Module keras.applications.densenet
DenseNet models for Keras.
Reference
- Densely Connected Convolutional Networks (CVPR 2017)
Expand source code
# Copyright 2018 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.
# ==============================================================================
# pylint: disable=invalid-name
"""DenseNet models for Keras.
Reference:
- [Densely Connected Convolutional Networks](
https://arxiv.org/abs/1608.06993) (CVPR 2017)
"""
import tensorflow.compat.v2 as tf
from keras import backend
from keras.applications import imagenet_utils
from keras.engine import training
from keras.layers import VersionAwareLayers
from keras.utils import data_utils
from keras.utils import layer_utils
from tensorflow.python.util.tf_export import keras_export
BASE_WEIGHTS_PATH = ('https://storage.googleapis.com/tensorflow/'
'keras-applications/densenet/')
DENSENET121_WEIGHT_PATH = (
BASE_WEIGHTS_PATH + 'densenet121_weights_tf_dim_ordering_tf_kernels.h5')
DENSENET121_WEIGHT_PATH_NO_TOP = (
BASE_WEIGHTS_PATH +
'densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5')
DENSENET169_WEIGHT_PATH = (
BASE_WEIGHTS_PATH + 'densenet169_weights_tf_dim_ordering_tf_kernels.h5')
DENSENET169_WEIGHT_PATH_NO_TOP = (
BASE_WEIGHTS_PATH +
'densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5')
DENSENET201_WEIGHT_PATH = (
BASE_WEIGHTS_PATH + 'densenet201_weights_tf_dim_ordering_tf_kernels.h5')
DENSENET201_WEIGHT_PATH_NO_TOP = (
BASE_WEIGHTS_PATH +
'densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5')
layers = VersionAwareLayers()
def dense_block(x, blocks, name):
"""A dense block.
Args:
x: input tensor.
blocks: integer, the number of building blocks.
name: string, block label.
Returns:
Output tensor for the block.
"""
for i in range(blocks):
x = conv_block(x, 32, name=name + '_block' + str(i + 1))
return x
def transition_block(x, reduction, name):
"""A transition block.
Args:
x: input tensor.
reduction: float, compression rate at transition layers.
name: string, block label.
Returns:
output tensor for the block.
"""
bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1
x = layers.BatchNormalization(
axis=bn_axis, epsilon=1.001e-5, name=name + '_bn')(
x)
x = layers.Activation('relu', name=name + '_relu')(x)
x = layers.Conv2D(
int(backend.int_shape(x)[bn_axis] * reduction),
1,
use_bias=False,
name=name + '_conv')(
x)
x = layers.AveragePooling2D(2, strides=2, name=name + '_pool')(x)
return x
def conv_block(x, growth_rate, name):
"""A building block for a dense block.
Args:
x: input tensor.
growth_rate: float, growth rate at dense layers.
name: string, block label.
Returns:
Output tensor for the block.
"""
bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1
x1 = layers.BatchNormalization(
axis=bn_axis, epsilon=1.001e-5, name=name + '_0_bn')(
x)
x1 = layers.Activation('relu', name=name + '_0_relu')(x1)
x1 = layers.Conv2D(
4 * growth_rate, 1, use_bias=False, name=name + '_1_conv')(
x1)
x1 = layers.BatchNormalization(
axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(
x1)
x1 = layers.Activation('relu', name=name + '_1_relu')(x1)
x1 = layers.Conv2D(
growth_rate, 3, padding='same', use_bias=False, name=name + '_2_conv')(
x1)
x = layers.Concatenate(axis=bn_axis, name=name + '_concat')([x, x1])
return x
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.
Reference:
- [Densely Connected Convolutional Networks](
https://arxiv.org/abs/1608.06993) (CVPR 2017)
This function returns a Keras image classification model,
optionally loaded with weights pre-trained on ImageNet.
For image classification use cases, see
[this page for detailed examples](
https://keras.io/api/applications/#usage-examples-for-image-classification-models).
For transfer learning use cases, make sure to read the
[guide to transfer learning & fine-tuning](
https://keras.io/guides/transfer_learning/).
Note: each Keras Application expects a specific kind of input preprocessing.
For DenseNet, call `tf.keras.applications.densenet.preprocess_input` on your
inputs before passing them to the model.
`densenet.preprocess_input` will scale pixels between 0 and 1 and then
will normalize each channel with respect to the ImageNet dataset statistics.
