def createNewModel(patchSize):
seed=5
np.random.seed(seed)
input=Input(shape=(1,patchSize[0, 0], patchSize[0, 1]))
out1=Conv2D(filters=64,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='valid',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(input)
out2=Conv2D(filters=64,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='valid',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out1)
sout3=Conv2D(filters=128,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out2)
out3=Conv2D(filters=64,kernel_size=(1,1),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out2)
out4=Conv2D(filters=128,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out3)
out4=Conv2D(filters=128,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out4)
out4=add([sout3,out4])
out4=pool2(pool_size=(2,2),data_format='channels_first')(out4)
sout5=Conv2D(filters=256,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out4)
out5=Conv2D(filters=64,kernel_size=(1,1),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out4)
out6=Conv2D(filters=256,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out5)
out6=Conv2D(filters=256,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out6)
out6=add([sout5,out6])
out6=pool2(pool_size=(2,2),data_format='channels_first')(out6)
out10=Flatten()(out6)
out11=Dense(units=11,
kernel_initializer='normal',
kernel_regularizer='l2',
activation='softmax')(out10)
cnn = Model(inputs=input,outputs=out11)
return cnn
def createNewModel(patchSize):
seed=5
np.random.seed(seed)
input=Input(shape=(1,patchSize[0, 0], patchSize[0, 1]))
out=Conv2D(filters=64,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(input)
out1=Conv2D(filters=64,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out)
#out1=pool2(pool_size=(3,3),strides=(2,2),data_format='channels_first')(out)
sout1=Conv2D(filters=128,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out1)
out=Block(out1,128,with_shortcut=True)
out=Block(out,128,with_shortcut=False)
out=add([sout1,out])
out=concatenate(inputs=[out1,out],axis=1)
out2=pool2(pool_size=(3,3),strides=(2,2),data_format='channels_first')(out)
sout2=Conv2D(filters=256,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out2)
out=Block(out2,256,with_shortcut=True)
out=Block(out,256,with_shortcut=False)
out=add([sout2,out])
out1=pool2(pool_size=(3,3),strides=(2,2),data_format="channels_first")(out1)
out=concatenate(inputs=[out1,out2,out],axis=1)
out3=pool2(pool_size=(3,3),strides=(2,2),data_format='channels_first')(out)
#out5=GlobalAveragePooling2D(data_format='channels_first')(out3)
out5=Flatten()(out3)
out6=Dense(units=11,
kernel_initializer='normal',
kernel_regularizer='l2',
activation='softmax')(out5)
cnn = Model(inputs=input,outputs=out6)
return cnn
def createNewModel(patchSize):
seed=5
np.random.seed(seed)
input=Input(shape=(1,patchSize[0, 0], patchSize[0, 1]))
out1=Conv2D(filters=64,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='valid',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(input)
out2=Conv2D(filters=64,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='valid',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out1)
out2=pool2(pool_size=(2,2),data_format='channels_first')(out2)
out3=Conv2D(filters=128,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out2)
out4=Conv2D(filters=128,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out3)
out4=add([out2,out4])
out4=pool2(pool_size=(2,2),data_format='channels_first')(out4)
out5_1=Conv2D(filters=32,kernel_size=(1,1),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out4)
out5_2=Conv2D(filters=32,kernel_size=(1,1),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out4)
out5_2=Conv2D(filters=128,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out5_2)
out5_3=Conv2D(filters=32,kernel_size=(1,1),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out4)
out5_3=Conv2D(filters=128,kernel_size=(5,5),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out5_3)
out5_4=pool2(pool_size=(3,3),strides=(1,1),padding='same',data_format='channels_first')(out4)
out5_4=Conv2D(filters=128,kernel_size=(1,1),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out5_4)
out5=concatenate(inputs=[out5_1,out5_2,out5_3],axis=1)
