Module keras.applications.efficientnet

EfficientNet models for Keras.

Reference

Expand source code
# Copyright 2019 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
# pylint: disable=missing-docstring
"""EfficientNet models for Keras.

Reference:
  - [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](
      https://arxiv.org/abs/1905.11946) (ICML 2019)
"""

import tensorflow.compat.v2 as tf

import copy
import math

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/keras-applications/'

WEIGHTS_HASHES = {
    'b0': ('902e53a9f72be733fc0bcb005b3ebbac',
           '50bc09e76180e00e4465e1a485ddc09d'),
    'b1': ('1d254153d4ab51201f1646940f018540',
           '74c4e6b3e1f6a1eea24c589628592432'),
    'b2': ('b15cce36ff4dcbd00b6dd88e7857a6ad',
           '111f8e2ac8aa800a7a99e3239f7bfb39'),
    'b3': ('ffd1fdc53d0ce67064dc6a9c7960ede0',
           'af6d107764bb5b1abb91932881670226'),
    'b4': ('18c95ad55216b8f92d7e70b3a046e2fc',
           'ebc24e6d6c33eaebbd558eafbeedf1ba'),
    'b5': ('ace28f2a6363774853a83a0b21b9421a',
           '38879255a25d3c92d5e44e04ae6cec6f'),
    'b6': ('165f6e37dce68623721b423839de8be5',
           '9ecce42647a20130c1f39a5d4cb75743'),
    'b7': ('8c03f828fec3ef71311cd463b6759d99',
           'cbcfe4450ddf6f3ad90b1b398090fe4a'),
}

DEFAULT_BLOCKS_ARGS = [{
    'kernel_size': 3,
    'repeats': 1,
    'filters_in': 32,
    'filters_out': 16,
    'expand_ratio': 1,
    'id_skip': True,
    'strides': 1,
    'se_ratio': 0.25
}, {
    'kernel_size': 3,
    'repeats': 2,
    'filters_in': 16,
    'filters_out': 24,
    'expand_ratio': 6,
    'id_skip': True,
    'strides': 2,
    'se_ratio': 0.25
}, {
    'kernel_size': 5,
    'repeats': 2,
    'filters_in': 24,
    'filters_out': 40,
    'expand_ratio': 6,
    'id_skip': True,
    'strides': 2,
    'se_ratio': 0.25
}, {
    'kernel_size': 3,
    'repeats': 3,
    'filters_in': 40,
    'filters_out': 80,
    'expand_ratio': 6,
    'id_skip': True,
    'strides': 2,
    'se_ratio': 0.25
}, {
    'kernel_size': 5,
    'repeats': 3,
    'filters_in': 80,
    'filters_out': 112,
    'expand_ratio': 6,
    'id_skip': True,
    'strides': 1,
    'se_ratio': 0.25
}, {
    'kernel_size': 5,
    'repeats': 4,
    'filters_in': 112,
    'filters_out': 192,
    'expand_ratio': 6,
    'id_skip': True,
    'strides': 2,
    'se_ratio': 0.25
}, {
    'kernel_size': 3,
    'repeats': 1,
    'filters_in': 192,
    'filters_out': 320,
    'expand_ratio': 6,
    'id_skip': True,
    'strides': 1,
    'se_ratio': 0.25
}]

CONV_KERNEL_INITIALIZER = {
    'class_name': 'VarianceScaling',
    'config': {
        'scale': 2.0,
        'mode': 'fan_out',
        'distribution': 'truncated_normal'
    }
}

DENSE_KERNEL_INITIALIZER = {
    'class_name': 'VarianceScaling',
    'config': {
        'scale': 1. / 3.,
        'mode': 'fan_out',
        'distribution': 'uniform'
    }
}

layers = VersionAwareLayers()

BASE_DOCSTRING = """Instantiates the {name} architecture.

  Reference:
  - [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](
      https://arxiv.org/abs/1905.11946) (ICML 2019)

  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 EfficientNet, input preprocessing is included as part of the model
  (as a `Rescaling` layer), and thus
  `tf.keras.applications.efficientnet.preprocess_input` is actually a
  pass-through function. EfficientNet models expect their inputs to be float
  tensors of pixels with values in the [0-255] range.

  Args:
    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'.
        When loading pretrained weights, `classifier_activation` can only
        be `None` or `"softmax"`.

  Returns:
    A `keras.Model` instance.
"""


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.

