow/python/keras/applications/densenet.py
@@ -125,13 +125,16 @@ def conv_block(x, growth_rate, name):
return x
-def DenseNet(blocks,
- include_top=True,
- weights='imagenet',
- input_tensor=None,
- input_shape=None,
- pooling=None,
- classes=1000):
+def DenseNet(
+ blocks,
+ include_top=True,
+ weights='imagenet',
+ input_tensor=None,
+ input_shape=None,
+ pooling=None,
+ classes=1000,
+ classifier_activation='softmax',
+):
"""Instantiates the DenseNet architecture.
Optionally loads weights pre-trained on ImageNet.
@@ -169,13 +172,18 @@ def DenseNet(blocks,
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.
+ 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.
Returns:
- A Keras model instance.
+ A `keras.Model` instance.
Raises:
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
+ ValueError: if `classifier_activation` is not `softmax` or `None` when
+ using a pretrained top layer.
"""
if not (weights in {'imagenet', None} or os.path.exists(weights)):
raise ValueError('The `weights` argument should be either '
@@ -228,7 +236,10 @@ def DenseNet(blocks,
if include_top:
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
- x = layers.Dense(classes, activation='softmax', name='fc1000')(x)
+
+ imagenet_utils.validate_activation(classifier_activation, weights)
+ x = layers.Dense(classes, activation=classifier_activation,
+ name='predictions')(x)
else:
if pooling == 'avg':
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
diff --git a/tensorflow/python/keras/applications/efficientnet.py b/tensorflow/python/keras/applications/efficientnet.py
index f3d0f1e5b0e..11ba3a98b7e 100644
--- a/tensorflow/python/keras/applications/efficientnet.py
+++ b/tensorflow/python/keras/applications/efficientnet.py
@@ -141,21 +141,24 @@ DENSE_KERNEL_INITIALIZER = {
}
-def EfficientNet(width_coefficient,
- depth_coefficient,
- default_size,
- dropout_rate=0.2,
- drop_connect_rate=0.2,
- depth_divisor=8,
- activation='swish',
- blocks_args='default',
- model_name='efficientnet',
- include_top=True,
- weights='imagenet',
- input_tensor=None,
- input_shape=None,
- pooling=None,
- classes=1000):
+def EfficientNet(
+ width_coefficient,
+ depth_coefficient,
+ default_size,
+ dropout_rate=0.2,
+ drop_connect_rate=0.2,
+ depth_divisor=8,
+ activation='swish',
+ blocks_args='default',
+ model_name='efficientnet',
+ include_top=True,
+ weights='imagenet',
+ input_tensor=None,
+ input_shape=None,
+ pooling=None,
+ classes=1000,
+ classifier_activation='softmax',
+):
"""Instantiates the EfficientNet architecture using given scaling coefficients.
Optionally loads weights pre-trained on ImageNet.
@@ -197,13 +200,18 @@ def EfficientNet(width_coefficient,
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.
+ 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.
Returns:
- A Keras model instance.
+ A `keras.Model` instance.
Raises:
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
+ ValueError: if `classifier_activation` is not `softmax` or `None` when
+ using a pretrained top layer.
"""
if blocks_args == 'default':
blocks_args = DEFAULT_BLOCKS_ARGS
@@ -307,11 +315,12 @@ def EfficientNet(width_coefficient,
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
if dropout_rate > 0:
x = layers.Dropout(dropout_rate, name='top_dropout')(x)
+ imagenet_utils.validate_activation(classifier_activation, weights)
x = layers.Dense(
classes,
- activation='softmax',
+ activation=classifier_activation,
kernel_initializer=DENSE_KERNEL_INITIALIZER,
- name='probs')(x)
+ name='predictions')(x)
else:
if pooling == 'avg':
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
diff --git a/tensorflow/python/keras/applications/imagenet_utils.py b/tensorflow/python/keras/applications/imagenet_utils.py
index 206be8406ee..55299ebfa50 100644
--- a/tensorflow/python/keras/applications/imagenet_utils.py
+++ b/tensorflow/python/keras/applications/imagenet_utils.py
@@ -22,6 +22,7 @@ import warnings
import numpy as np
+from tensorflow.python.keras import activations
from tensorflow.python.keras import backend
from tensorflow.python.keras.utils import data_utils
from tensorflow.python.util.tf_export import keras_export
@@ -355,3 +356,27 @@ def correct_pad(inputs, kernel_size):
correct = (kernel_size[0] // 2, kernel_size[1] // 2)
return ((correct[0] - adjust[0], correct[0]),
(correct[1] - adjust[1], correct[1]))
+
+
+def validate_activation(classifier_activation, weights):
+ """validates that the classifer_activation is compatible with the weights.