Args:
blocks: numbers of building blocks for the four dense layers.
include_top: whether to include the fully-connected
layer at the top of the network.
weights: one of `None` (random initialization),
'imagenet' (pre-training on ImageNet),
or the path to the weights file to be loaded.
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 (otherwise the input shape
has to be `(224, 224, 3)` (with `'channels_last'` data format)
or `(3, 224, 224)` (with `'channels_first'` data format).
It should have exactly 3 inputs channels,
and width and height should be no smaller than 32.
E.g. `(200, 200, 3)` would be one valid value.
pooling: optional pooling mode for feature extraction
when `include_top` is `False`.
- `None` means that the output of the model will be
the 4D tensor output of the
last convolutional block.
- `avg` means that global average pooling
will be applied to the output of the
last convolutional block, 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.
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.
When loading pretrained weights, `classifier_activation` can only
be `None` or `"softmax"`.
Returns:
A `keras.Model` instance.
"""
if not (weights in {'imagenet', None} or tf.io.gfile.exists(weights)):
raise ValueError('The `weights` argument should be either '
'`None` (random initialization), `imagenet` '
'(pre-training on ImageNet), '
'or the path to the weights file to be loaded.')
if weights == 'imagenet' and include_top and classes != 1000:
raise ValueError('If using `weights` as `"imagenet"` with `include_top`'
' as true, `classes` should be 1000')
# Determine proper input shape
input_shape = imagenet_utils.obtain_input_shape(
input_shape,
default_size=224,
min_size=32,
data_format=backend.image_data_format(),
require_flatten=include_top,
weights=weights)
if input_tensor is None:
img_input = layers.Input(shape=input_shape)
else:
if not backend.is_keras_tensor(input_tensor):
img_input = layers.Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1
x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input)
x = layers.Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x)
x = layers.BatchNormalization(
axis=bn_axis, epsilon=1.001e-5, name='conv1/bn')(
x)
x = layers.Activation('relu', name='conv1/relu')(x)
x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)))(x)
x = layers.MaxPooling2D(3, strides=2, name='pool1')(x)
x = dense_block(x, blocks[0], name='conv2')
x = transition_block(x, 0.5, name='pool2')
x = dense_block(x, blocks[1], name='conv3')
x = transition_block(x, 0.5, name='pool3')
x = dense_block(x, blocks[2], name='conv4')
x = transition_block(x, 0.5, name='pool4')
x = dense_block(x, blocks[3], name='conv5')
x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name='bn')(x)
x = layers.Activation('relu', name='relu')(x)
if include_top:
x = layers.GlobalAveragePooling2D(name='avg_pool')(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)
elif pooling == 'max':
x = layers.GlobalMaxPooling2D(name='max_pool')(x)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = layer_utils.get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
if blocks == [6, 12, 24, 16]:
model = training.Model(inputs, x, name='densenet121')
elif blocks == [6, 12, 32, 32]:
model = training.Model(inputs, x, name='densenet169')
elif blocks == [6, 12, 48, 32]:
model = training.Model(inputs, x, name='densenet201')
else:
model = training.Model(inputs, x, name='densenet')
# Load weights.
if weights == 'imagenet':
if include_top:
if blocks == [6, 12, 24, 16]:
weights_path = data_utils.get_file(
'densenet121_weights_tf_dim_ordering_tf_kernels.h5',
DENSENET121_WEIGHT_PATH,
cache_subdir='models',
file_hash='9d60b8095a5708f2dcce2bca79d332c7')
elif blocks == [6, 12, 32, 32]:
weights_path = data_utils.get_file(
'densenet169_weights_tf_dim_ordering_tf_kernels.h5',
DENSENET169_WEIGHT_PATH,
cache_subdir='models',
file_hash='d699b8f76981ab1b30698df4c175e90b')
elif blocks == [6, 12, 48, 32]:
weights_path = data_utils.get_file(
'densenet201_weights_tf_dim_ordering_tf_kernels.h5',
DENSENET201_WEIGHT_PATH,
cache_subdir='models',
file_hash='1ceb130c1ea1b78c3bf6114dbdfd8807')
else:
if blocks == [6, 12, 24, 16]:
weights_path = data_utils.get_file(
'densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5',
DENSENET121_WEIGHT_PATH_NO_TOP,
cache_subdir='models',
file_hash='30ee3e1110167f948a6b9946edeeb738')
elif blocks == [6, 12, 32, 32]:
weights_path = data_utils.get_file(
'densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5',
DENSENET169_WEIGHT_PATH_NO_TOP,
cache_subdir='models',
file_hash='b8c4d4c20dd625c148057b9ff1c1176b')
elif blocks == [6, 12, 48, 32]:
weights_path = data_utils.get_file(
'densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5',
DENSENET201_WEIGHT_PATH_NO_TOP,
cache_subdir='models',
file_hash='c13680b51ded0fb44dff2d8f86ac8bb1')
model.load_weights(weights_path)
elif weights is not None:
model.load_weights(weights)
return model
@keras_export('keras.applications.densenet.DenseNet121',
'keras.applications.DenseNet121')
def DenseNet121(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000):
"""Instantiates the Densenet121 architecture."""
