EVOLUTION-MANAGER

Edit File: _2d.py

# -*- coding: utf-8 -*-

"""
keras_resnet.classifiers
~~~~~~~~~~~~~~~~~~~~~~~~

This module implements popular residual two-dimensional classifiers.
"""

import keras.backend
import keras.layers
import keras.models
import keras.regularizers

import keras_resnet.models

class ResNet18(keras.models.Model):
    """
    A :class:`ResNet18 <ResNet18>` object.

:param inputs: input tensor (e.g. an instance of `keras.layers.Input`)

Usage:

>>> import keras_resnet.classifiers

>>> shape, classes = (224, 224, 3), 1000

>>> x = keras.layers.Input(shape)

>>> model = keras_resnet.classifiers.ResNet18(x)

>>> model.compile("adam", "categorical_crossentropy", ["accuracy"])
    """
    def __init__(self, inputs, classes):
        outputs = keras_resnet.models.ResNet18(inputs)

outputs = keras.layers.Flatten()(outputs.output)

outputs = keras.layers.Dense(classes, activation="softmax")(outputs)

super(ResNet18, self).__init__(inputs, outputs)

class ResNet34(keras.models.Model):
    """
    A :class:`ResNet34 <ResNet34>` object.

:param inputs: input tensor (e.g. an instance of `keras.layers.Input`)

Usage:

>>> import keras_resnet.classifiers

>>> shape, classes = (224, 224, 3), 1000

>>> x = keras.layers.Input(shape)

>>> model = keras_resnet.classifiers.ResNet34(x)

>>> model.compile("adam", "categorical_crossentropy", ["accuracy"])
    """
    def __init__(self, inputs, classes):
        outputs = keras_resnet.models.ResNet34(inputs)

outputs = keras.layers.Flatten()(outputs.output)

outputs = keras.layers.Dense(classes, activation="softmax")(outputs)

super(ResNet34, self).__init__(inputs, outputs)

class ResNet50(keras.models.Model):
    """
    A :class:`ResNet50 <ResNet50>` object.

:param inputs: input tensor (e.g. an instance of `keras.layers.Input`)

Usage:

>>> import keras_resnet.classifiers

>>> shape, classes = (224, 224, 3), 1000

>>> x = keras.layers.Input(shape)

>>> model = keras_resnet.classifiers.ResNet50(x)

>>> model.compile("adam", "categorical_crossentropy", ["accuracy"])
    """
    def __init__(self, inputs, classes):
        outputs = keras_resnet.models.ResNet50(inputs)

outputs = keras.layers.Flatten()(outputs.output)

outputs = keras.layers.Dense(classes, activation="softmax")(outputs)

super(ResNet50, self).__init__(inputs, outputs)

class ResNet101(keras.models.Model):
    """
    A :class:`ResNet101 <ResNet101>` object.

:param inputs: input tensor (e.g. an instance of `keras.layers.Input`)

Usage:

>>> import keras_resnet.classifiers

>>> shape, classes = (224, 224, 3), 1000

>>> x = keras.layers.Input(shape)

>>> model = keras_resnet.classifiers.ResNet101(x)

>>> model.compile("adam", "categorical_crossentropy", ["accuracy"])
    """
    def __init__(self, inputs, classes):
        outputs = keras_resnet.models.ResNet101(inputs)

outputs = keras.layers.Flatten()(outputs.output)

outputs = keras.layers.Dense(classes, activation="softmax")(outputs)

super(ResNet101, self).__init__(inputs, outputs)

class ResNet152(keras.models.Model):
    """
    A :class:`ResNet152 <ResNet152>` object.

:param inputs: input tensor (e.g. an instance of `keras.layers.Input`)

Usage:

>>> import keras_resnet.classifiers

>>> shape, classes = (224, 224, 3), 1000

>>> x = keras.layers.Input(shape)

>>> model = keras_resnet.classifiers.ResNet152(x)

>>> model.compile("adam", "categorical_crossentropy", ["accuracy"])

"""
    def __init__(self, inputs, classes):
        outputs = keras_resnet.models.ResNet152(inputs)

outputs = keras.layers.Flatten()(outputs.output)

outputs = keras.layers.Dense(classes, activation="softmax")(outputs)

super(ResNet152, self).__init__(inputs, outputs)

class ResNet200(keras.models.Model):
    """
    A :class:`ResNet200 <ResNet200>` object.

:param inputs: input tensor (e.g. an instance of `keras.layers.Input`)

Usage:

>>> import keras_resnet.classifiers

>>> shape, classes = (224, 224, 3), 1000

>>> x = keras.layers.Input(shape)

>>> model = keras_resnet.classifiers.ResNet200(x)

>>> model.compile("adam", "categorical_crossentropy", ["accuracy"])
    """
    def __init__(self, inputs, classes):
        outputs = keras_resnet.models.ResNet200(inputs)

outputs = keras.layers.Flatten()(outputs.output)

outputs = keras.layers.Dense(classes, activation="softmax")(outputs)

super(ResNet200, self).__init__(inputs, outputs)