# out6_1=Conv2D(filters=32,kernel_initializer='he_normal',weights=None,padding='valid',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out5)
#
# out6_2=Conv2D(filters=32,kernel_size=(1,1),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out5)
# out6_2=Conv2D(filters=128,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out6_2)
#
# out6_3=Conv2D(filters=32,kernel_size=(1,1),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out5)
# out6_3=Conv2D(filters=128,kernel_size=(5,5),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out6_3)
#
# out6_4=pool2(pool_size=(3,3),strides=(1,1),padding='same',data_format='channels_first')(out5)
# out6_4=Conv2D(filters=128,kernel_size=(1,1),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out6_4)
#
# out6=concatenate(inputs=[out6_1,out6_2,out6_3,out6_4],axis=1)
out7=Conv2D(filters=256,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),
activation='relu')(out5)
out8=Conv2D(filters=256,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),
activation='relu')(out7)
out8=add([out5, out8])
out8=pool2(pool_size=(2,2),data_format='channels_first')(out8)
out9=Flatten()(out8)
out10=Dense(units=11,
kernel_initializer='normal',
kernel_regularizer='l2',
activation='softmax')(out9)
cnn = Model(inputs=input,outputs=out10)
return cnn
def create180180Model(patchSize, iVersion=1):
seed = 5
np.random.seed(seed)
input = Input(shape=(1, patchSize[0, 0], patchSize[0, 1]))
# DenseResNet 180180 (selected)
if (iVersion == 1):
out = Conv2D(filters=64,
kernel_size=(3, 3),
kernel_initializer='he_normal',
weights=None,
padding='valid',
strides=(2, 2),
kernel_regularizer=l2(1e-6),
activation='relu')(input)
out = Conv2D(filters=64,
kernel_size=(3, 3),
kernel_initializer='he_normal',
weights=None,
padding='valid',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out)
out1 = pool2(pool_size=(3, 3),
strides=(2, 2),
data_format='channels_first')(out)
out = Block(out1, 128, with_shortcut=True)
out = Block(out, 128, with_shortcut=False)
out = concatenate(inputs=[out1, out], axis=1)
out2 = pool2(pool_size=(3, 3),
strides=(2, 2),
data_format='channels_first')(out)
out = Block(out2, 128, with_shortcut=True)
out = Block(out, 128, with_shortcut=False)
out1 = pool2(pool_size=(2, 2), data_format="channels_first")(out1)
out = concatenate(inputs=[out1, out2, out], axis=1)
out3 = pool2(pool_size=(3, 3),
strides=(2, 2),
data_format='channels_first')(out)
out = Block(out3, 256, with_shortcut=True)
out = Block(out, 256, with_shortcut=False)
out2 = pool2(pool_size=(2, 2), data_format="channels_first")(out2)
out = concatenate(inputs=[out2, out3, out], axis=1)
out4 = pool2(pool_size=(3, 3),
strides=(2, 2),
data_format='channels_first')(out)
#out5=GlobalAveragePooling2D(data_format='channels_first')(out4)
out5 = Flatten()(out4)
out6 = Dense(units=11,
kernel_initializer='normal',
kernel_regularizer='l2',
activation='softmax')(out5)
cnn = Model(inputs=input, outputs=out6)
# temp/resdensenet2 for 180180
elif iVersion == 2:
out = Conv2D(filters=64,
kernel_size=(3, 3),
kernel_initializer='he_normal',
weights=None,
padding='valid',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(input)
out = Conv2D(filters=64,
kernel_size=(3, 3),
kernel_initializer='he_normal',
weights=None,
padding='valid',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out)
out1 = pool2(pool_size=(3, 3),
strides=(2, 2),
data_format='channels_first')(out)
out = Block(out1, 128, with_shortcut=True)
out = Block(out, 128, with_shortcut=False)
out = Block(out, 128, with_shortcut=False)
out = concatenate(inputs=[out1, out], axis=1)
out2 = pool2(pool_size=(3, 3),
strides=(2, 2),
data_format='channels_first')(out)
out = Block(out2, 128, with_shortcut=True)
out = Block(out, 128, with_shortcut=False)
out = Block(out, 128, with_shortcut=False)
out1 = pool2(pool_size=(2, 2), data_format="channels_first")(out1)
out = concatenate(inputs=[out1, out2, out], axis=1)
out3 = pool2(pool_size=(3, 3),
strides=(2, 2),
data_format='channels_first')(out)
out = Block(out3, 256, with_shortcut=True)
out = Block(out, 256, with_shortcut=False)
out = Block(out, 256, with_shortcut=False)
out2 = pool2(pool_size=(2, 2), data_format="channels_first")(out2)
out = concatenate(inputs=[out2, out3, out], axis=1)
out4 = pool2(pool_size=(3, 3),
strides=(2, 2),
data_format='channels_first')(out)
# out5=GlobalAveragePooling2D(data_format='channels_first')(out4)
out5 = Flatten()(out4)
out6 = Dense(units=11,
kernel_initializer='normal',
kernel_regularizer='l2',
activation='softmax')(out5)
cnn = Model(inputs=input, outputs=out6)
elif iVersion == 3:
out = Conv2D(filters=64,