return DenseNet([6, 12, 24, 16], include_top, weights, input_tensor,
input_shape, pooling, classes)
@keras_export('keras.applications.densenet.DenseNet169',
'keras.applications.DenseNet169')
def DenseNet169(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000):
"""Instantiates the Densenet169 architecture."""
return DenseNet([6, 12, 32, 32], include_top, weights, input_tensor,
input_shape, pooling, classes)
@keras_export('keras.applications.densenet.DenseNet201',
'keras.applications.DenseNet201')
def DenseNet201(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000):
"""Instantiates the Densenet201 architecture."""
return DenseNet([6, 12, 48, 32], include_top, weights, input_tensor,
input_shape, pooling, classes)
@keras_export('keras.applications.densenet.preprocess_input')
def preprocess_input(x, data_format=None):
return imagenet_utils.preprocess_input(
x, data_format=data_format, mode='torch')
@keras_export('keras.applications.densenet.decode_predictions')
def decode_predictions(preds, top=5):
return imagenet_utils.decode_predictions(preds, top=top)
preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
mode='',
ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TORCH,
error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC)
decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
DOC = """
Reference:
- [Densely Connected Convolutional Networks](
https://arxiv.org/abs/1608.06993) (CVPR 2017)
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`.
Note: each Keras Application expects a specific kind of input preprocessing.
For DenseNet, call `tf.keras.applications.densenet.preprocess_input` on your
inputs before passing them to the model.
Args:
include_top: whether to include the fully-connected
layer at the top of the network.
weights: one of `None` (random initialization),
'imagenet' (pre-training on ImageNet),
or the path to the weights file to be loaded.
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 (otherwise the input shape
has to be `(224, 224, 3)` (with `'channels_last'` data format)
or `(3, 224, 224)` (with `'channels_first'` data format).
It should have exactly 3 inputs channels,
and width and height should be no smaller than 32.
E.g. `(200, 200, 3)` would be one valid value.
pooling: Optional pooling mode for feature extraction
when `include_top` is `False`.
- `None` means that the output of the model will be
the 4D tensor output of the
last convolutional block.
- `avg` means that global average pooling
will be applied to the output of the
last convolutional block, 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.
Returns:
A Keras model instance.
"""
setattr(DenseNet121, '__doc__', DenseNet121.__doc__ + DOC)
setattr(DenseNet169, '__doc__', DenseNet169.__doc__ + DOC)
setattr(DenseNet201, '__doc__', DenseNet201.__doc__ + DOC)
Functions
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.
Reference: - Densely Connected Convolutional Networks (CVPR 2017)
This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet.
For image classification use cases, see this page for detailed examples.
For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.
Note: each Keras Application expects a specific kind of input preprocessing. For DenseNet, call
tf.keras.applications.densenet.preprocess_input
on your inputs before passing them to the model.densenet.preprocess_input
will scale pixels between 0 and 1 and then will normalize each channel with respect to the ImageNet dataset statistics.Args
blocks
- numbers of building blocks for the four dense layers.
include_top
- whether to include the fully-connected layer at the top of the network.
weights
- one of
None
(random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. 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 (otherwise the input shape has to be(224, 224, 3)
(with'channels_last'
data format) or(3, 224, 224)
(with'channels_first'
data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g.(200, 200, 3)
would be one valid value. pooling
- optional pooling mode for feature extraction
when
include_top
isFalse
. -None
means that the output of the model will be the 4D tensor output of the last convolutional block. -avg
means that global average pooling will be applied to the output of the last convolutional block, 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 noweights
argument is specified. classifier_activation
- A
str
or callable. The activation function to use on the "top" layer. Ignored unlessinclude_top=True
. Setclassifier_activation=None
to return the logits of the "top" layer. When loading pretrained weights,classifier_activation
can only beNone
or"softmax"
.