  Args:
    width_coefficient: float, scaling coefficient for network width.
    depth_coefficient: float, scaling coefficient for network depth.
    default_size: integer, default input image size.
    dropout_rate: float, dropout rate before final classifier layer.
    drop_connect_rate: float, dropout rate at skip connections.
    depth_divisor: integer, a unit of network width.
    activation: activation function.
    blocks_args: list of dicts, parameters to construct block modules.
    model_name: string, model name.
    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.
        It should have exactly 3 inputs channels.
    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 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.
    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.

  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

  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=default_size,
      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

  def round_filters(filters, divisor=depth_divisor):
    """Round number of filters based on depth multiplier."""
    filters *= width_coefficient
    new_filters = max(divisor, int(filters + divisor / 2) // divisor * divisor)
    # Make sure that round down does not go down by more than 10%.
    if new_filters < 0.9 * filters:
      new_filters += divisor
    return int(new_filters)

  def round_repeats(repeats):
    """Round number of repeats based on depth multiplier."""
    return int(math.ceil(depth_coefficient * repeats))

  # Build stem
  x = img_input
  x = layers.Rescaling(1. / 255.)(x)
  x = layers.Normalization(axis=bn_axis)(x)

  x = layers.ZeroPadding2D(
      padding=imagenet_utils.correct_pad(x, 3),
      name='stem_conv_pad')(x)
  x = layers.Conv2D(
      round_filters(32),
      3,
      strides=2,
      padding='valid',
      use_bias=False,
      kernel_initializer=CONV_KERNEL_INITIALIZER,
      name='stem_conv')(x)
  x = layers.BatchNormalization(axis=bn_axis, name='stem_bn')(x)
  x = layers.Activation(activation, name='stem_activation')(x)

  # Build blocks
  blocks_args = copy.deepcopy(blocks_args)

  b = 0
  blocks = float(sum(round_repeats(args['repeats']) for args in blocks_args))
  for (i, args) in enumerate(blocks_args):
    assert args['repeats'] > 0
    # Update block input and output filters based on depth multiplier.
    args['filters_in'] = round_filters(args['filters_in'])
    args['filters_out'] = round_filters(args['filters_out'])

    for j in range(round_repeats(args.pop('repeats'))):
      # The first block needs to take care of stride and filter size increase.
      if j > 0:
        args['strides'] = 1
        args['filters_in'] = args['filters_out']
      x = block(
          x,
          activation,
          drop_connect_rate * b / blocks,
          name='block{}{}_'.format(i + 1, chr(j + 97)),
          **args)
      b += 1

  # Build top
  x = layers.Conv2D(
      round_filters(1280),
      1,
      padding='same',
      use_bias=False,
      kernel_initializer=CONV_KERNEL_INITIALIZER,
      name='top_conv')(x)
  x = layers.BatchNormalization(axis=bn_axis, name='top_bn')(x)
  x = layers.Activation(activation, name='top_activation')(x)
  if include_top:
    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=classifier_activation,
        kernel_initializer=DENSE_KERNEL_INITIALIZER,
        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.
  model = training.Model(inputs, x, name=model_name)

  # Load weights.
  if weights == 'imagenet':
    if include_top:
      file_suffix = '.h5'
      file_hash = WEIGHTS_HASHES[model_name[-2:]][0]
    else:
      file_suffix = '_notop.h5'
      file_hash = WEIGHTS_HASHES[model_name[-2:]][1]
    file_name = model_name + file_suffix
    weights_path = data_utils.get_file(
        file_name,
        BASE_WEIGHTS_PATH + file_name,
        cache_subdir='models',
        file_hash=file_hash)
    model.load_weights(weights_path)
  elif weights is not None:
    model.load_weights(weights)
  return model


def block(inputs,
          activation='swish',
          drop_rate=0.,
          name='',
          filters_in=32,
          filters_out=16,
          kernel_size=3,
          strides=1,
          expand_ratio=1,
          se_ratio=0.,
          id_skip=True):
  """An inverted residual block.

  Args:
      inputs: input tensor.
      activation: activation function.
      drop_rate: float between 0 and 1, fraction of the input units to drop.
      name: string, block label.
      filters_in: integer, the number of input filters.
      filters_out: integer, the number of output filters.
      kernel_size: integer, the dimension of the convolution window.
      strides: integer, the stride of the convolution.
      expand_ratio: integer, scaling coefficient for the input filters.
      se_ratio: float between 0 and 1, fraction to squeeze the input filters.
      id_skip: boolean.