+
+ Args:
+ classifier_activation: str or callable activation function
+ weights: The pretrained weights to load.
+
+ Raises:
+ ValueError: if an activation other than `None` or `softmax` are used with
+ pretrained weights.
+ """
+ if weights is None:
+ return
+
+ classifier_activation = activations.get(classifier_activation)
+ if classifier_activation not in [
+ activations.get('softmax'),
+ activations.get(None)
+ ]:
+ raise ValueError('Only `None` and `softmax` activations are allowed '
+ 'for the `classifier_activation` argument when using '
+ 'pretrained weights, with `include_top=True`')
diff --git a/tensorflow/python/keras/applications/inception_resnet_v2.py b/tensorflow/python/keras/applications/inception_resnet_v2.py
index 092343144c7..ab8ab71e3b0 100644
--- a/tensorflow/python/keras/applications/inception_resnet_v2.py
+++ b/tensorflow/python/keras/applications/inception_resnet_v2.py
@@ -48,6 +48,7 @@ def InceptionResNetV2(include_top=True,
input_shape=None,
pooling=None,
classes=1000,
+ classifier_activation='softmax',
**kwargs):
"""Instantiates the Inception-ResNet v2 architecture.
@@ -82,14 +83,19 @@ def InceptionResNetV2(include_top=True,
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.
+ 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.
**kwargs: For backwards compatibility only.
Returns:
- A Keras `Model` instance.
+ A `keras.Model` instance.
Raises:
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
+ ValueError: if `classifier_activation` is not `softmax` or `None` when
+ using a pretrained top layer.
"""
if 'layers' in kwargs:
global layers
@@ -189,7 +195,9 @@ def InceptionResNetV2(include_top=True,
if include_top:
# Classification block
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
- x = layers.Dense(classes, activation='softmax', name='predictions')(x)
+ imagenet_utils.validate_activation(classifier_activation, weights)
+ x = layers.Dense(classes, activation=classifier_activation,
+ name='predictions')(x)
else:
if pooling == 'avg':
x = layers.GlobalAveragePooling2D()(x)
diff --git a/tensorflow/python/keras/applications/inception_v3.py b/tensorflow/python/keras/applications/inception_v3.py
index ecec195dff6..f8a56e62234 100644
--- a/tensorflow/python/keras/applications/inception_v3.py
+++ b/tensorflow/python/keras/applications/inception_v3.py
@@ -44,12 +44,15 @@ WEIGHTS_PATH_NO_TOP = (
@keras_export('keras.applications.inception_v3.InceptionV3',
'keras.applications.InceptionV3')
-def InceptionV3(include_top=True,
- weights='imagenet',
- input_tensor=None,
- input_shape=None,
- pooling=None,
- classes=1000):
+def InceptionV3(
+ include_top=True,
+ weights='imagenet',
+ input_tensor=None,
+ input_shape=None,
+ pooling=None,
+ classes=1000,
+ classifier_activation='softmax',
+):
"""Instantiates the Inception v3 architecture.
Reference paper:
@@ -89,13 +92,18 @@ def InceptionV3(include_top=True,
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. Default to 1000.
+ 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.
Returns:
- A Keras `tf.keras.Model` instance.
+ A `keras.Model` instance.
Raises:
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
+ ValueError: if `classifier_activation` is not `softmax` or `None` when
+ using a pretrained top layer.
"""
if not (weights in {'imagenet', None} or os.path.exists(weights)):
raise ValueError('The `weights` argument should be either '
@@ -309,7 +317,9 @@ def InceptionV3(include_top=True,
if include_top:
# Classification block
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
- x = layers.Dense(classes, activation='softmax', name='predictions')(x)
+ imagenet_utils.validate_activation(classifier_activation, weights)
+ x = layers.Dense(classes, activation=classifier_activation,
+ name='predictions')(x)
else:
if pooling == 'avg':
x = layers.GlobalAveragePooling2D()(x)
diff --git a/tensorflow/python/keras/applications/mobilenet.py b/tensorflow/python/keras/applications/mobilenet.py
index e64efa53815..224e8c84496 100644
--- a/tensorflow/python/keras/applications/mobilenet.py
+++ b/tensorflow/python/keras/applications/mobilenet.py
@@ -90,6 +90,7 @@ def MobileNet(input_shape=None,
input_tensor=None,
pooling=None,
classes=1000,
+ classifier_activation='softmax',
**kwargs):
"""Instantiates the MobileNet architecture.