Returns
A
keras.Model
instance.Expand source code
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. Reference: - [Densely Connected Convolutional Networks]( https://arxiv.org/abs/1608.06993) (CVPR 2017) This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet. For image classification use cases, see [this page for detailed examples]( https://keras.io/api/applications/#usage-examples-for-image-classification-models). For transfer learning use cases, make sure to read the [guide to transfer learning & fine-tuning]( https://keras.io/guides/transfer_learning/). Note: each Keras Application expects a specific kind of input preprocessing. For DenseNet, call `tf.keras.applications.densenet.preprocess_input` on your inputs before passing them to the model. `densenet.preprocess_input` will scale pixels between 0 and 1 and then will normalize each channel with respect to the ImageNet dataset statistics. Args: blocks: numbers of building blocks for the four dense layers. include_top: whether to include the fully-connected layer at the top of the network. weights: one of `None` (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. 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 (otherwise the input shape has to be `(224, 224, 3)` (with `'channels_last'` data format) or `(3, 224, 224)` (with `'channels_first'` data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. `(200, 200, 3)` would be one valid value. pooling: optional pooling mode for feature extraction when `include_top` is `False`. - `None` means that the output of the model will be the 4D tensor output of the last convolutional block. - `avg` means that global average pooling will be applied to the output of the last convolutional block, 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. 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. When loading pretrained weights, `classifier_activation` can only be `None` or `"softmax"`. Returns: A `keras.Model` instance. """ if not (weights in {'imagenet', None} or tf.io.gfile.exists(weights)): raise ValueError('The `weights` argument should be either ' '`None` (random initialization), `imagenet` ' '(pre-training on ImageNet), ' 'or the path to the weights file to be loaded.') if weights == 'imagenet' and include_top and classes != 1000: raise ValueError('If using `weights` as `"imagenet"` with `include_top`' ' as true, `classes` should be 1000') # Determine proper input shape input_shape = imagenet_utils.obtain_input_shape( input_shape, default_size=224, min_size=32, data_format=backend.image_data_format(), require_flatten=include_top, weights=weights) if input_tensor is None: img_input = layers.Input(shape=input_shape) else: if not backend.is_keras_tensor(input_tensor): img_input = layers.Input(tensor=input_tensor, shape=input_shape) else: img_input = input_tensor bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1 x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input) x = layers.Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x) x = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name='conv1/bn')( x) x = layers.Activation('relu', name='conv1/relu')(x) x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)))(x) x = layers.MaxPooling2D(3, strides=2, name='pool1')(x) x = dense_block(x, blocks[0], name='conv2') x = transition_block(x, 0.5, name='pool2') x = dense_block(x, blocks[1], name='conv3') x = transition_block(x, 0.5, name='pool3') x = dense_block(x, blocks[2], name='conv4') x = transition_block(x, 0.5, name='pool4') x = dense_block(x, blocks[3], name='conv5') x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name='bn')(x) x = layers.Activation('relu', name='relu')(x) if include_top: x = layers.GlobalAveragePooling2D(name='avg_pool')(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) elif pooling == 'max': x = layers.GlobalMaxPooling2D(name='max_pool')(x) # Ensure that the model takes into account # any potential predecessors of `input_tensor`. if input_tensor is not None: inputs = layer_utils.get_source_inputs(input_tensor) else: inputs = img_input # Create model. if blocks == [6, 12, 24, 16]: model = training.Model(inputs, x, name='densenet121') elif blocks == [6, 12, 32, 32]: model = training.Model(inputs, x, name='densenet169') elif blocks == [6, 12, 48, 32]: model = training.Model(inputs, x, name='densenet201') else: model = training.Model(inputs, x, name='densenet') # Load weights. if weights == 'imagenet': if include_top: if blocks == [6, 12, 24, 16]: weights_path = data_utils.get_file( 'densenet121_weights_tf_dim_ordering_tf_kernels.h5', DENSENET121_WEIGHT_PATH, cache_subdir='models', file_hash='9d60b8095a5708f2dcce2bca79d332c7') elif blocks == [6, 12, 32, 32]: weights_path = data_utils.get_file( 'densenet169_weights_tf_dim_ordering_tf_kernels.h5', DENSENET169_WEIGHT_PATH, cache_subdir='models', file_hash='d699b8f76981ab1b30698df4c175e90b') elif blocks == [6, 12, 48, 32]: weights_path = data_utils.get_file( 'densenet201_weights_tf_dim_ordering_tf_kernels.h5', DENSENET201_WEIGHT_PATH, cache_subdir='models', file_hash='1ceb130c1ea1b78c3bf6114dbdfd8807') else: if blocks == [6, 12, 24, 16]: weights_path = data_utils.get_file( 'densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5', DENSENET121_WEIGHT_PATH_NO_TOP, cache_subdir='models', file_hash='30ee3e1110167f948a6b9946edeeb738') elif blocks == [6, 12, 32, 32]: weights_path = data_utils.get_file( 'densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5', DENSENET169_WEIGHT_PATH_NO_TOP, cache_subdir='models', file_hash='b8c4d4c20dd625c148057b9ff1c1176b') elif blocks == [6, 12, 48, 32]: weights_path = data_utils.get_file( 'densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5', DENSENET201_WEIGHT_PATH_NO_TOP, cache_subdir='models', file_hash='c13680b51ded0fb44dff2d8f86ac8bb1') model.load_weights(weights_path) elif weights is not None: model.load_weights(weights) return model
def DenseNet121(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)
-
Instantiates the Densenet121 architecture.