  Returns:
      output tensor for the block.
  """
  bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1

  # Expansion phase
  filters = filters_in * expand_ratio
  if expand_ratio != 1:
    x = layers.Conv2D(
        filters,
        1,
        padding='same',
        use_bias=False,
        kernel_initializer=CONV_KERNEL_INITIALIZER,
        name=name + 'expand_conv')(
            inputs)
    x = layers.BatchNormalization(axis=bn_axis, name=name + 'expand_bn')(x)
    x = layers.Activation(activation, name=name + 'expand_activation')(x)
  else:
    x = inputs

  # Depthwise Convolution
  if strides == 2:
    x = layers.ZeroPadding2D(
        padding=imagenet_utils.correct_pad(x, kernel_size),
        name=name + 'dwconv_pad')(x)
    conv_pad = 'valid'
  else:
    conv_pad = 'same'
  x = layers.DepthwiseConv2D(
      kernel_size,
      strides=strides,
      padding=conv_pad,
      use_bias=False,
      depthwise_initializer=CONV_KERNEL_INITIALIZER,
      name=name + 'dwconv')(x)
  x = layers.BatchNormalization(axis=bn_axis, name=name + 'bn')(x)
  x = layers.Activation(activation, name=name + 'activation')(x)

  # Squeeze and Excitation phase
  if 0 < se_ratio <= 1:
    filters_se = max(1, int(filters_in * se_ratio))
    se = layers.GlobalAveragePooling2D(name=name + 'se_squeeze')(x)
    if bn_axis == 1:
      se_shape = (filters, 1, 1)
    else:
      se_shape = (1, 1, filters)
    se = layers.Reshape(se_shape, name=name + 'se_reshape')(se)
    se = layers.Conv2D(
        filters_se,
        1,
        padding='same',
        activation=activation,
        kernel_initializer=CONV_KERNEL_INITIALIZER,
        name=name + 'se_reduce')(
            se)
    se = layers.Conv2D(
        filters,
        1,
        padding='same',
        activation='sigmoid',
        kernel_initializer=CONV_KERNEL_INITIALIZER,
        name=name + 'se_expand')(se)
    x = layers.multiply([x, se], name=name + 'se_excite')

  # Output phase
  x = layers.Conv2D(
      filters_out,
      1,
      padding='same',
      use_bias=False,
      kernel_initializer=CONV_KERNEL_INITIALIZER,
      name=name + 'project_conv')(x)
  x = layers.BatchNormalization(axis=bn_axis, name=name + 'project_bn')(x)
  if id_skip and strides == 1 and filters_in == filters_out:
    if drop_rate > 0:
      x = layers.Dropout(
          drop_rate, noise_shape=(None, 1, 1, 1), name=name + 'drop')(x)
    x = layers.add([x, inputs], name=name + 'add')
  return x


@keras_export('keras.applications.efficientnet.EfficientNetB0',
              'keras.applications.EfficientNetB0')
def EfficientNetB0(include_top=True,
                   weights='imagenet',
                   input_tensor=None,
                   input_shape=None,
                   pooling=None,
                   classes=1000,
                   classifier_activation='softmax',
                   **kwargs):
  return EfficientNet(
      1.0,
      1.0,
      224,
      0.2,
      model_name='efficientnetb0',
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      input_shape=input_shape,
      pooling=pooling,
      classes=classes,
      classifier_activation=classifier_activation,
      **kwargs)


@keras_export('keras.applications.efficientnet.EfficientNetB1',
              'keras.applications.EfficientNetB1')
def EfficientNetB1(include_top=True,
                   weights='imagenet',
                   input_tensor=None,
                   input_shape=None,
                   pooling=None,
                   classes=1000,
                   classifier_activation='softmax',
                   **kwargs):
  return EfficientNet(
      1.0,
      1.1,
      240,
      0.2,
      model_name='efficientnetb1',
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      input_shape=input_shape,
      pooling=pooling,
      classes=classes,
      classifier_activation=classifier_activation,
      **kwargs)


@keras_export('keras.applications.efficientnet.EfficientNetB2',
              'keras.applications.EfficientNetB2')
def EfficientNetB2(include_top=True,
                   weights='imagenet',
                   input_tensor=None,
                   input_shape=None,
                   pooling=None,
                   classes=1000,
                   classifier_activation='softmax',
                   **kwargs):
  return EfficientNet(
      1.1,
      1.2,
      260,
      0.3,
      model_name='efficientnetb2',
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      input_shape=input_shape,
      pooling=pooling,
      classes=classes,
      classifier_activation=classifier_activation,
      **kwargs)


