Module keras.api.keras.applications.densenet
Public API for tf.keras.applications.densenet namespace.
Expand source code
# This file is MACHINE GENERATED! Do not edit.
# Generated by: tensorflow/python/tools/api/generator/create_python_api.py script.
"""Public API for tf.keras.applications.densenet namespace.
"""
from __future__ import print_function as _print_function
import sys as _sys
from keras.applications.densenet import DenseNet121
from keras.applications.densenet import DenseNet169
from keras.applications.densenet import DenseNet201
from keras.applications.densenet import decode_predictions
from keras.applications.densenet import preprocess_input
del _print_function
from tensorflow.python.util import module_wrapper as _module_wrapper
if not isinstance(_sys.modules[__name__], _module_wrapper.TFModuleWrapper):
_sys.modules[__name__] = _module_wrapper.TFModuleWrapper(
_sys.modules[__name__], "keras.applications.densenet", public_apis=None, deprecation=True,
has_lite=False)
Functions
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 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 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')