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Merge pull request #23 from titu1994/ror
Added Residual Of Residual Networks
This commit is contained in:
Junwei Pan
@@ -16,7 +16,7 @@ from keras.datasets import cifar10
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from keras.optimizers import Adam
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from keras.preprocessing.image import ImageDataGenerator
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from keras.utils import np_utils
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from keras_contrib.applications.densenet import DenseNet
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from keras_contrib.applications import DenseNet
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batch_size = 64
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nb_classes = 10
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@@ -0,0 +1,55 @@
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'''
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Trains a Residual-of-Residual Network (WRN-40-2) model on the CIFAR-10 Dataset.
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Gets a 94.53% accuracy score after 150 epochs.
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'''
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import numpy as np
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import sklearn.metrics as metrics
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import keras.callbacks as callbacks
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import keras.utils.np_utils as kutils
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from keras.datasets import cifar10
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from keras.preprocessing.image import ImageDataGenerator
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from keras.optimizers import Adam
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from keras_contrib.applications import ResidualOfResidual
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batch_size = 64
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nb_epoch = 150
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img_rows, img_cols = 32, 32
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(trainX, trainY), (testX, testY) = cifar10.load_data()
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trainX = trainX.astype('float32')
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testX = testX.astype('float32')
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trainX /= 255
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testX /= 255
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tempY = testY
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trainY = kutils.to_categorical(trainY)
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testY = kutils.to_categorical(testY)
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generator = ImageDataGenerator(rotation_range=15,
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width_shift_range=5. / 32,
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height_shift_range=5. / 32)
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generator.fit(trainX, seed=0)
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model = ResidualOfResidual(depth=40, width=2, dropout_rate=0.0, weights='None')
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optimizer = Adam(lr=1e-3)
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model.compile(loss="categorical_crossentropy", optimizer=optimizer, metrics=["acc"])
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print("Finished compiling")
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model.fit_generator(generator.flow(trainX, trainY, batch_size=batch_size), samples_per_epoch=len(trainX),
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nb_epoch=nb_epoch,
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callbacks=[callbacks.ModelCheckpoint("weights/RoR-WRN-40-2-Weights.h5", monitor="val_acc",
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save_best_only=True, save_weights_only=True)],
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validation_data=(testX, testY),
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nb_val_samples=testX.shape[0], verbose=2)
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scores = model.evaluate(testX, testY, batch_size)
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print("Test loss : ", scores[0])
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print("Test accuracy : ", scores[1])
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@@ -0,0 +1,2 @@
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from .densenet import DenseNet
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from .ror import ResidualOfResidual
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@@ -0,0 +1,290 @@
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# -*- coding: utf-8 -*-
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"""Residual of Residual Network models for Keras.
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# Reference
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- [Residual Networks of Residual Networks: Multilevel Residual Networks](https://arxiv.org/abs/1608.02908)
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"""
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from __future__ import print_function
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from __future__ import absolute_import
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from __future__ import division
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import warnings
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from keras.models import Model
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from keras.layers.core import Dense, Dropout, Activation, Flatten
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from keras.layers.convolutional import Convolution2D
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from keras.layers.pooling import AveragePooling2D, MaxPooling2D
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from keras.layers import Input, merge
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from keras.layers.normalization import BatchNormalization
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from keras.utils.layer_utils import convert_all_kernels_in_model
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from keras.utils.data_utils import get_file
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from keras.engine.topology import get_source_inputs
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from keras.applications.imagenet_utils import _obtain_input_shape
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import keras.backend as K
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TH_WEIGHTS_PATH = 'https://github.com/titu1994/Residual-of-Residual-Networks/releases/download/v0.2/ror_wrn_40_2_th_kernels_th_dim_ordering.h5'
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TF_WEIGHTS_PATH = 'https://github.com/titu1994/Residual-of-Residual-Networks/releases/download/v0.2/ror_wrn_40_2_tf_kernels_tf_dim_ordering.h5'
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TH_WEIGHTS_PATH_NO_TOP = 'https://github.com/titu1994/Residual-of-Residual-Networks/releases/download/v0.2/ror_wrn_40_2_th_kernels_th_dim_ordering_no_top.h5'
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TF_WEIGHTS_PATH_NO_TOP = 'https://github.com/titu1994/Residual-of-Residual-Networks/releases/download/v0.2/ror_wrn_40_2_tf_kernels_tf_dim_ordering_no_top.h5'
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def ResidualOfResidual(depth=40, width=2, dropout_rate=0.0,
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include_top=True, weights='cifar10',
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input_tensor=None, input_shape=None,
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classes=10):
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"""Instantiate the Residual of Residual Network architecture,
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optionally loading weights pre-trained
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on CIFAR-10. Note that when using TensorFlow,
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for best performance you should set
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`image_dim_ordering="tf"` in your Keras config
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at ~/.keras/keras.json.
