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inception_v3.py
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inception_v3.py
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# -*- coding: utf-8 -*-
from keras.models import Sequential
from keras.optimizers import SGD
from keras.layers import Input, Dense, Convolution2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, merge, Reshape, Activation
from keras.layers.normalization import BatchNormalization
from keras.models import Model
from keras import backend as K
from sklearn.metrics import log_loss
from load_cifar10 import load_cifar10_data
def conv2d_bn(x, nb_filter, nb_row, nb_col,
border_mode='same', subsample=(1, 1),
name=None):
"""
Utility function to apply conv + BN for Inception V3.
"""
if name is not None:
bn_name = name + '_bn'
conv_name = name + '_conv'
else:
bn_name = None
conv_name = None
bn_axis = 1
x = Convolution2D(nb_filter, nb_row, nb_col,
subsample=subsample,
activation='relu',
border_mode=border_mode,
name=conv_name)(x)
x = BatchNormalization(axis=bn_axis, name=bn_name)(x)
return x
def inception_v3_model(img_rows, img_cols, channel=1, num_classes=None):
"""
Inception-V3 Model for Keras
Model Schema is based on
https://github.com/fchollet/deep-learning-models/blob/master/inception_v3.py
ImageNet Pretrained Weights
https://github.com/fchollet/deep-learning-models/releases/download/v0.2/inception_v3_weights_th_dim_ordering_th_kernels.h5
Parameters:
img_rows, img_cols - resolution of inputs
channel - 1 for grayscale, 3 for color
num_classes - number of class labels for our classification task
"""
channel_axis = 1
img_input = Input(shape=(channel, img_rows, img_cols))
x = conv2d_bn(img_input, 32, 3, 3, subsample=(2, 2), border_mode='valid')
x = conv2d_bn(x, 32, 3, 3, border_mode='valid')
x = conv2d_bn(x, 64, 3, 3)
x = MaxPooling2D((3, 3), strides=(2, 2))(x)
x = conv2d_bn(x, 80, 1, 1, border_mode='valid')
x = conv2d_bn(x, 192, 3, 3, border_mode='valid')
x = MaxPooling2D((3, 3), strides=(2, 2))(x)
# mixed 0, 1, 2: 35 x 35 x 256
for i in range(3):
branch1x1 = conv2d_bn(x, 64, 1, 1)
branch5x5 = conv2d_bn(x, 48, 1, 1)
branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)
branch3x3dbl = conv2d_bn(x, 64, 1, 1)
branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
branch_pool = AveragePooling2D(
(3, 3), strides=(1, 1), border_mode='same')(x)
branch_pool = conv2d_bn(branch_pool, 32, 1, 1)
x = merge([branch1x1, branch5x5, branch3x3dbl, branch_pool],
mode='concat', concat_axis=channel_axis,
name='mixed' + str(i))
# mixed 3: 17 x 17 x 768
branch3x3 = conv2d_bn(x, 384, 3, 3, subsample=(2, 2), border_mode='valid')
branch3x3dbl = conv2d_bn(x, 64, 1, 1)
branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3,
subsample=(2, 2), border_mode='valid')
branch_pool = MaxPooling2D((3, 3), strides=(2, 2))(x)
x = merge([branch3x3, branch3x3dbl, branch_pool],
mode='concat', concat_axis=channel_axis,
name='mixed3')
# mixed 4: 17 x 17 x 768
branch1x1 = conv2d_bn(x, 192, 1, 1)
branch7x7 = conv2d_bn(x, 128, 1, 1)
branch7x7 = conv2d_bn(branch7x7, 128, 1, 7)
branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)
branch7x7dbl = conv2d_bn(x, 128, 1, 1)
branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1)
branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 1, 7)
branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1)
branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
branch_pool = AveragePooling2D((3, 3), strides=(1, 1), border_mode='same')(x)
branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
x = merge([branch1x1, branch7x7, branch7x7dbl, branch_pool],
mode='concat', concat_axis=channel_axis,
name='mixed4')
# mixed 5, 6: 17 x 17 x 768
for i in range(2):
branch1x1 = conv2d_bn(x, 192, 1, 1)
branch7x7 = conv2d_bn(x, 160, 1, 1)