kernel_size=(3, 3),
kernel_initializer='he_normal',
weights=None,
padding='valid',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(input)
out = Conv2D(filters=64,
kernel_size=(3, 3),
kernel_initializer='he_normal',
weights=None,
padding='valid',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out)
out1 = pool2(pool_size=(3, 3),
strides=(2, 2),
data_format='channels_first')(out)
out = Block(out1, 128, with_shortcut=True)
out = Block(out, 128, with_shortcut=False)
out = Block(out, 128, with_shortcut=False)
out = concatenate(inputs=[out1, out], axis=1)
out2 = pool2(pool_size=(3, 3),
strides=(2, 2),
data_format='channels_first')(out)
out = Block(out2, 128, with_shortcut=True)
out = Block(out, 128, with_shortcut=False)
out = Block(out, 128, with_shortcut=False)
out1 = pool2(pool_size=(3, 3),
strides=(2, 2),
data_format="channels_first")(out1)
out = concatenate(inputs=[out1, out2, out], axis=1)
out3 = pool2(pool_size=(3, 3),
strides=(2, 2),
data_format='channels_first')(out)
out = Block(out3, 256, with_shortcut=True)
out = Block(out, 256, with_shortcut=False)
out2 = pool2(pool_size=(3, 3),
strides=(2, 2),
data_format="channels_first")(out2)
out = concatenate(inputs=[out2, out3, out], axis=1)
out4 = pool2(pool_size=(3, 3),
strides=(2, 2),
data_format='channels_first')(out)
# out5=GlobalAveragePooling2D(data_format='channels_first')(out4)
out5 = Flatten()(out4)
out6 = Dense(units=11,
kernel_initializer='normal',
kernel_regularizer='l2',
activation='softmax')(out5)
cnn = Model(inputs=input, outputs=out6)
return cnn
def createModel(patchSize, iVersion = 1):
seed=5
np.random.seed(seed)
input=Input(shape=(1,patchSize[0, 0], patchSize[0, 1]))
# googlenet7 for 120120 180180
if iVersion == 1:
out1=Conv2D(filters=32,
kernel_size=(3,3),
kernel_initializer='he_normal',
weights=None,
padding='valid',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(input)
out2=Conv2D(filters=64,
kernel_size=(3,3),
kernel_initializer='he_normal',
weights=None,
padding='valid',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out1)
out2=pool2(pool_size=(2,2),data_format='channels_first')(out2)
out3=Conv2D(filters=128, # learning rate: 0.1 -> 76%
kernel_size=(3,3),
kernel_initializer='he_normal',
weights=None,
padding='valid',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out2)
out4=Conv2D(filters=128, # learning rate: 0.1 -> 76%
kernel_size=(3,3),
kernel_initializer='he_normal',
weights=None,
padding='valid',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out3)
out4=pool2(pool_size=(2,2),data_format='channels_first')(out4)
out5_1=Conv2D(filters=32,
kernel_size=(1,1),
kernel_initializer='he_normal',
weights=None,
padding='same',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out4)
out5_2=Conv2D(filters=32, # learning rate: 0.1 -> 76%
kernel_size=(1,1),
kernel_initializer='he_normal',
weights=None,
padding='same',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out4)
out5_2=Conv2D(filters=128, # learning rate: 0.1 -> 76%
kernel_size=(3,3),
kernel_initializer='he_normal',
weights=None,
padding='same',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out5_2)
out5_3=Conv2D(filters=32, # learning rate: 0.1 -> 76%
kernel_size=(1,1),
kernel_initializer='he_normal',
weights=None,
padding='same',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out4)
out5_3=Conv2D(filters=128, # learning rate: 0.1 -> 76%
kernel_size=(5,5),
kernel_initializer='he_normal',
weights=None,
padding='same',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out5_3)
out5_4=pool2(pool_size=(3,3),strides=(1,1),padding='same',data_format='channels_first')(out4)
out5_4=Conv2D(filters=128, # learning rate: 0.1 -> 76%
kernel_size=(1,1),
kernel_initializer='he_normal',
weights=None,
padding='same',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out5_4)
out5=concatenate(inputs=[out5_1,out5_2,out5_3],axis=1)
out7=Conv2D(filters=256, # learning rate: 0.1 -> 76%
kernel_size=(3,3),
kernel_initializer='he_normal',
weights=None,
padding='valid',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out5)
#out7=pool2(pool_size=(2,2),data_format='channels_first')(out7)
out8=Conv2D(filters=256, # learning rate: 0.1 -> 76%
kernel_size=(3,3),
kernel_initializer='he_normal',
weights=None,
padding='valid',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out7)
out8=pool2(pool_size=(2,2),data_format='channels_first')(out8)
out9=Flatten()(out8)
out10=Dense(units=11,