@@ -138,14 +139,18 @@ def MobileNet(input_shape=None,
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.
+ 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.
**kwargs: For backwards compatibility only.
-
Returns:
- A `tf.keras.Model` instance.
+ A `keras.Model` instance.
Raises:
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
+ ValueError: if `classifier_activation` is not `softmax` or `None` when
+ using a pretrained top layer.
"""
if 'layers' in kwargs:
global layers
@@ -252,7 +257,9 @@ def MobileNet(input_shape=None,
x = layers.Dropout(dropout, name='dropout')(x)
x = layers.Conv2D(classes, (1, 1), padding='same', name='conv_preds')(x)
x = layers.Reshape((classes,), name='reshape_2')(x)
- x = layers.Activation('softmax', name='act_softmax')(x)
+ imagenet_utils.validate_activation(classifier_activation, weights)
+ x = layers.Activation(activation=classifier_activation,
+ name='predictions')(x)
else:
if pooling == 'avg':
x = layers.GlobalAveragePooling2D()(x)
diff --git a/tensorflow/python/keras/applications/mobilenet_v2.py b/tensorflow/python/keras/applications/mobilenet_v2.py
index 186b6e3db61..a983f6d7e46 100644
--- a/tensorflow/python/keras/applications/mobilenet_v2.py
+++ b/tensorflow/python/keras/applications/mobilenet_v2.py
@@ -85,7 +85,6 @@ from tensorflow.python.keras.utils import layer_utils
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util.tf_export import keras_export
-
BASE_WEIGHT_PATH = ('https://storage.googleapis.com/tensorflow/'
'keras-applications/mobilenet_v2/')
@@ -99,6 +98,7 @@ def MobileNetV2(input_shape=None,
input_tensor=None,
pooling=None,
classes=1000,
+ classifier_activation='softmax',
**kwargs):
"""Instantiates the MobileNetV2 architecture.
@@ -152,6 +152,9 @@ def MobileNetV2(input_shape=None,
classes: Integer, optional number of classes to classify images
into, only to be specified if `include_top` is True, and
if no `weights` argument is specified.
+ 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.
**kwargs: For backwards compatibility only.
Returns:
@@ -161,6 +164,8 @@ def MobileNetV2(input_shape=None,
ValueError: in case of invalid argument for `weights`,
or invalid input shape or invalid alpha, rows when
weights='imagenet'
+ ValueError: if `classifier_activation` is not `softmax` or `None` when
+ using a pretrained top layer.
"""
if 'layers' in kwargs:
global layers
@@ -360,9 +365,10 @@ def MobileNetV2(input_shape=None,
if include_top:
x = layers.GlobalAveragePooling2D()(x)
- x = layers.Dense(
- classes, activation='softmax', use_bias=True, name='Logits')(
- x)
+ imagenet_utils.validate_activation(classifier_activation, weights)
+ x = layers.Dense(classes, activation=classifier_activation,
+ name='predictions')(x)
+
else:
if pooling == 'avg':
x = layers.GlobalAveragePooling2D()(x)
diff --git a/tensorflow/python/keras/applications/nasnet.py b/tensorflow/python/keras/applications/nasnet.py
index 0a693b83652..a29d5f4c380 100644
--- a/tensorflow/python/keras/applications/nasnet.py
+++ b/tensorflow/python/keras/applications/nasnet.py
@@ -61,18 +61,21 @@ NASNET_LARGE_WEIGHT_PATH = BASE_WEIGHTS_PATH + 'NASNet-large.h5'
NASNET_LARGE_WEIGHT_PATH_NO_TOP = BASE_WEIGHTS_PATH + 'NASNet-large-no-top.h5'
-def NASNet(input_shape=None,
- penultimate_filters=4032,
- num_blocks=6,
- stem_block_filters=96,
- skip_reduction=True,
- filter_multiplier=2,
- include_top=True,
- weights=None,
- input_tensor=None,
- pooling=None,
- classes=1000,
- default_size=None):
+def NASNet(
+ input_shape=None,
+ penultimate_filters=4032,
+ num_blocks=6,
+ stem_block_filters=96,
+ skip_reduction=True,
+ filter_multiplier=2,
+ include_top=True,
+ weights=None,
+ input_tensor=None,
+ pooling=None,
+ classes=1000,
+ default_size=None,
+ classifier_activation='softmax',
+):
"""Instantiates a NASNet model.