Reference: - Densely Connected Convolutional Networks (CVPR 2017)
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
.Note: each Keras Application expects a specific kind of input preprocessing. For DenseNet, call
tf.keras.applications.densenet.preprocess_input
on your inputs before passing them to the model.Args
include_top
- whether to include the fully-connected layer at the top of the network.
weights
- one of
None
(random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. 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 (otherwise the input shape has to be(224, 224, 3)
(with'channels_last'
data format) or(3, 224, 224)
(with'channels_first'
data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g.(200, 200, 3)
would be one valid value. pooling
- Optional pooling mode for feature extraction
when
include_top
isFalse
. -None
means that the output of the model will be the 4D tensor output of the last convolutional block. -avg
means that global average pooling will be applied to the output of the last convolutional block, 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 noweights
argument is specified.
Returns
A Keras model instance.
Expand source code
@keras_export('keras.applications.densenet.DenseNet121', 'keras.applications.DenseNet121') def DenseNet121(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000): """Instantiates the Densenet121 architecture.""" return DenseNet([6, 12, 24, 16], include_top, weights, input_tensor, input_shape, pooling, classes)
def DenseNet169(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)
-
Instantiates the Densenet169 architecture.
Reference: - Densely Connected Convolutional Networks (CVPR 2017)
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
.Note: each Keras Application expects a specific kind of input preprocessing. For DenseNet, call
tf.keras.applications.densenet.preprocess_input
on your inputs before passing them to the model.Args
include_top
- whether to include the fully-connected layer at the top of the network.
weights
- one of
None
(random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. 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 (otherwise the input shape has to be(224, 224, 3)
(with'channels_last'
data format) or(3, 224, 224)
(with'channels_first'
data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g.(200, 200, 3)
would be one valid value. pooling
- Optional pooling mode for feature extraction
when
include_top
isFalse
. -None
means that the output of the model will be the 4D tensor output of the last convolutional block. -avg
means that global average pooling will be applied to the output of the last convolutional block, 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 noweights
argument is specified.
Returns
A Keras model instance.
Expand source code
@keras_export('keras.applications.densenet.DenseNet169', 'keras.applications.DenseNet169') def DenseNet169(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000): """Instantiates the Densenet169 architecture.""" return DenseNet([6, 12, 32, 32], include_top, weights, input_tensor, input_shape, pooling, classes)
def DenseNet201(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)
-
Instantiates the Densenet201 architecture.
Reference: - Densely Connected Convolutional Networks (CVPR 2017)
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
.Note: each Keras Application expects a specific kind of input preprocessing. For DenseNet, call
tf.keras.applications.densenet.preprocess_input
on your inputs before passing them to the model.Args
include_top
- whether to include the fully-connected layer at the top of the network.
weights
- one of
None
(random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. 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 (otherwise the input shape has to be(224, 224, 3)
(with'channels_last'
data format) or(3, 224, 224)
(with'channels_first'
data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g.(200, 200, 3)
would be one valid value. pooling
- Optional pooling mode for feature extraction
when
include_top
isFalse
. -None
means that the output of the model will be the 4D tensor output of the last convolutional block. -avg
means that global average pooling will be applied to the output of the last convolutional block, 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 noweights
argument is specified.
Returns
A Keras model instance.