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The model and the weights are compatible with both
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TensorFlow and Theano. The dimension ordering
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convention used by the model is the one
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specified in your Keras config file.
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# Arguments
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depth: number or layers in the DenseNet
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width: width of the network
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dropout_rate: dropout rate
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include_top: whether to include the fully-connected
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layer at the top of the network.
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weights: one of `None` (random initialization) or
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"cifar10" (pre-training on CIFAR-10)..
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input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
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to use as image input for the model.
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input_shape: optional shape tuple, only to be specified
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if `include_top` is False (otherwise the input shape
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has to be `(32, 32, 3)` (with `tf` dim ordering)
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or `(3, 32, 32)` (with `th` dim ordering).
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It should have exactly 3 inputs channels,
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and width and height should be no smaller than 8.
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E.g. `(200, 200, 3)` would be one valid value.
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classes: optional number of classes to classify images
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into, only to be specified if `include_top` is True, and
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if no `weights` argument is specified.
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# Returns
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A Keras model instance.
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"""
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if weights not in {'cifar10', None}:
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raise ValueError('The `weights` argument should be either '
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'`None` (random initialization) or `cifar10` '
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'(pre-training on CIFAR-10).')
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if weights == 'cifar10' and include_top and classes != 10:
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raise ValueError('If using `weights` as CIFAR 10 with `include_top`'
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' as true, `classes` should be 10')
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if (depth - 4) % 6 != 0:
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raise ValueError('Depth of the network must be such that (depth - 4)'
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'should be divisible by 6.')
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# Determine proper input shape
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input_shape = _obtain_input_shape(input_shape,
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default_size=32,
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min_size=8,
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dim_ordering=K.image_dim_ordering(),
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include_top=include_top)
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if input_tensor is None:
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img_input = Input(shape=input_shape)
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else:
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if not K.is_keras_tensor(input_tensor):
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img_input = Input(tensor=input_tensor, shape=input_shape)
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else:
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img_input = input_tensor
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x = __create_pre_residual_of_residual(classes, img_input, include_top,
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depth, width, dropout_rate)
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# Ensure that the model takes into account
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# any potential predecessors of `input_tensor`.
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if input_tensor is not None:
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inputs = get_source_inputs(input_tensor)
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else:
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inputs = img_input
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# Create model.
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model = Model(inputs, x, name='residual_of_residual')
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# load weights
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if weights == 'cifar10':
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if (depth == 40) and (width == 2) and (dropout_rate == 0.0):
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# Default parameters match. Weights for this model exist:
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if K.image_dim_ordering() == 'th':
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if include_top:
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weights_path = get_file('ror_wrn_40_2_th_kernels_th_dim_ordering.h5',
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TH_WEIGHTS_PATH,
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cache_subdir='models')
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else:
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weights_path = get_file('ror_wrn_40_2_th_kernels_th_dim_ordering_no_top.h5',
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TH_WEIGHTS_PATH_NO_TOP,
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cache_subdir='models')
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model.load_weights(weights_path)
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if K.backend() == 'tensorflow':
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warnings.warn('You are using the TensorFlow backend, yet you '
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'are using the Theano '
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'image dimension ordering convention '
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'(`image_dim_ordering="th"`). '
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'For best performance, set '
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'`image_dim_ordering="tf"` in '
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'your Keras config '
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'at ~/.keras/keras.json.')