kernel_initializer='normal',
kernel_regularizer='l2',
activation='softmax')(out9)
cnn = Model(inputs=input,outputs=out10)
#336C2P for 4040
elif iVersion == 2:
out1=Conv2D(filters=32,
kernel_size=(3,3),
kernel_initializer='he_normal',
weights=None,
padding='valid',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(input)
out2=Conv2D(filters=64,
kernel_size=(3,3),
kernel_initializer='he_normal',
weights=None,
padding='valid',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out1)
out3=Conv2D(filters=128, # learning rate: 0.1 -> 76%
kernel_size=(3,3),
kernel_initializer='he_normal',
weights=None,
padding='valid',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out2)
out4=Conv2D(filters=128, # learning rate: 0.1 -> 76%
kernel_size=(3,3),
kernel_initializer='he_normal',
weights=None,
padding='valid',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out3)
out4=pool2(pool_size=(2,2),data_format='channels_first')(out4)
out5=Conv2D(filters=256,
kernel_size=(3,3),
kernel_initializer='he_normal',
weights=None,
padding='valid',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out4)
out6=Conv2D(filters=256, # learning rate: 0.1 -> 76%
kernel_size=(3,3),
kernel_initializer='he_normal',
weights=None,
padding='valid',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out5)
out6=pool2(pool_size=(2,2),data_format='channels_first')(out6)
out7=Flatten()(out6)
out10=Dense(units=11,
kernel_initializer='normal',
kernel_regularizer='l2',
activation='softmax')(out7)
cnn = Model(inputs=input,outputs=out10)
#temp/googlenet764
elif iVersion == 3:
out1=Conv2D(filters=64,
kernel_size=(3,3),
kernel_initializer='he_normal',
weights=None,
padding='valid',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(input)
out2=Conv2D(filters=64,
kernel_size=(3,3),
kernel_initializer='he_normal',
weights=None,
padding='valid',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out1)
out2=pool2(pool_size=(2,2),data_format='channels_first')(out2)
out3=Conv2D(filters=128, # learning rate: 0.1 -> 76%
kernel_size=(3,3),
kernel_initializer='he_normal',
weights=None,
padding='valid',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out2)
out4=Conv2D(filters=128, # learning rate: 0.1 -> 76%
kernel_size=(3,3),
kernel_initializer='he_normal',
weights=None,
padding='valid',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out3)
out4=pool2(pool_size=(2,2),data_format='channels_first')(out4)
out5_1=Conv2D(filters=32,
kernel_size=(1,1),
kernel_initializer='he_normal',
weights=None,
padding='same',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out4)
out5_2=Conv2D(filters=32, # learning rate: 0.1 -> 76%
kernel_size=(1,1),
kernel_initializer='he_normal',
weights=None,
padding='same',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out4)
out5_2=Conv2D(filters=128, # learning rate: 0.1 -> 76%
kernel_size=(3,3),
kernel_initializer='he_normal',
weights=None,
padding='same',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out5_2)
out5_3=Conv2D(filters=32, # learning rate: 0.1 -> 76%
kernel_size=(1,1),
kernel_initializer='he_normal',
weights=None,
padding='same',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out4)
out5_3=Conv2D(filters=128, # learning rate: 0.1 -> 76%
kernel_size=(5,5),
kernel_initializer='he_normal',
weights=None,
padding='same',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out5_3)
out5_4=pool2(pool_size=(3,3),strides=(1,1),padding='same',data_format='channels_first')(out4)
out5_4=Conv2D(filters=128, # learning rate: 0.1 -> 76%
kernel_size=(1,1),
kernel_initializer='he_normal',
weights=None,
padding='same',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out5_4)
out5=concatenate(inputs=[out5_1,out5_2,out5_3],axis=1)
out7=Conv2D(filters=256, # learning rate: 0.1 -> 76%
kernel_size=(3,3),
kernel_initializer='he_normal',
weights=None,
padding='valid',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out5)
#out7=pool2(pool_size=(2,2),data_format='channels_first')(out7)
out8=Conv2D(filters=256, # learning rate: 0.1 -> 76%
kernel_size=(3,3),
kernel_initializer='he_normal',
weights=None,
padding='valid',
strides=(1, 1),
kernel_regularizer=l2(1e-6),
activation='relu')(out7)
out8=pool2(pool_size=(2,2),data_format='channels_first')(out8)
out9=Flatten()(out8)
out10=Dense(units=11,
kernel_initializer='normal',
kernel_regularizer='l2',
activation='softmax')(out9)
cnn = Model(inputs=input,outputs=out10)
#temp/resgooglenet764 wrong!!!
