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Inception-v3 implementation in Keras
from keras.models import Model
from keras.layers import (
Input,
Dense,
Flatten,
merge,
Lambda
)
from keras.layers.convolutional import (
Convolution2D,
MaxPooling2D,
AveragePooling2D,
ZeroPadding2D
)
from keras.layers.normalization import BatchNormalization
from keras.regularizers import l2
import keras.backend as K
# Evidently this model breaks Python's default recursion limit
# This is a theano issue
import sys
sys.setrecursionlimit(10000)
def BNConv(nb_filter, nb_row, nb_col, w_decay, subsample=(1, 1), border_mode="same"):
def f(input):
conv = Convolution2D(nb_filter=nb_filter, nb_row=nb_row, nb_col=nb_col, subsample=subsample,
border_mode=border_mode, activation="relu",
W_regularizer=l2(w_decay) if w_decay else None, init="he_normal")(input)
return BatchNormalization(mode=0, axis=1)(conv)
return f
def inception_v3(w_decay=None):
input = Input(shape=(3, 299, 299))
conv_1 = BNConv(32, 3, 3, w_decay, subsample=(2, 2), border_mode="valid")(input)
conv_2 = BNConv(32, 3, 3, w_decay, border_mode="valid")(conv_1)
conv_3 = BNConv(64, 3, 3, w_decay)(conv_2)
pool_4 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), border_mode="valid")(conv_3)
conv_5 = BNConv(80, 1, 1, w_decay)(pool_4)
conv_6 = BNConv(192, 3, 3, w_decay, border_mode="valid")(conv_5)
pool_7 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), border_mode="valid")(conv_6)
inception_8 = InceptionFig5(w_decay)(pool_7)
inception_9 = InceptionFig5(w_decay)(inception_8)
inception_10 = InceptionFig5(w_decay)(inception_9)
inception_11 = DimReductionA(w_decay)(inception_10)
inception_12 = InceptionFig6(w_decay)(inception_11)
inception_13 = InceptionFig6(w_decay)(inception_12)
inception_14 = InceptionFig6(w_decay)(inception_13)
inception_15 = InceptionFig6(w_decay)(inception_14)
inception_16 = InceptionFig6(w_decay)(inception_15)
inception_17 = DimReductionB(w_decay)(inception_16)
inception_18 = InceptionFig7(w_decay)(inception_17)
inception_19 = InceptionFig7(w_decay)(inception_18)
pool_20 = Lambda(lambda x: K.mean(x, axis=(2, 3)), output_shape=(2048, ))(inception_19)
model = Model(input, pool_20)
return model
def InceptionFig5(w_decay):
def f(input):
# Tower A
conv_a1 = BNConv(64, 1, 1, w_decay)(input)
conv_a2 = BNConv(96, 3, 3, w_decay)(conv_a1)
conv_a3 = BNConv(96, 3, 3, w_decay)(conv_a2)
# Tower B
conv_b1 = BNConv(48, 1, 1, w_decay)(input)
conv_b2 = BNConv(64, 3, 3, w_decay)(conv_b1)
# Tower C
pool_c1 = AveragePooling2D(pool_size=(3, 3), strides=(1, 1), border_mode="same")(input)
conv_c2 = BNConv(64, 1, 1, w_decay)(pool_c1)
# Tower D
conv_d1 = BNConv(64, 1, 1, w_decay)(input)
return merge([conv_a3, conv_b2, conv_c2, conv_d1], mode='concat', concat_axis=1)
return f
def InceptionFig6(w_decay):
def f(input):
conv_a1 = BNConv(128, 1, 1, w_decay)(input)
conv_a2 = BNConv(128, 1, 7, w_decay)(conv_a1)
conv_a3 = BNConv(128, 7, 1, w_decay)(conv_a2)
conv_a4 = BNConv(128, 1, 7, w_decay)(conv_a3)
conv_a5 = BNConv(192, 7, 1, w_decay)(conv_a4)
# Tower B
conv_b1 = BNConv(128, 1, 1, w_decay)(input)
conv_b2 = BNConv(128, 1, 7, w_decay)(conv_b1)
conv_b3 = BNConv(192, 7, 1, w_decay)(conv_b2)
# Tower C
pool_c1 = AveragePooling2D(pool_size=(3, 3), strides=(1, 1), border_mode="same")(input)
conv_c2 = BNConv(192, 1, 1, w_decay)(pool_c1)
# Tower D
conv_d = BNConv(192, 1, 1, w_decay)(input)
return merge([conv_a5, conv_b3, conv_c2, conv_d], mode="concat", concat_axis=1)
return f
def InceptionFig7(w_decay):
def f(input):
# Tower A
conv_a1 = BNConv(448, 1, 1, w_decay)(input)
conv_a2 = BNConv(384, 3, 3, w_decay)(conv_a1)
conv_a3 = BNConv(384, 1, 3, w_decay)(conv_a2)
conv_a4 = BNConv(384, 3, 1, w_decay)(conv_a3)
# Tower B
conv_b1 = BNConv(384, 1, 1, w_decay)(input)
conv_b2 = BNConv(384, 1, 3, w_decay)(conv_b1)
conv_b3 = BNConv(384, 3, 1, w_decay)(conv_b2)
# Tower C
pool_c1 = AveragePooling2D(pool_size=(3, 3), strides=(1, 1), border_mode="same")(input)
conv_c2 = BNConv(192, 1, 1, w_decay)(pool_c1)
# Tower D
conv_d = BNConv(320, 1, 1, w_decay)(input)
return merge([conv_a4, conv_b3, conv_c2, conv_d], mode="concat", concat_axis=1)
return f
def DimReductionA(w_decay):
def f(input):
conv_a1 = BNConv(64, 1, 1, w_decay)(input)
conv_a2 = BNConv(96, 3, 3, w_decay)(conv_a1)
conv_a3 = BNConv(96, 3, 3, w_decay, subsample=(2, 2), border_mode="valid")(conv_a2)
# another inconsistency between model.txt and the paper
# the Fig 10 in the paper shows a 1x1 convolution before
# the 3x3. Going with model.txt
conv_b = BNConv(384, 3, 3, w_decay, subsample=(2, 2), border_mode="valid")(input)
pool_c = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), border_mode="valid")(input)
return merge([conv_a3, conv_b, pool_c], mode="concat", concat_axis=1)
return f
def DimReductionB(w_decay):
def f(input):
# Tower A
conv_a1 = BNConv(192, 1, 1, w_decay)(input)
conv_a2 = BNConv(320, 3, 3, w_decay, subsample=(2, 2), border_mode="valid")(conv_a1)
# Tower B
conv_b1 = BNConv(192, 1, 1, w_decay)(input)
conv_b2 = BNConv(192, 1, 7, w_decay)(conv_b1)
conv_b3 = BNConv(192, 7, 1, w_decay)(conv_b2)
conv_b4 = BNConv(192, 3, 3, w_decay, subsample=(2, 2), border_mode="valid")(conv_b3)
# Tower C
pool_c = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), border_mode="valid")(input)
return merge([conv_a2, conv_b4, pool_c], mode="concat", concat_axis=1)
return f
def main():
import time
start = time.time()
model = inception_v3()
duration = time.time() - start
print "{} s to make model".format(duration)
start = time.time()
model.output
duration = time.time() - start
print "{} s to get output".format(duration)
start = time.time()
model.compile(loss="categorical_crossentropy", optimizer="sgd")
# model.compile(loss={"distance": "binary_crossentropy"}, optimizer="sgd")
duration = time.time() - start
print "{} s to get compile".format(duration)
if __name__ == '__main__':
main()
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