Expand source code
@keras_export('keras.applications.densenet.DenseNet201', 'keras.applications.DenseNet201') def DenseNet201(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000): """Instantiates the Densenet201 architecture.""" return DenseNet([6, 12, 48, 32], include_top, weights, input_tensor, input_shape, pooling, classes)
def conv_block(x, growth_rate, name)
-
A building block for a dense block.
Args
x
- input tensor.
growth_rate
- float, growth rate at dense layers.
name
- string, block label.
Returns
Output tensor for the block.
Expand source code
def conv_block(x, growth_rate, name): """A building block for a dense block. Args: x: input tensor. growth_rate: float, growth rate at dense layers. name: string, block label. Returns: Output tensor for the block. """ bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1 x1 = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name=name + '_0_bn')( x) x1 = layers.Activation('relu', name=name + '_0_relu')(x1) x1 = layers.Conv2D( 4 * growth_rate, 1, use_bias=False, name=name + '_1_conv')( x1) x1 = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')( x1) x1 = layers.Activation('relu', name=name + '_1_relu')(x1) x1 = layers.Conv2D( growth_rate, 3, padding='same', use_bias=False, name=name + '_2_conv')( x1) x = layers.Concatenate(axis=bn_axis, name=name + '_concat')([x, x1]) return x
def decode_predictions(preds, top=5)
-
Decodes the prediction of an ImageNet model.
Args
preds
- Numpy array encoding a batch of predictions.
top
- Integer, how many top-guesses to return. Defaults to 5.
Returns
A list of lists of top class prediction tuples
(class_name, class_description, score)
. One list of tuples per sample in batch input.Raises
ValueError
- In case of invalid shape of the
pred
array (must be 2D).
Expand source code
@keras_export('keras.applications.densenet.decode_predictions') def decode_predictions(preds, top=5): return imagenet_utils.decode_predictions(preds, top=top)
def dense_block(x, blocks, name)
-
A dense block.
Args
x
- input tensor.
blocks
- integer, the number of building blocks.
name
- string, block label.
Returns
Output tensor for the block.
Expand source code
def dense_block(x, blocks, name): """A dense block. Args: x: input tensor. blocks: integer, the number of building blocks. name: string, block label. Returns: Output tensor for the block. """ for i in range(blocks): x = conv_block(x, 32, name=name + '_block' + str(i + 1)) return x
def preprocess_input(x, data_format=None)
-
Preprocesses a tensor or Numpy array encoding a batch of images.
Usage example with
applications.MobileNet
:i = tf.keras.layers.Input([None, None, 3], dtype = tf.uint8) x = tf.cast(i, tf.float32) x = tf.keras.applications.mobilenet.preprocess_input(x) core = tf.keras.applications.MobileNet() x = core(x) model = tf.keras.Model(inputs=[i], outputs=[x]) image = tf.image.decode_png(tf.io.read_file('file.png')) result = model(image)
Args
x
- A floating point
numpy.array
or atf.Tensor
, 3D or 4D with 3 color channels, with values in the range [0, 255]. The preprocessed data are written over the input data if the data types are compatible. To avoid this behaviour,numpy.copy(x)
can be used. data_format
- Optional data format of the image tensor/array. Defaults to
None, in which case the global setting
tf.keras.backend.image_data_format()
is used (unless you changed it, it defaults to "channels_last").
Returns
Preprocessed
numpy.array
or atf.Tensor
with typefloat32
.The input pixels values are scaled between 0 and 1 and each channel is normalized with respect to the ImageNet dataset.
Raises
ValueError
- In case of unknown
data_format
argument.
Expand source code
@keras_export('keras.applications.densenet.preprocess_input') def preprocess_input(x, data_format=None): return imagenet_utils.preprocess_input( x, data_format=data_format, mode='torch')
def transition_block(x, reduction, name)
-
A transition block.
Args
x
- input tensor.
reduction
- float, compression rate at transition layers.
name
- string, block label.
Returns
output tensor for the block.
Expand source code
def transition_block(x, reduction, name): """A transition block. Args: x: input tensor. reduction: float, compression rate at transition layers. name: string, block label. Returns: output tensor for the block. """ bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1 x = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name=name + '_bn')( x) x = layers.Activation('relu', name=name + '_relu')(x) x = layers.Conv2D( int(backend.int_shape(x)[bn_axis] * reduction), 1, use_bias=False, name=name + '_conv')( x) x = layers.AveragePooling2D(2, strides=2, name=name + '_pool')(x) return x