@keras_export('keras.applications.efficientnet.EfficientNetB3',
              'keras.applications.EfficientNetB3')
def EfficientNetB3(include_top=True,
                   weights='imagenet',
                   input_tensor=None,
                   input_shape=None,
                   pooling=None,
                   classes=1000,
                   classifier_activation='softmax',
                   **kwargs):
  return EfficientNet(
      1.2,
      1.4,
      300,
      0.3,
      model_name='efficientnetb3',
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      input_shape=input_shape,
      pooling=pooling,
      classes=classes,
      classifier_activation=classifier_activation,
      **kwargs)


@keras_export('keras.applications.efficientnet.EfficientNetB4',
              'keras.applications.EfficientNetB4')
def EfficientNetB4(include_top=True,
                   weights='imagenet',
                   input_tensor=None,
                   input_shape=None,
                   pooling=None,
                   classes=1000,
                   classifier_activation='softmax',
                   **kwargs):
  return EfficientNet(
      1.4,
      1.8,
      380,
      0.4,
      model_name='efficientnetb4',
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      input_shape=input_shape,
      pooling=pooling,
      classes=classes,
      classifier_activation=classifier_activation,
      **kwargs)


@keras_export('keras.applications.efficientnet.EfficientNetB5',
              'keras.applications.EfficientNetB5')
def EfficientNetB5(include_top=True,
                   weights='imagenet',
                   input_tensor=None,
                   input_shape=None,
                   pooling=None,
                   classes=1000,
                   classifier_activation='softmax',
                   **kwargs):
  return EfficientNet(
      1.6,
      2.2,
      456,
      0.4,
      model_name='efficientnetb5',
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      input_shape=input_shape,
      pooling=pooling,
      classes=classes,
      classifier_activation=classifier_activation,
      **kwargs)


@keras_export('keras.applications.efficientnet.EfficientNetB6',
              'keras.applications.EfficientNetB6')
def EfficientNetB6(include_top=True,
                   weights='imagenet',
                   input_tensor=None,
                   input_shape=None,
                   pooling=None,
                   classes=1000,
                   classifier_activation='softmax',
                   **kwargs):
  return EfficientNet(
      1.8,
      2.6,
      528,
      0.5,
      model_name='efficientnetb6',
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      input_shape=input_shape,
      pooling=pooling,
      classes=classes,
      classifier_activation=classifier_activation,
      **kwargs)


@keras_export('keras.applications.efficientnet.EfficientNetB7',
              'keras.applications.EfficientNetB7')
def EfficientNetB7(include_top=True,
                   weights='imagenet',
                   input_tensor=None,
                   input_shape=None,
                   pooling=None,
                   classes=1000,
                   classifier_activation='softmax',
                   **kwargs):
  return EfficientNet(
      2.0,
      3.1,
      600,
      0.5,
      model_name='efficientnetb7',
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      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
  """A placeholder method for backward compatibility.

  The preprocessing logic has been included in the efficientnet model
  implementation. Users are no longer required to call this method to normalize
  the input data. This method does nothing and only kept as a placeholder to
  align the API surface between old and new version of model.

  Args:
    x: A floating point `numpy.array` or a `tf.Tensor`.
    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").{mode}

  Returns:
    Unchanged `numpy.array` or `tf.Tensor`.
  """
  return x


@keras_export('keras.applications.efficientnet.decode_predictions')
def decode_predictions(preds, top=5):
  return imagenet_utils.decode_predictions(preds, top=top)


decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__

Functions

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.

Args

width_coefficient
float, scaling coefficient for network width.
depth_coefficient
float, scaling coefficient for network depth.
default_size
integer, default input image size.
dropout_rate
float, dropout rate before final classifier layer.
drop_connect_rate
float, dropout rate at skip connections.
depth_divisor
integer, a unit of network width.
activation
activation function.
blocks_args
list of dicts, parameters to construct block modules.
model_name
string, model name.
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. It should have exactly 3 inputs channels.
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 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.
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.

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.
Expand source code
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.

  Args:
    width_coefficient: float, scaling coefficient for network width.
    depth_coefficient: float, scaling coefficient for network depth.
    default_size: integer, default input image size.
    dropout_rate: float, dropout rate before final classifier layer.
    drop_connect_rate: float, dropout rate at skip connections.
    depth_divisor: integer, a unit of network width.
    activation: activation function.
    blocks_args: list of dicts, parameters to construct block modules.
    model_name: string, model name.
    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.
        It should have exactly 3 inputs channels.
    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 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.
    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.