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convert_all_kernels_in_model(model)
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else:
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if include_top:
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weights_path = get_file('ror_wrn_40_2_tf_kernels_tf_dim_ordering_no_top.h5',
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TF_WEIGHTS_PATH,
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cache_subdir='models')
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else:
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weights_path = get_file('ror_wrn_40_2_tf_kernels_tf_dim_ordering_no_top.h5',
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TF_WEIGHTS_PATH_NO_TOP,
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cache_subdir='models')
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model.load_weights(weights_path)
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if K.backend() == 'theano':
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convert_all_kernels_in_model(model)
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return model
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def __initial_conv_block(input, k=1, dropout=0.0, initial=False):
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init = input
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channel_axis = 1 if K.image_dim_ordering() == "th" else -1
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# Check if input number of filters is same as 16 * k, else create convolution2d for this input
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if initial:
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if K.image_dim_ordering() == "th":
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init = Convolution2D(16 * k, 1, 1, init='he_normal', border_mode='same')(init)
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else:
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init = Convolution2D(16 * k, 1, 1, init='he_normal', border_mode='same')(init)
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x = BatchNormalization(axis=channel_axis)(input)
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x = Activation('relu')(x)
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x = Convolution2D(16 * k, 3, 3, border_mode='same', init='he_normal')(x)
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if dropout > 0.0:
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x = Dropout(dropout)(x)
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x = BatchNormalization(axis=channel_axis)(x)
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x = Activation('relu')(x)
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x = Convolution2D(16 * k, 3, 3, border_mode='same', init='he_normal')(x)
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m = merge([init, x], mode='sum')
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return m
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def __conv_block(input, nb_filters=32, k=1, dropout=0.0):
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init = input
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channel_axis = 1 if K.image_dim_ordering() == "th" else -1
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# Check if input number of filters is same as 32 * k, else create convolution2d for this input
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if K.image_dim_ordering() == "th":
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if init._keras_shape[1] != nb_filters * k:
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init = Convolution2D(nb_filters * k, 1, 1, init='he_normal', border_mode='same')(init)
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else:
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if init._keras_shape[-1] != nb_filters * k:
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init = Convolution2D(nb_filters * k, 1, 1, init='he_normal', border_mode='same')(init)
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x = BatchNormalization(axis=channel_axis)(input)
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x = Activation('relu')(x)
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x = Convolution2D(nb_filters * k, 3, 3, border_mode='same', init='he_normal')(x)
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if dropout > 0.0:
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x = Dropout(dropout)(x)
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x = BatchNormalization(axis=channel_axis)(x)
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x = Activation('relu')(x)
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x = Convolution2D(nb_filters * k, 3, 3, border_mode='same', init='he_normal')(x)
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m = merge([init, x], mode='sum')
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return m
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def __create_pre_residual_of_residual(nb_classes, img_input, include_top, depth=28, width=1, dropout=0.0):
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'''
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Creates a Residual Network of Residual Network with specified parameters
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Example : To create a Pre-RoR model, use k = 1
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model = ResidualOfResidual(depth=28, width=1) # Pre-RoR-3
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To create a RoR-WRN model, use k > 1
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model = ResidualOfResidual(depth=28, width=10) # Pre-RoR-3, RoR-3-WRN-28-10
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Args:
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nb_classes: number of classes
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img_input: tuple of shape (channels, rows, columns) or (rows, columns, channels)
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include_top: flag to include the final Dense layer
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depth: depth of the network
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width: width of the network
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dropout: Adds dropout if value is greater than 0.0.
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Note : Generally not used in RoR
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Returns: a Keras Model
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'''
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N = (depth - 4) // 6
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channel_axis = 1 if K.image_dim_ordering() == "th" else -1
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# Initial convolution layer
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x = Convolution2D(16, 3, 3, border_mode='same', init='he_normal')(img_input)
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nb_conv = 4 # Dont count 4 long residual connections in WRN models
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conv0_level1_shortcut = Convolution2D(64 * width, 1, 1, border_mode='same', subsample=(4, 4),
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name='conv0_level1_shortcut')(x)
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conv1_level2_shortcut = Convolution2D(16 * width, 1, 1, border_mode='same',
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name='conv1_level2_shortcut')(x)
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for i in range(N):
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initial = (i == 0)
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x = __initial_conv_block(x, k=width, dropout=dropout, initial=initial)
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nb_conv += 2
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# Add Level 2 shortcut
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x = merge([x, conv1_level2_shortcut], mode='sum')
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x = MaxPooling2D((2, 2))(x)
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conv2_level2_shortcut = Convolution2D(32 * width, 1, 1, border_mode='same',
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name='conv2_level2_shortcut')(x)
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for i in range(N):
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x = __conv_block(x, k=width, dropout=dropout)
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nb_conv += 2
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# Add Level 2 shortcut
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x = merge([x, conv2_level2_shortcut], mode='sum')
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x = MaxPooling2D((2, 2))(x)
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conv3_level2_shortcut = Convolution2D(64 * width, 1, 1, border_mode='same',
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name='conv3_level2_shortcut')(x)
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for i in range(N):
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x = __conv_block(x, nb_filters=64, k=width, dropout=dropout)
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nb_conv += 2
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# Add Level 2 shortcut
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x = merge([x, conv3_level2_shortcut], mode='sum')
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# Add Level 1 shortcut
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x = merge([x, conv0_level1_shortcut], mode='sum')
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x = BatchNormalization(axis=channel_axis)(x)
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x = Activation('relu')(x)
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x = AveragePooling2D((8, 8))(x)
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if include_top:
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x = Flatten()(x)
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x = Dense(nb_classes, activation='softmax')(x)
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return x
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