Module keras.api.keras.applications.inception_resnet_v2
Public API for tf.keras.applications.inception_resnet_v2 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.inception_resnet_v2 namespace.
"""
from __future__ import print_function as _print_function
import sys as _sys
from keras.applications.inception_resnet_v2 import InceptionResNetV2
from keras.applications.inception_resnet_v2 import decode_predictions
from keras.applications.inception_resnet_v2 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.inception_resnet_v2", public_apis=None, deprecation=True,
has_lite=False)
Functions
def InceptionResNetV2(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation='softmax', **kwargs)
-
Instantiates the Inception-ResNet v2 architecture.
Reference: - Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning (AAAI 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 InceptionResNetV2, call
tf.keras.applications.inception_resnet_v2.preprocess_input
on your inputs before passing them to the model.inception_resnet_v2.preprocess_input
will scale input pixels between -1 and 1.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
isFalse
(otherwise the input shape has to be(299, 299, 3)
(with'channels_last'
data format) or(3, 299, 299)
(with'channels_first'
data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 75. E.g.(150, 150, 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
isTrue
, 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"
. **kwargs
- For backwards compatibility only.
Returns
A
keras.Model
instance.Expand source code
@keras_export('keras.applications.inception_resnet_v2.InceptionResNetV2', 'keras.applications.InceptionResNetV2') def InceptionResNetV2(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation='softmax', **kwargs): """Instantiates the Inception-ResNet v2 architecture. Reference: - [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://arxiv.org/abs/1602.07261) (AAAI 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 InceptionResNetV2, call `tf.keras.applications.inception_resnet_v2.preprocess_input` on your inputs before passing them to the model. `inception_resnet_v2.preprocess_input` will scale input pixels between -1 and 1. 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 `(299, 299, 3)` (with `'channels_last'` data format) or `(3, 299, 299)` (with `'channels_first'` data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 75. E.g. `(150, 150, 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"`. **kwargs: For backwards compatibility only. Returns: A `keras.Model` instance. """ global layers if 'layers' in kwargs: layers = kwargs.pop('layers') else: layers = VersionAwareLayers() if kwargs: raise ValueError('Unknown argument(s): %s' % (kwargs,)) 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=299, min_size=75, 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 # Stem block: 35 x 35 x 192 x = conv2d_bn(img_input, 32, 3, strides=2, padding='valid') x = conv2d_bn(x, 32, 3, padding='valid') x = conv2d_bn(x, 64, 3) x = layers.MaxPooling2D(3, strides=2)(x) x = conv2d_bn(x, 80, 1, padding='valid') x = conv2d_bn(x, 192, 3, padding='valid') x = layers.MaxPooling2D(3, strides=2)(x) # Mixed 5b (Inception-A block): 35 x 35 x 320 branch_0 = conv2d_bn(x, 96, 1) branch_1 = conv2d_bn(x, 48, 1) branch_1 = conv2d_bn(branch_1, 64, 5) branch_2 = conv2d_bn(x, 64, 1) branch_2 = conv2d_bn(branch_2, 96, 3) branch_2 = conv2d_bn(branch_2, 96, 3) branch_pool = layers.AveragePooling2D(3, strides=1, padding='same')(x) branch_pool = conv2d_bn(branch_pool, 64, 1) branches = [branch_0, branch_1, branch_2, branch_pool] channel_axis = 1 if backend.image_data_format() == 'channels_first' else 3 x = layers.Concatenate(axis=channel_axis, name='mixed_5b')(branches) # 10x block35 (Inception-ResNet-A block): 35 x 35 x 320 for block_idx in range(1, 11): x = inception_resnet_block( x, scale=0.17, block_type='block35', block_idx=block_idx) # Mixed 6a (Reduction-A block): 17 x 17 x 1088 branch_0 = conv2d_bn(x, 384, 3, strides=2, padding='valid') branch_1 = conv2d_bn(x, 256, 1) branch_1 = conv2d_bn(branch_1, 256, 3) branch_1 = conv2d_bn(branch_1, 384, 3, strides=2, padding='valid') branch_pool = layers.MaxPooling2D(3, strides=2, padding='valid')(x) branches = [branch_0, branch_1, branch_pool] x = layers.Concatenate(axis=channel_axis, name='mixed_6a')(branches) # 20x block17 (Inception-ResNet-B block): 17 x 17 x 1088 for block_idx in range(1, 21): x = inception_resnet_block( x, scale=0.1, block_type='block17', block_idx=block_idx) # Mixed 7a (Reduction-B block): 8 x 8 x 2080 branch_0 = conv2d_bn(x, 256, 1) branch_0 = conv2d_bn(branch_0, 384, 3, strides=2, padding='valid') branch_1 = conv2d_bn(x, 256, 1) branch_1 = conv2d_bn(branch_1, 288, 3, strides=2, padding='valid') branch_2 = conv2d_bn(x, 256, 1) branch_2 = conv2d_bn(branch_2, 288, 3) branch_2 = conv2d_bn(branch_2, 320, 3, strides=2, padding='valid') branch_pool = layers.MaxPooling2D(3, strides=2, padding='valid')(x) branches = [branch_0, branch_1, branch_2, branch_pool] x = layers.Concatenate(axis=channel_axis, name='mixed_7a')(branches) # 10x block8 (Inception-ResNet-C block): 8 x 8 x 2080 for block_idx in range(1, 10): x = inception_resnet_block( x, scale=0.2, block_type='block8', block_idx=block_idx) x = inception_resnet_block( x, scale=1., activation=None, block_type='block8', block_idx=10) # Final convolution block: 8 x 8 x 1536 x = conv2d_bn(x, 1536, 1, name='conv_7b') if include_top: # Classification block 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()(x) elif pooling == 'max': x = layers.GlobalMaxPooling2D()(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. model = training.Model(inputs, x, name='inception_resnet_v2') # Load weights. if weights == 'imagenet': if include_top: fname = 'inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5' weights_path = data_utils.get_file( fname, BASE_WEIGHT_URL + fname, cache_subdir='models', file_hash='e693bd0210a403b3192acc6073ad2e96') else: fname = ('inception_resnet_v2_weights_' 'tf_dim_ordering_tf_kernels_notop.h5') weights_path = data_utils.get_file( fname, BASE_WEIGHT_URL + fname, cache_subdir='models', file_hash='d19885ff4a710c122648d3b5c3b684e4') model.load_weights(weights_path) elif weights is not None: model.load_weights(weights) return model
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.inception_resnet_v2.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 inputs pixel values are scaled between -1 and 1, sample-wise.
Raises
ValueError
- In case of unknown
data_format
argument.
Expand source code
@keras_export('keras.applications.inception_resnet_v2.preprocess_input') def preprocess_input(x, data_format=None): return imagenet_utils.preprocess_input(x, data_format=data_format, mode='tf')