  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

  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=default_size,
      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

  def round_filters(filters, divisor=depth_divisor):
    """Round number of filters based on depth multiplier."""
    filters *= width_coefficient
    new_filters = max(divisor, int(filters + divisor / 2) // divisor * divisor)
    # Make sure that round down does not go down by more than 10%.
    if new_filters < 0.9 * filters:
      new_filters += divisor
    return int(new_filters)

  def round_repeats(repeats):
    """Round number of repeats based on depth multiplier."""
    return int(math.ceil(depth_coefficient * repeats))

  # Build stem
  x = img_input
  x = layers.Rescaling(1. / 255.)(x)
  x = layers.Normalization(axis=bn_axis)(x)

  x = layers.ZeroPadding2D(
      padding=imagenet_utils.correct_pad(x, 3),
      name='stem_conv_pad')(x)
  x = layers.Conv2D(
      round_filters(32),
      3,
      strides=2,
      padding='valid',
      use_bias=False,
      kernel_initializer=CONV_KERNEL_INITIALIZER,
      name='stem_conv')(x)
  x = layers.BatchNormalization(axis=bn_axis, name='stem_bn')(x)
  x = layers.Activation(activation, name='stem_activation')(x)

  # Build blocks
  blocks_args = copy.deepcopy(blocks_args)

  b = 0
  blocks = float(sum(round_repeats(args['repeats']) for args in blocks_args))
  for (i, args) in enumerate(blocks_args):
    assert args['repeats'] > 0
    # Update block input and output filters based on depth multiplier.
    args['filters_in'] = round_filters(args['filters_in'])
    args['filters_out'] = round_filters(args['filters_out'])

    for j in range(round_repeats(args.pop('repeats'))):
      # The first block needs to take care of stride and filter size increase.
      if j > 0:
        args['strides'] = 1
        args['filters_in'] = args['filters_out']
      x = block(
          x,
          activation,
          drop_connect_rate * b / blocks,
          name='block{}{}_'.format(i + 1, chr(j + 97)),
          **args)
      b += 1

  # Build top
  x = layers.Conv2D(
      round_filters(1280),
      1,
      padding='same',
      use_bias=False,
      kernel_initializer=CONV_KERNEL_INITIALIZER,
      name='top_conv')(x)
  x = layers.BatchNormalization(axis=bn_axis, name='top_bn')(x)
  x = layers.Activation(activation, name='top_activation')(x)
  if include_top:
    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=classifier_activation,
        kernel_initializer=DENSE_KERNEL_INITIALIZER,
        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.
  model = training.Model(inputs, x, name=model_name)

  # Load weights.
  if weights == 'imagenet':
    if include_top:
      file_suffix = '.h5'
      file_hash = WEIGHTS_HASHES[model_name[-2:]][0]
    else:
      file_suffix = '_notop.h5'
      file_hash = WEIGHTS_HASHES[model_name[-2:]][1]
    file_name = model_name + file_suffix
    weights_path = data_utils.get_file(
        file_name,
        BASE_WEIGHTS_PATH + file_name,
        cache_subdir='models',
        file_hash=file_hash)
    model.load_weights(weights_path)
  elif weights is not None:
    model.load_weights(weights)
  return model
def EfficientNetB0(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation='softmax', **kwargs)

Instantiates the EfficientNetB0 architecture.

Reference: - EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (ICML 2019)

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 EfficientNet, input preprocessing is included as part of the model (as a Rescaling layer), and thus tf.keras.applications.efficientnet.preprocess_input is actually a pass-through function. EfficientNet models expect their inputs to be float tensors of pixels with values in the [0-255] range.

Args

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'. When loading pretrained weights, classifier_activation can only be None or "softmax".

Returns

A keras.Model instance.

Expand source code
@keras_export('keras.applications.efficientnet.EfficientNetB0',
              'keras.applications.EfficientNetB0')
def EfficientNetB0(include_top=True,
                   weights='imagenet',
                   input_tensor=None,
                   input_shape=None,
                   pooling=None,
                   classes=1000,
                   classifier_activation='softmax',
                   **kwargs):
  return EfficientNet(
      1.0,
      1.0,
      224,
      0.2,
      model_name='efficientnetb0',
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      input_shape=input_shape,
      pooling=pooling,
      classes=classes,
      classifier_activation=classifier_activation,
      **kwargs)
def EfficientNetB1(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation='softmax', **kwargs)

Instantiates the EfficientNetB1 architecture.

