# -*- coding: utf-8 -*-
from keras.optimizers import SGD
from keras.layers import Input, merge, ZeroPadding2D
from keras.layers.core import Dense, Dropout, Activation
from keras.layers.convolutional import Convolution2D
from keras.layers.pooling import AveragePooling2D, GlobalAveragePooling2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.models import Model
import keras.backend as K
from sklearn.metrics import log_loss
from custom_layers.scale_layer import Scale
from load_cifar10 import load_cifar10_data
def densenet161_model(img_rows, img_cols, color_type=1, nb_dense_block=4, growth_rate=48, nb_filter=96, reduction=0.5, dropout_rate=0.0, weight_decay=1e-4, num_classes=None):
'''
DenseNet 161 Model for Keras
Model Schema is based on
https://github.com/flyyufelix/DenseNet-Keras
ImageNet Pretrained Weights
Theano: https://drive.google.com/open?id=0Byy2AcGyEVxfVnlCMlBGTDR3RGs
TensorFlow: https://drive.google.com/open?id=0Byy2AcGyEVxfUDZwVjU2cFNidTA
# Arguments
nb_dense_block: number of dense blocks to add to end
growth_rate: number of filters to add per dense block
nb_filter: initial number of filters
reduction: reduction factor of transition blocks.
dropout_rate: dropout rate
weight_decay: weight decay factor
classes: optional number of classes to classify images
weights_path: path to pre-trained weights
# Returns
A Keras model instance.
'''
eps = 1.1e-5
# compute compression factor
compression = 1.0 - reduction
# Handle Dimension Ordering for different backends
global concat_axis
if K.image_dim_ordering() == 'tf':
concat_axis = 3
img_input = Input(shape=(224, 224, 3), name='data')
else:
concat_axis = 1
img_input = Input(shape=(3, 224, 224), name='data')
# From architecture for ImageNet (Table 1 in the paper)
nb_filter = 96
nb_layers = [6,12,36,24] # For DenseNet-161
# Initial convolution
x = ZeroPadding2D((3, 3), name='conv1_zeropadding')(img_input)
x = Convolution2D(nb_filter, 7, 7, subsample=(2, 2), name='conv1', bias=False)(x)
x = BatchNormalization(epsilon=eps, axis=concat_axis, name='conv1_bn')(x)
x = Scale(axis=concat_axis, name='conv1_scale')(x)
x = Activation('relu', name='relu1')(x)
x = ZeroPadding2D((1, 1), name='pool1_zeropadding')(x)
x = MaxPooling2D((3, 3), strides=(2, 2), name='pool1')(x)
# Add dense blocks
for block_idx in range(nb_dense_block - 1):
stage = block_idx+2
x, nb_filter = dense_block(x, stage, nb_layers[block_idx], nb_filter, growth_rate, dropout_rate=dropout_rate, weight_decay=weight_decay)
# Add transition_block
x = transition_block(x, stage, nb_filter, compression=compression, dropout_rate=dropout_rate, weight_decay=weight_decay)
nb_filter = int(nb_filter * compression)
final_stage = stage + 1
x, nb_filter = dense_block(x, final_stage, nb_layers[-1], nb_filter, growth_rate, dropout_rate=dropout_rate, weight_decay=weight_decay)
x = BatchNormalization(epsilon=eps, axis=concat_axis, name='conv'+str(final_stage)+'_blk_bn')(x)
x = Scale(axis=concat_axis, name='conv'+str(final_stage)+'_blk_scale')(x)
x = Activation('relu', name='relu'+str(final_stage)+'_blk')(x)
x_fc = GlobalAveragePooling2D(name='pool'+str(final_stage))(x)
x_fc = Dense(1000, name='fc6')(x_fc)
x_fc = Activation('softmax', name='prob')(x_fc)
model = Model(img_input, x_fc, name='densenet')
if K.image_dim_ordering() == 'th':
# Use pre-trained weights for Theano backend
weights_path = 'imagenet_models/densenet161_weights_th.h5'
else:
# Use pre-trained weights for Tensorflow backend
weights_path = 'imagenet_models/densenet161_weights_tf.h5'
model.load_weights(weights_path, by_name=True)
# Truncate and replace softmax layer for transfer learning
# Cannot use model.layers.pop() since model is not of Sequential() type
# The method below works since pre-trained weights are stored in layers but not in the model
x_newfc = GlobalAveragePooling2D(name='pool'+str(final_stage))(x)
x_newfc = Dense(num_classes, name='fc6')(x_newfc)
x_newfc = Activation('softmax', name='prob')(x_newfc)
model = Model(img_input, x_newfc)