branch7x7 = conv2d_bn(branch7x7, 160, 1, 7)
branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)
branch7x7dbl = conv2d_bn(x, 160, 1, 1)
branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1)
branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 1, 7)
branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1)
branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
branch_pool = AveragePooling2D(
(3, 3), strides=(1, 1), border_mode='same')(x)
branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
x = merge([branch1x1, branch7x7, branch7x7dbl, branch_pool],
mode='concat', concat_axis=channel_axis,
name='mixed' + str(5 + i))
# mixed 7: 17 x 17 x 768
branch1x1 = conv2d_bn(x, 192, 1, 1)
branch7x7 = conv2d_bn(x, 192, 1, 1)
branch7x7 = conv2d_bn(branch7x7, 192, 1, 7)
branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)
branch7x7dbl = conv2d_bn(x, 160, 1, 1)
branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)
branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)
branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
branch_pool = AveragePooling2D((3, 3), strides=(1, 1), border_mode='same')(x)
branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
x = merge([branch1x1, branch7x7, branch7x7dbl, branch_pool],
mode='concat', concat_axis=channel_axis,
name='mixed7')
# mixed 8: 8 x 8 x 1280
branch3x3 = conv2d_bn(x, 192, 1, 1)
branch3x3 = conv2d_bn(branch3x3, 320, 3, 3,
subsample=(2, 2), border_mode='valid')
branch7x7x3 = conv2d_bn(x, 192, 1, 1)
branch7x7x3 = conv2d_bn(branch7x7x3, 192, 1, 7)
branch7x7x3 = conv2d_bn(branch7x7x3, 192, 7, 1)
branch7x7x3 = conv2d_bn(branch7x7x3, 192, 3, 3,
subsample=(2, 2), border_mode='valid')
branch_pool = AveragePooling2D((3, 3), strides=(2, 2))(x)
x = merge([branch3x3, branch7x7x3, branch_pool],
mode='concat', concat_axis=channel_axis,
name='mixed8')
# mixed 9: 8 x 8 x 2048
for i in range(2):
branch1x1 = conv2d_bn(x, 320, 1, 1)
branch3x3 = conv2d_bn(x, 384, 1, 1)
branch3x3_1 = conv2d_bn(branch3x3, 384, 1, 3)
branch3x3_2 = conv2d_bn(branch3x3, 384, 3, 1)
branch3x3 = merge([branch3x3_1, branch3x3_2],
mode='concat', concat_axis=channel_axis,
name='mixed9_' + str(i))
branch3x3dbl = conv2d_bn(x, 448, 1, 1)
branch3x3dbl = conv2d_bn(branch3x3dbl, 384, 3, 3)
branch3x3dbl_1 = conv2d_bn(branch3x3dbl, 384, 1, 3)
branch3x3dbl_2 = conv2d_bn(branch3x3dbl, 384, 3, 1)
branch3x3dbl = merge([branch3x3dbl_1, branch3x3dbl_2],
mode='concat', concat_axis=channel_axis)
branch_pool = AveragePooling2D(
(3, 3), strides=(1, 1), border_mode='same')(x)
branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
x = merge([branch1x1, branch3x3, branch3x3dbl, branch_pool],
mode='concat', concat_axis=channel_axis,
name='mixed' + str(9 + i))
# Fully Connected Softmax Layer
x_fc = AveragePooling2D((8, 8), strides=(8, 8), name='avg_pool')(x)
x_fc = Flatten(name='flatten')(x_fc)
x_fc = Dense(1000, activation='softmax', name='predictions')(x_fc)
# Create model
model = Model(img_input, x_fc)
# Load ImageNet pre-trained data
model.load_weights('imagenet_models/inception_v3_weights_th_dim_ordering_th_kernels.h5')
# 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 = AveragePooling2D((8, 8), strides=(8, 8), name='avg_pool')(x)
x_newfc = Flatten(name='flatten')(x_newfc)
x_newfc = Dense(num_classes, activation='softmax', name='predictions')(x_newfc)
# Create another model with our customized softmax
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
if __name__ == '__main__':
# Example to fine-tune on 3000 samples from Cifar10
img_rows, img_cols = 299, 299 # Resolution of inputs
channel = 3
num_classes = 10
batch_size = 16
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 = inception_v3_model(img_rows, img_cols, channel, 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)