Optionally loads weights pre-trained on ImageNet.
@@ -127,13 +130,18 @@ def NASNet(input_shape=None,
into, only to be specified if `include_top` is True, and
if no `weights` argument is specified.
default_size: Specifies the default image size of the model
+ 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.
Returns:
- A Keras model instance.
+ A `keras.Model` instance.
Raises:
ValueError: In case of invalid argument for `weights`,
- invalid input shape or invalid `penultimate_filters` value.
+ invalid input shape or invalid `penultimate_filters` value.
+ ValueError: if `classifier_activation` is not `softmax` or `None` when
+ using a pretrained top layer.
"""
if not (weights in {'imagenet', None} or os.path.exists(weights)):
raise ValueError('The `weights` argument should be either '
@@ -247,7 +255,9 @@ def NASNet(input_shape=None,
if include_top:
x = layers.GlobalAveragePooling2D()(x)
- x = layers.Dense(classes, activation='softmax', name='predictions')(x)
+ imagenet_utils.validate_activation(classifier_activation, weights)
+ x = layers.Dense(classes, activation=classifier_activation,
+ name='predictions')(x)
else:
if pooling == 'avg':
x = layers.GlobalAveragePooling2D()(x)
diff --git a/tensorflow/python/keras/applications/resnet.py b/tensorflow/python/keras/applications/resnet.py
index d30b3cca55e..86d26695373 100644
--- a/tensorflow/python/keras/applications/resnet.py
+++ b/tensorflow/python/keras/applications/resnet.py
@@ -61,6 +61,7 @@ def ResNet(stack_fn,
input_shape=None,
pooling=None,
classes=1000,
+ classifier_activation='softmax',
**kwargs):
"""Instantiates the ResNet, ResNetV2, and ResNeXt architecture.
@@ -103,14 +104,18 @@ def ResNet(stack_fn,
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.
+ 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.
**kwargs: For backwards compatibility only.
-
Returns:
- A Keras model instance.
+ A `keras.Model` instance.
Raises:
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
+ ValueError: if `classifier_activation` is not `softmax` or `None` when
+ using a pretrained top layer.
"""
if 'layers' in kwargs:
global layers
@@ -167,7 +172,9 @@ def ResNet(stack_fn,
if include_top:
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
- x = layers.Dense(classes, activation='softmax', name='probs')(x)
+ imagenet_utils.validate_activation(classifier_activation, weights)
+ x = layers.Dense(classes, activation=classifier_activation,
+ name='predictions')(x)
else:
if pooling == 'avg':
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
diff --git a/tensorflow/python/keras/applications/resnet_v2.py b/tensorflow/python/keras/applications/resnet_v2.py
index ce56fbb19cb..2e31017dfa9 100644
--- a/tensorflow/python/keras/applications/resnet_v2.py
+++ b/tensorflow/python/keras/applications/resnet_v2.py
@@ -25,56 +25,101 @@ from tensorflow.python.util.tf_export import keras_export
@keras_export('keras.applications.resnet_v2.ResNet50V2',
'keras.applications.ResNet50V2')
-def ResNet50V2(include_top=True,
- weights='imagenet',
- input_tensor=None,
- input_shape=None,
- pooling=None,
- classes=1000):
+def ResNet50V2(
+ include_top=True,
+ weights='imagenet',
+ input_tensor=None,
+ input_shape=None,
+ pooling=None,
+ classes=1000,
+ classifier_activation='softmax',
+):
"""Instantiates the ResNet50V2 architecture."""
def stack_fn(x):
x = resnet.stack2(x, 64, 3, name='conv2')
x = resnet.stack2(x, 128, 4, name='conv3')
x = resnet.stack2(x, 256, 6, name='conv4')
return resnet.stack2(x, 512, 3, stride1=1, name='conv5')
- return resnet.ResNet(stack_fn, True, True, 'resnet50v2', include_top, weights,
- input_tensor, input_shape, pooling, classes)
+
+ return resnet.ResNet(
+ stack_fn,
+ True,
+ True,
+ 'resnet50v2',
+ include_top,
+ weights,
+ input_tensor,
+ input_shape,
+ pooling,
+ classes,
+ classifier_activation=classifier_activation,
+ )
@keras_export('keras.applications.resnet_v2.ResNet101V2',
'keras.applications.ResNet101V2')
-def ResNet101V2(include_top=True,
- weights='imagenet',
- input_tensor=None,
- input_shape=None,
- pooling=None,
- classes=1000):
+def ResNet101V2(
+ include_top=True,
+ weights='imagenet',
+ input_tensor=None,
+ input_shape=None,
+ pooling=None,
+ classes=1000,
+ classifier_activation='softmax',
+):
"""Instantiates the ResNet101V2 architecture."""