Reference: - EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (ICML 2019)

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 EfficientNet, input preprocessing is included as part of the model (as a Rescaling layer), and thus tf.keras.applications.efficientnet.preprocess_input is actually a pass-through function. EfficientNet models expect their inputs to be float tensors of pixels with values in the [0-255] range.

Args

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'. When loading pretrained weights, classifier_activation can only be None or "softmax".

Returns

A keras.Model instance.

Expand source code
@keras_export('keras.applications.efficientnet.EfficientNetB1',
              'keras.applications.EfficientNetB1')
def EfficientNetB1(include_top=True,
                   weights='imagenet',
                   input_tensor=None,
                   input_shape=None,
                   pooling=None,
                   classes=1000,
                   classifier_activation='softmax',
                   **kwargs):
  return EfficientNet(
      1.0,
      1.1,
      240,
      0.2,
      model_name='efficientnetb1',
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      input_shape=input_shape,
      pooling=pooling,
      classes=classes,
      classifier_activation=classifier_activation,
      **kwargs)
def EfficientNetB2(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation='softmax', **kwargs)

Instantiates the EfficientNetB2 architecture.

Reference: - EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (ICML 2019)

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 EfficientNet, input preprocessing is included as part of the model (as a Rescaling layer), and thus tf.keras.applications.efficientnet.preprocess_input is actually a pass-through function. EfficientNet models expect their inputs to be float tensors of pixels with values in the [0-255] range.

Args

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'. When loading pretrained weights, classifier_activation can only be None or "softmax".

Returns

A keras.Model instance.

Expand source code
@keras_export('keras.applications.efficientnet.EfficientNetB2',
              'keras.applications.EfficientNetB2')
def EfficientNetB2(include_top=True,
                   weights='imagenet',
                   input_tensor=None,
                   input_shape=None,
                   pooling=None,
                   classes=1000,
                   classifier_activation='softmax',
                   **kwargs):
  return EfficientNet(
      1.1,
      1.2,
      260,
      0.3,
      model_name='efficientnetb2',
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      input_shape=input_shape,
      pooling=pooling,
      classes=classes,
      classifier_activation=classifier_activation,
      **kwargs)
def EfficientNetB3(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation='softmax', **kwargs)

Instantiates the EfficientNetB3 architecture.

Reference: - EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (ICML 2019)

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 EfficientNet, input preprocessing is included as part of the model (as a Rescaling layer), and thus tf.keras.applications.efficientnet.preprocess_input is actually a pass-through function. EfficientNet models expect their inputs to be float tensors of pixels with values in the [0-255] range.

Args

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'. When loading pretrained weights, classifier_activation can only be None or "softmax".

Returns

A keras.Model instance.

Expand source code
@keras_export('keras.applications.efficientnet.EfficientNetB3',
              'keras.applications.EfficientNetB3')
def EfficientNetB3(include_top=True,
                   weights='imagenet',
                   input_tensor=None,
                   input_shape=None,
                   pooling=None,
                   classes=1000,
                   classifier_activation='softmax',
                   **kwargs):
  return EfficientNet(
      1.2,
      1.4,
      300,
      0.3,
      model_name='efficientnetb3',
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      input_shape=input_shape,
      pooling=pooling,
      classes=classes,
      classifier_activation=classifier_activation,
      **kwargs)
def EfficientNetB4(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation='softmax', **kwargs)

Instantiates the EfficientNetB4 architecture.

Reference: - EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (ICML 2019)

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 EfficientNet, input preprocessing is included as part of the model (as a Rescaling layer), and thus tf.keras.applications.efficientnet.preprocess_input is actually a pass-through function. EfficientNet models expect their inputs to be float tensors of pixels with values in the [0-255] range.

Args

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'. When loading pretrained weights, classifier_activation can only be None or "softmax".

Returns

A keras.Model instance.

Expand source code
@keras_export('keras.applications.efficientnet.EfficientNetB4',
              'keras.applications.EfficientNetB4')
def EfficientNetB4(include_top=True,
                   weights='imagenet',
                   input_tensor=None,
                   input_shape=None,
                   pooling=None,
                   classes=1000,
                   classifier_activation='softmax',
                   **kwargs):
  return EfficientNet(
      1.4,
      1.8,
      380,
      0.4,
      model_name='efficientnetb4',
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      input_shape=input_shape,
      pooling=pooling,
      classes=classes,
      classifier_activation=classifier_activation,
      **kwargs)
def EfficientNetB5(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation='softmax', **kwargs)

Instantiates the EfficientNetB5 architecture.

