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NiceModels.py
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NiceModels.py
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def UltraDenseResNeuro(NameShell='26_3',CellNumber=7,X_train=X_train,X_test=X_test,Y_train=Y_train,Y_test=Y_test,usegpu=True):
# When you set cell number is zero, there is still one cell, be careful.
INput=Input(shape=(X_train.shape[1],1,))
conv1=Conv1D(8,15,strides=2,padding='same')(INput)
conv1=Conv1D(16,3,strides=1,padding='same')(conv1)
batc1=BatchNormalization()(conv1)
acti1=Activation('relu')(batc1)
pool1=MaxPooling1D(2)(acti1)
conv2=Conv1D(8,1)(pool1)
batc2=BatchNormalization()(conv2)
acti2=Activation('relu')(batc2)
conv3=Conv1D(16,3,padding='same')(acti2)
adds=[pool1]
addi=Add()(adds+[conv3])
adds.append(addi)
for i in range(CellNumber):
conv2=Conv1D(8,1)(addi)
batc2=BatchNormalization()(conv2)
acti2=Activation('relu')(batc2)
conv3=Conv1D(16,3,padding='same')(acti2)
addi=Add()(adds+[conv3])
adds.append(addi)
batc2=BatchNormalization()(addi)
flat1=keras.layers.Flatten()(batc2)
drop1=Dropout(0.2)(flat1)
dens1=Dense(256,activation='relu')(drop1)
drop2=Dropout(0.2)(dens1)
dens2=Dense(128,activation='relu')(drop2)
dens3=Dense(1,activation='sigmoid')(dens2)
model=Model(inputs=INput,outputs=dens3)
print(model.summary())
if usegpu==True:model=keras.utils.multi_gpu_model(model,gpus=2)
opt=keras.optimizers.adam(lr=0.00003,decay=1e-6)
model.compile(optimizer=opt,loss='mse')
print(model.summary())
history1=model.fit(X_train,Y_train[NameShell],epochs=700,\
validation_data=[X_test,Y_test[NameShell]],batch_size=4000,\
callbacks=[keras.callbacks.EarlyStopping(patience=10)],verbose=2)
opt=keras.optimizers.adam(lr=0.0000003,decay=1e-6)
model.compile(optimizer=opt,loss='mse')
history2=model.fit(X_train,Y_train[NameShell],epochs=700,\
validation_data=[X_test,Y_test[NameShell]],batch_size=10000,\
callbacks=[keras.callbacks.EarlyStopping(patience=5)],verbose=2)
return model,history1,history2
def ConNeuro(NameShell='26_3',CellNumber=7,X_train=X_train,X_test=X_test,Y_train=Y_train,Y_test=Y_test):
INput=Input(shape=(X_train.shape[1],1,))
conv1=Conv1D(8,15,strides=2,padding='same')(INput)
conv1=Conv1D(16,3,strides=1,padding='same')(conv1)
batc1=BatchNormalization()(conv1)
acti1=Activation('relu')(batc1)
pool1=MaxPooling1D(2)(acti1)
conv2=Conv1D(8,1)(pool1)
batc2=BatchNormalization()(conv2)
acti2=Activation('relu')(batc2)
conv3=Conv1D(16,3,padding='same')(acti2)
for i in range(CellNumber):
conv2=Conv1D(8,1)(conv3)
batc2=BatchNormalization()(conv2)
acti2=Activation('relu')(batc2)
conv3=Conv1D(16,3,padding='same')(acti2)
batc2=BatchNormalization()(conv3)
flat1=keras.layers.Flatten()(batc2)
drop1=Dropout(0.2)(flat1)
dens1=Dense(256,activation='relu')(drop1)
drop2=Dropout(0.2)(dens1)
dens2=Dense(128,activation='relu')(drop2)
dens3=Dense(1,activation='sigmoid')(dens2)
model=Model(inputs=INput,outputs=dens3)
print(model.summary())
model=keras.utils.multi_gpu_model(model,gpus=2)
opt=keras.optimizers.adam(lr=0.00003,decay=1e-6)
model.compile(optimizer=opt,loss='mse')
print(model.summary())
history1=model.fit(X_train,Y_train[NameShell],epochs=700,\
validation_data=[X_test,Y_test[NameShell]],batch_size=4000,\
callbacks=[keras.callbacks.EarlyStopping(patience=10)],verbose=2)
opt=keras.optimizers.adam(lr=0.0000003,decay=1e-6)