def stack_fn(x):
x = resnet.stack2(x, 64, 3, name='conv2')
x = resnet.stack2(x, 128, 4, name='conv3')
x = resnet.stack2(x, 256, 23, name='conv4')
return resnet.stack2(x, 512, 3, stride1=1, name='conv5')
- return resnet.ResNet(stack_fn, True, True, 'resnet101v2', include_top,
- weights, input_tensor, input_shape, pooling, classes)
+
+ return resnet.ResNet(
+ stack_fn,
+ True,
+ True,
+ 'resnet101v2',
+ include_top,
+ weights,
+ input_tensor,
+ input_shape,
+ pooling,
+ classes,
+ classifier_activation=classifier_activation,
+ )
@keras_export('keras.applications.resnet_v2.ResNet152V2',
'keras.applications.ResNet152V2')
-def ResNet152V2(include_top=True,
- weights='imagenet',
- input_tensor=None,
- input_shape=None,
- pooling=None,
- classes=1000):
+def ResNet152V2(
+ include_top=True,
+ weights='imagenet',
+ input_tensor=None,
+ input_shape=None,
+ pooling=None,
+ classes=1000,
+ classifier_activation='softmax',
+):
"""Instantiates the ResNet152V2 architecture."""
def stack_fn(x):
x = resnet.stack2(x, 64, 3, name='conv2')
x = resnet.stack2(x, 128, 8, name='conv3')
x = resnet.stack2(x, 256, 36, name='conv4')
return resnet.stack2(x, 512, 3, stride1=1, name='conv5')
- return resnet.ResNet(stack_fn, True, True, 'resnet152v2', include_top,
- weights, input_tensor, input_shape, pooling, classes)
+
+ return resnet.ResNet(
+ stack_fn,
+ True,
+ True,
+ 'resnet152v2',
+ include_top,
+ weights,
+ input_tensor,
+ input_shape,
+ pooling,
+ classes,
+ classifier_activation=classifier_activation,
+ )
@keras_export('keras.applications.resnet_v2.preprocess_input')
@@ -123,9 +168,12 @@ DOC = """
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.
+ 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.
Returns:
- A Keras model instance.
+ A `keras.Model` instance.
"""
setattr(ResNet50V2, '__doc__', ResNet50V2.__doc__ + DOC)
diff --git a/tensorflow/python/keras/applications/vgg16.py b/tensorflow/python/keras/applications/vgg16.py
index 958ed955106..e268a592833 100644
--- a/tensorflow/python/keras/applications/vgg16.py
+++ b/tensorflow/python/keras/applications/vgg16.py
@@ -37,12 +37,15 @@ WEIGHTS_PATH_NO_TOP = ('https://storage.googleapis.com/tensorflow/'
@keras_export('keras.applications.vgg16.VGG16', 'keras.applications.VGG16')
-def VGG16(include_top=True,
- weights='imagenet',
- input_tensor=None,
- input_shape=None,
- pooling=None,
- classes=1000):
+def VGG16(
+ include_top=True,
+ weights='imagenet',
+ input_tensor=None,
+ input_shape=None,
+ pooling=None,
+ classes=1000,
+ classifier_activation='softmax',
+):
"""Instantiates the VGG16 model.
By default, it loads weights pre-trained on ImageNet. Check 'weights' for
@@ -85,13 +88,18 @@ def VGG16(include_top=True,
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.
+ 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.
Returns:
- A Keras model instance.
+ A `keras.Model` instance.
Raises:
- ValueError: in case of invalid argument for `weights`,
- or invalid input shape.
+ ValueError: in case of invalid argument for `weights`,
+ or invalid input shape.
+ ValueError: if `classifier_activation` is not `softmax` or `None` when
+ using a pretrained top layer.