Reference: - EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (ICML 2019)

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 EfficientNet, input preprocessing is included as part of the model (as a Rescaling layer), and thus tf.keras.applications.efficientnet.preprocess_input is actually a pass-through function. EfficientNet models expect their inputs to be float tensors of pixels with values in the [0-255] range.

Args

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'. When loading pretrained weights, classifier_activation can only be None or "softmax".

Returns

A keras.Model instance.

Expand source code
@keras_export('keras.applications.efficientnet.EfficientNetB5',
              'keras.applications.EfficientNetB5')
def EfficientNetB5(include_top=True,
                   weights='imagenet',
                   input_tensor=None,
                   input_shape=None,
                   pooling=None,
                   classes=1000,
                   classifier_activation='softmax',
                   **kwargs):
  return EfficientNet(
      1.6,
      2.2,
      456,
      0.4,
      model_name='efficientnetb5',
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      input_shape=input_shape,
      pooling=pooling,
      classes=classes,
      classifier_activation=classifier_activation,
      **kwargs)
def EfficientNetB6(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation='softmax', **kwargs)

Instantiates the EfficientNetB6 architecture.

Reference: - EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (ICML 2019)

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 EfficientNet, input preprocessing is included as part of the model (as a Rescaling layer), and thus tf.keras.applications.efficientnet.preprocess_input is actually a pass-through function. EfficientNet models expect their inputs to be float tensors of pixels with values in the [0-255] range.

Args

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'. When loading pretrained weights, classifier_activation can only be None or "softmax".

Returns

A keras.Model instance.

Expand source code
@keras_export('keras.applications.efficientnet.EfficientNetB6',
              'keras.applications.EfficientNetB6')
def EfficientNetB6(include_top=True,
                   weights='imagenet',
                   input_tensor=None,
                   input_shape=None,
                   pooling=None,
                   classes=1000,
                   classifier_activation='softmax',
                   **kwargs):
  return EfficientNet(
      1.8,
      2.6,
      528,
      0.5,
      model_name='efficientnetb6',
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      input_shape=input_shape,
      pooling=pooling,
      classes=classes,
      classifier_activation=classifier_activation,
      **kwargs)
def EfficientNetB7(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation='softmax', **kwargs)

Instantiates the EfficientNetB7 architecture.

Reference: - EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (ICML 2019)

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 EfficientNet, input preprocessing is included as part of the model (as a Rescaling layer), and thus tf.keras.applications.efficientnet.preprocess_input is actually a pass-through function. EfficientNet models expect their inputs to be float tensors of pixels with values in the [0-255] range.

Args

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'. When loading pretrained weights, classifier_activation can only be None or "softmax".

Returns

A keras.Model instance.

Expand source code
@keras_export('keras.applications.efficientnet.EfficientNetB7',
              'keras.applications.EfficientNetB7')
def EfficientNetB7(include_top=True,
                   weights='imagenet',
                   input_tensor=None,
                   input_shape=None,
                   pooling=None,
                   classes=1000,
                   classifier_activation='softmax',
                   **kwargs):
  return EfficientNet(
      2.0,
      3.1,
      600,
      0.5,
      model_name='efficientnetb7',
      include_top=include_top,
      weights=weights,
      input_tensor=input_tensor,
      input_shape=input_shape,
      pooling=pooling,
      classes=classes,
      classifier_activation=classifier_activation,
      **kwargs)
def block(inputs, activation='swish', drop_rate=0.0, name='', filters_in=32, filters_out=16, kernel_size=3, strides=1, expand_ratio=1, se_ratio=0.0, id_skip=True)

An inverted residual block.

Args

inputs
input tensor.
activation
activation function.
drop_rate
float between 0 and 1, fraction of the input units to drop.
name
string, block label.
filters_in
integer, the number of input filters.
filters_out
integer, the number of output filters.
kernel_size
integer, the dimension of the convolution window.
strides
integer, the stride of the convolution.
expand_ratio
integer, scaling coefficient for the input filters.
se_ratio
float between 0 and 1, fraction to squeeze the input filters.
id_skip
boolean.

Returns

output tensor for the block.

Expand source code
def block(inputs,
          activation='swish',
          drop_rate=0.,
          name='',
          filters_in=32,
          filters_out=16,
          kernel_size=3,
          strides=1,
          expand_ratio=1,
          se_ratio=0.,
          id_skip=True):
  """An inverted residual block.