# Learning rate is changed to 0.001
sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy'])
return model
def conv_block(x, stage, branch, nb_filter, dropout_rate=None, weight_decay=1e-4):
'''Apply BatchNorm, Relu, bottleneck 1x1 Conv2D, 3x3 Conv2D, and option dropout
# Arguments
x: input tensor
stage: index for dense block
branch: layer index within each dense block
nb_filter: number of filters
dropout_rate: dropout rate
weight_decay: weight decay factor
'''
eps = 1.1e-5
conv_name_base = 'conv' + str(stage) + '_' + str(branch)
relu_name_base = 'relu' + str(stage) + '_' + str(branch)
# 1x1 Convolution (Bottleneck layer)
inter_channel = nb_filter * 4
x = BatchNormalization(epsilon=eps, axis=concat_axis, name=conv_name_base+'_x1_bn')(x)
x = Scale(axis=concat_axis, name=conv_name_base+'_x1_scale')(x)
x = Activation('relu', name=relu_name_base+'_x1')(x)
x = Convolution2D(inter_channel, 1, 1, name=conv_name_base+'_x1', bias=False)(x)
if dropout_rate:
x = Dropout(dropout_rate)(x)
# 3x3 Convolution
x = BatchNormalization(epsilon=eps, axis=concat_axis, name=conv_name_base+'_x2_bn')(x)
x = Scale(axis=concat_axis, name=conv_name_base+'_x2_scale')(x)
x = Activation('relu', name=relu_name_base+'_x2')(x)
x = ZeroPadding2D((1, 1), name=conv_name_base+'_x2_zeropadding')(x)
x = Convolution2D(nb_filter, 3, 3, name=conv_name_base+'_x2', bias=False)(x)
if dropout_rate:
x = Dropout(dropout_rate)(x)
return x
def transition_block(x, stage, nb_filter, compression=1.0, dropout_rate=None, weight_decay=1E-4):
''' Apply BatchNorm, 1x1 Convolution, averagePooling, optional compression, dropout
# Arguments
x: input tensor
stage: index for dense block
nb_filter: number of filters
compression: calculated as 1 - reduction. Reduces the number of feature maps in the transition block.
dropout_rate: dropout rate
weight_decay: weight decay factor
'''
eps = 1.1e-5
conv_name_base = 'conv' + str(stage) + '_blk'
relu_name_base = 'relu' + str(stage) + '_blk'
pool_name_base = 'pool' + str(stage)
x = BatchNormalization(epsilon=eps, axis=concat_axis, name=conv_name_base+'_bn')(x)
x = Scale(axis=concat_axis, name=conv_name_base+'_scale')(x)
x = Activation('relu', name=relu_name_base)(x)
x = Convolution2D(int(nb_filter * compression), 1, 1, name=conv_name_base, bias=False)(x)
if dropout_rate:
x = Dropout(dropout_rate)(x)
x = AveragePooling2D((2, 2), strides=(2, 2), name=pool_name_base)(x)
return x
def dense_block(x, stage, nb_layers, nb_filter, growth_rate, dropout_rate=None, weight_decay=1e-4, grow_nb_filters=True):
''' Build a dense_block where the output of each conv_block is fed to subsequent ones
# Arguments
x: input tensor
stage: index for dense block
nb_layers: the number of layers of conv_block to append to the model.
nb_filter: number of filters
growth_rate: growth rate
dropout_rate: dropout rate
weight_decay: weight decay factor
grow_nb_filters: flag to decide to allow number of filters to grow
'''
eps = 1.1e-5
concat_feat = x
for i in range(nb_layers):
branch = i+1
x = conv_block(concat_feat, stage, branch, growth_rate, dropout_rate, weight_decay)
concat_feat = merge([concat_feat, x], mode='concat', concat_axis=concat_axis, name='concat_'+str(stage)+'_'+str(branch))
if grow_nb_filters:
nb_filter += growth_rate
return concat_feat, nb_filter
if __name__ == '__main__':
# Example to fine-tune on 3000 samples from Cifar10
img_rows, img_cols = 224, 224 # Resolution of inputs
channel = 3
num_classes = 10
batch_size = 8
nb_epoch = 10
# Load Cifar10 data. Please implement your own load_data() module for your own dataset
X_train, Y_train, X_valid, Y_valid = load_cifar10_data(img_rows, img_cols)
# Load our model
model = densenet161_model(img_rows=img_rows, img_cols=img_cols, color_type=channel, num_classes=num_classes)
# Start Fine-tuning
model.fit(X_train, Y_train,
batch_size=batch_size,
nb_epoch=nb_epoch,
shuffle=True,
verbose=1,
validation_data=(X_valid, Y_valid),
)
# Make predictions
predictions_valid = model.predict(X_valid, batch_size=batch_size, verbose=1)
# Cross-entropy loss score
score = log_loss(Y_valid, predictions_valid)