"""
if not (weights in {'imagenet', None} or os.path.exists(weights)):
raise ValueError('The `weights` argument should be either '
@@ -165,7 +173,10 @@ def VGG16(include_top=True,
x = layers.Flatten(name='flatten')(x)
x = layers.Dense(4096, activation='relu', name='fc1')(x)
x = layers.Dense(4096, activation='relu', name='fc2')(x)
- x = layers.Dense(classes, activation='softmax', name='predictions')(x)
+
+ imagenet_utils.validate_activation(classifier_activation, weights)
+ x = layers.Dense(classes, activation=classifier_activation,
+ name='predictions')(x)
else:
if pooling == 'avg':
x = layers.GlobalAveragePooling2D()(x)
diff --git a/tensorflow/python/keras/applications/vgg19.py b/tensorflow/python/keras/applications/vgg19.py
index 808580ada07..8d25dc0e42f 100644
--- a/tensorflow/python/keras/applications/vgg19.py
+++ b/tensorflow/python/keras/applications/vgg19.py
@@ -42,12 +42,15 @@ WEIGHTS_PATH_NO_TOP = ('https://storage.googleapis.com/tensorflow/'
@keras_export('keras.applications.vgg19.VGG19', 'keras.applications.VGG19')
-def VGG19(include_top=True,
- weights='imagenet',
- input_tensor=None,
- input_shape=None,
- pooling=None,
- classes=1000):
+def VGG19(
+ include_top=True,
+ weights='imagenet',
+ input_tensor=None,
+ input_shape=None,
+ pooling=None,
+ classes=1000,
+ classifier_activation='softmax',
+):
"""Instantiates the VGG19 architecture.
By default, it loads weights pre-trained on ImageNet. Check 'weights' for
@@ -90,13 +93,18 @@ def VGG19(include_top=True,
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.
+ 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.
Returns:
- A Keras model instance.
+ A `keras.Model` instance.
Raises:
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
+ ValueError: if `classifier_activation` is not `softmax` or `None` when
+ using a pretrained top layer.
"""
if not (weights in {'imagenet', None} or os.path.exists(weights)):
raise ValueError('The `weights` argument should be either '
@@ -176,7 +184,9 @@ def VGG19(include_top=True,
x = layers.Flatten(name='flatten')(x)
x = layers.Dense(4096, activation='relu', name='fc1')(x)
x = layers.Dense(4096, activation='relu', name='fc2')(x)
- x = layers.Dense(classes, activation='softmax', name='predictions')(x)
+ imagenet_utils.validate_activation(classifier_activation, weights)
+ x = layers.Dense(classes, activation=classifier_activation,
+ name='predictions')(x)
else:
if pooling == 'avg':
x = layers.GlobalAveragePooling2D()(x)
diff --git a/tensorflow/python/keras/applications/xception.py b/tensorflow/python/keras/applications/xception.py
index 47f386cc721..7f6602b90d1 100644
--- a/tensorflow/python/keras/applications/xception.py
+++ b/tensorflow/python/keras/applications/xception.py
@@ -48,12 +48,15 @@ TF_WEIGHTS_PATH_NO_TOP = (
@keras_export('keras.applications.xception.Xception',
'keras.applications.Xception')
-def Xception(include_top=True,
- weights='imagenet',
- input_tensor=None,
- input_shape=None,
- pooling=None,
- classes=1000):
+def Xception(
+ include_top=True,
+ weights='imagenet',
+ input_tensor=None,
+ input_shape=None,
+ pooling=None,
+ classes=1000,
+ classifier_activation='softmax',
+):
"""Instantiates the Xception architecture.
Optionally loads weights pre-trained on ImageNet.
@@ -90,13 +93,18 @@ def Xception(include_top=True,
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.
+ 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.
Returns:
- A Keras model instance.
+ A `keras.Model` instance.
Raises:
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
+ ValueError: if `classifier_activation` is not `softmax` or `None` when
+ using a pretrained top layer.
"""
if not (weights in {'imagenet', None} or os.path.exists(weights)):
raise ValueError('The `weights` argument should be either '
@@ -260,7 +268,9 @@ def Xception(include_top=True,
if include_top:
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
- x = layers.Dense(classes, activation='softmax', name='predictions')(x)
+ imagenet_utils.validate_activation(classifier_activation, weights)
+ x = layers.Dense(classes, activation=classifier_activation,
+ name='predictions')(x)
else:
if pooling == 'avg':
x = layers.GlobalAveragePooling2D()(x)