  Args:
      inputs: input tensor.
      activation: activation function.
      drop_rate: float between 0 and 1, fraction of the input units to drop.
      name: string, block label.
      filters_in: integer, the number of input filters.
      filters_out: integer, the number of output filters.
      kernel_size: integer, the dimension of the convolution window.
      strides: integer, the stride of the convolution.
      expand_ratio: integer, scaling coefficient for the input filters.
      se_ratio: float between 0 and 1, fraction to squeeze the input filters.
      id_skip: boolean.

  Returns:
      output tensor for the block.
  """
  bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1

  # Expansion phase
  filters = filters_in * expand_ratio
  if expand_ratio != 1:
    x = layers.Conv2D(
        filters,
        1,
        padding='same',
        use_bias=False,
        kernel_initializer=CONV_KERNEL_INITIALIZER,
        name=name + 'expand_conv')(
            inputs)
    x = layers.BatchNormalization(axis=bn_axis, name=name + 'expand_bn')(x)
    x = layers.Activation(activation, name=name + 'expand_activation')(x)
  else:
    x = inputs

  # Depthwise Convolution
  if strides == 2:
    x = layers.ZeroPadding2D(
        padding=imagenet_utils.correct_pad(x, kernel_size),
        name=name + 'dwconv_pad')(x)
    conv_pad = 'valid'
  else:
    conv_pad = 'same'
  x = layers.DepthwiseConv2D(
      kernel_size,
      strides=strides,
      padding=conv_pad,
      use_bias=False,
      depthwise_initializer=CONV_KERNEL_INITIALIZER,
      name=name + 'dwconv')(x)
  x = layers.BatchNormalization(axis=bn_axis, name=name + 'bn')(x)
  x = layers.Activation(activation, name=name + 'activation')(x)

  # Squeeze and Excitation phase
  if 0 < se_ratio <= 1:
    filters_se = max(1, int(filters_in * se_ratio))
    se = layers.GlobalAveragePooling2D(name=name + 'se_squeeze')(x)
    if bn_axis == 1:
      se_shape = (filters, 1, 1)
    else:
      se_shape = (1, 1, filters)
    se = layers.Reshape(se_shape, name=name + 'se_reshape')(se)
    se = layers.Conv2D(
        filters_se,
        1,
        padding='same',
        activation=activation,
        kernel_initializer=CONV_KERNEL_INITIALIZER,
        name=name + 'se_reduce')(
            se)
    se = layers.Conv2D(
        filters,
        1,
        padding='same',
        activation='sigmoid',
        kernel_initializer=CONV_KERNEL_INITIALIZER,
        name=name + 'se_expand')(se)
    x = layers.multiply([x, se], name=name + 'se_excite')

  # Output phase
  x = layers.Conv2D(
      filters_out,
      1,
      padding='same',
      use_bias=False,
      kernel_initializer=CONV_KERNEL_INITIALIZER,
      name=name + 'project_conv')(x)
  x = layers.BatchNormalization(axis=bn_axis, name=name + 'project_bn')(x)
  if id_skip and strides == 1 and filters_in == filters_out:
    if drop_rate > 0:
      x = layers.Dropout(
          drop_rate, noise_shape=(None, 1, 1, 1), name=name + 'drop')(x)
    x = layers.add([x, inputs], name=name + 'add')
  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.efficientnet.decode_predictions')
def decode_predictions(preds, top=5):
  return imagenet_utils.decode_predictions(preds, top=top)
def preprocess_input(x, data_format=None)

A placeholder method for backward compatibility.

The preprocessing logic has been included in the efficientnet model implementation. Users are no longer required to call this method to normalize the input data. This method does nothing and only kept as a placeholder to align the API surface between old and new version of model.

Args

x
A floating point numpy.array or a tf.Tensor.
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").{mode}

Returns

Unchanged numpy.array or tf.Tensor.

Expand source code
@keras_export('keras.applications.efficientnet.preprocess_input')
def preprocess_input(x, data_format=None):  # pylint: disable=unused-argument
  """A placeholder method for backward compatibility.

  The preprocessing logic has been included in the efficientnet model
  implementation. Users are no longer required to call this method to normalize
  the input data. This method does nothing and only kept as a placeholder to
  align the API surface between old and new version of model.

  Args:
    x: A floating point `numpy.array` or a `tf.Tensor`.
    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").{mode}

  Returns:
    Unchanged `numpy.array` or `tf.Tensor`.
  """
  return x