elif iVersion == 4:
out1=Conv2D(filters=64,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='valid',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(input)
out2=Conv2D(filters=64,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='valid',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out1)
out2=pool2(pool_size=(2,2),data_format='channels_first')(out2)
out3=Conv2D(filters=128,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out2)
out4=Conv2D(filters=128,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out3)
out4=add([out2,out4])
out4=pool2(pool_size=(2,2),data_format='channels_first')(out4)
out5_1=Conv2D(filters=32,kernel_size=(1,1),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out4)
out5_2=Conv2D(filters=32,kernel_size=(1,1),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out4)
out5_2=Conv2D(filters=128,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out5_2)
out5_3=Conv2D(filters=32,kernel_size=(1,1),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out4)
out5_3=Conv2D(filters=128,kernel_size=(5,5),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out5_3)
out5_4=pool2(pool_size=(3,3),strides=(1,1),padding='same',data_format='channels_first')(out4)
out5_4=Conv2D(filters=128,kernel_size=(1,1),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out5_4)
out5=concatenate(inputs=[out5_1,out5_2,out5_3],axis=1)
# out6_1=Conv2D(filters=32,kernel_initializer='he_normal',weights=None,padding='valid',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out5)
#
# out6_2=Conv2D(filters=32,kernel_size=(1,1),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out5)
# out6_2=Conv2D(filters=128,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out6_2)
#
# out6_3=Conv2D(filters=32,kernel_size=(1,1),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out5)
# out6_3=Conv2D(filters=128,kernel_size=(5,5),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out6_3)
#
# out6_4=pool2(pool_size=(3,3),strides=(1,1),padding='same',data_format='channels_first')(out5)
# out6_4=Conv2D(filters=128,kernel_size=(1,1),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out6_4)
#
# out6=concatenate(inputs=[out6_1,out6_2,out6_3,out6_4],axis=1)
out7=Conv2D(filters=256,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),
activation='relu')(out5)
out8=Conv2D(filters=256,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),
activation='relu')(out7)
out8=add([out5, out8])
out8=pool2(pool_size=(2,2),data_format='channels_first')(out8)
out9=Flatten()(out8)
out10=Dense(units=11,
kernel_initializer='normal',
kernel_regularizer='l2',
activation='softmax')(out9)
cnn = Model(inputs=input,outputs=out10)
#temp/resgooglenet764_v1
elif iVersion == 5:
out1=Conv2D(filters=32,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='valid',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(input)
out2=Conv2D(filters=64,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='valid',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out1)
out2=pool2(pool_size=(2,2),data_format='channels_first')(out2)
out3=Conv2D(filters=64,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out2)
out4=Conv2D(filters=64,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out3)
out4=add([out2,out4])
out4=pool2(pool_size=(2,2),data_format='channels_first')(out4)
out_3=Conv2D(filters=128,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out4)
out_4=Conv2D(filters=128,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out_3)
out5_1=Conv2D(filters=64,kernel_size=(1,1),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out_4)
out5_2=Conv2D(filters=32,kernel_size=(1,1),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out_4)
out5_2=Conv2D(filters=128,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out5_2)
out5_3=Conv2D(filters=64,kernel_size=(1,1),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out_4)
out5_3=Conv2D(filters=128,kernel_size=(5,5),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out5_3)
out5_4=pool2(pool_size=(3,3),strides=(1,1),padding='same',data_format='channels_first')(out4)
out5_4=Conv2D(filters=128,kernel_size=(1,1),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),activation='relu')(out5_4)
out5=concatenate(inputs=[out5_1,out5_2,out5_3],axis=1)
sout6=Conv2D(filters=256,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),
activation='relu')(out5)
out7=Conv2D(filters=256,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),
activation='relu')(out5)
out8=Conv2D(filters=256,kernel_size=(3,3),kernel_initializer='he_normal',weights=None,padding='same',strides=(1, 1),kernel_regularizer=l2(1e-6),
activation='relu')(out7)
out8=add([sout6, out8])
out8=pool2(pool_size=(2,2),data_format='channels_first')(out8)
out9=Flatten()(out8)
out10=Dense(units=11,
kernel_initializer='normal',
kernel_regularizer='l2',
activation='softmax')(out9)
cnn = Model(inputs=input,outputs=out10)
return cnn