model.compile(optimizer=opt,loss='mse')
history2=model.fit(X_train,Y_train[NameShell],epochs=700,\
validation_data=[X_test,Y_test[NameShell]],batch_size=10000,\
callbacks=[keras.callbacks.EarlyStopping(patience=5)],verbose=2)
return model,history1,history2
def WiderConNeuro(NameShell='26_3',CellNumber=7,X_train=X_train,X_test=X_test,Y_train=Y_train,Y_test=Y_test):
INput=Input(shape=(X_train.shape[1],1,))
conv1=Conv1D(8,15,strides=2,padding='same')(INput)
conv1=Conv1D(16,3,strides=1,padding='same')(conv1)
batc1=BatchNormalization()(conv1)
acti1=Activation('relu')(batc1)
pool1=MaxPooling1D(2)(acti1)
conv2=Conv1D(16,3)(pool1)
batc2=BatchNormalization()(conv2)
acti2=Activation('relu')(batc2)
conv3=Conv1D(16,3,padding='same')(acti2)
for i in range(CellNumber):
conv2=Conv1D(16,3)(conv3)
batc2=BatchNormalization()(conv2)
acti2=Activation('relu')(batc2)
conv3=Conv1D(16,3,padding='same')(acti2)
batc2=BatchNormalization()(conv3)
flat1=keras.layers.Flatten()(batc2)
drop1=Dropout(0.2)(flat1)
dens1=Dense(256,activation='relu')(drop1)
drop2=Dropout(0.2)(dens1)
dens2=Dense(128,activation='relu')(drop2)
dens3=Dense(1,activation='sigmoid')(dens2)
model=Model(inputs=INput,outputs=dens3)
print(model.summary())
model=keras.utils.multi_gpu_model(model,gpus=2)
opt=keras.optimizers.adam(lr=0.00003,decay=1e-6)
model.compile(optimizer=opt,loss='mse')
print(model.summary())
history1=model.fit(X_train,Y_train[NameShell],epochs=700,\
validation_data=[X_test,Y_test[NameShell]],batch_size=4000,\
callbacks=[keras.callbacks.EarlyStopping(patience=10)],verbose=2)
opt=keras.optimizers.adam(lr=0.0000003,decay=1e-6)
model.compile(optimizer=opt,loss='mse')
history2=model.fit(X_train,Y_train[NameShell],epochs=700,\
validation_data=[X_test,Y_test[NameShell]],batch_size=10000,\
callbacks=[keras.callbacks.EarlyStopping(patience=5)],verbose=2)
return model,history1,history2
def UltraDenseConcatNeuro(NameShell='26_3',CellNumber=7,\
X_train=X_train,X_test=X_test,Y_train=Y_train,Y_test=Y_test,usegpu=True):
INput=Input(shape=(X_train.shape[1],1,))
conv1=Conv1D(8,15,strides=2,padding='same')(INput)
conv1=Conv1D(16,3,strides=1,padding='same')(conv1)
batc1=BatchNormalization()(conv1)
acti1=Activation('relu')(batc1)
pool1=MaxPooling1D(2)(acti1)
conv2=Conv1D(8,1)(pool1)
batc2=BatchNormalization()(conv2)
acti2=Activation('relu')(batc2)
conv3=Conv1D(16,3,padding='same')(acti2)
adds=[pool1]
addi=keras.layers.Concatenate()(adds+[conv3])
adds.append(addi)
for i in range(CellNumber):
conv2=Conv1D(8,1)(addi)
batc2=BatchNormalization()(conv2)
acti2=Activation('relu')(batc2)
conv3=Conv1D(16,3,padding='same')(acti2)
addi=keras.layers.Concatenate()(adds+[conv3])
adds.append(addi)
batc2=BatchNormalization()(addi)
flat1=keras.layers.Flatten()(batc2)
drop1=Dropout(0.2)(flat1)
dens1=Dense(256,activation='relu')(drop1)
drop2=Dropout(0.2)(dens1)
dens2=Dense(128,activation='relu')(drop2)
dens3=Dense(1,activation='sigmoid')(dens2)
model=Model(inputs=INput,outputs=dens3)
print(model.summary())
if usegpu==True:model=keras.utils.multi_gpu_model(model,gpus=2)
opt=keras.optimizers.adam(lr=0.00003,decay=1e-6)
model.compile(optimizer=opt,loss='mse')
print(model.summary())
history1=model.fit(X_train,Y_train[NameShell],epochs=700,\
validation_data=[X_test,Y_test[NameShell]],batch_size=4000,\
callbacks=[keras.callbacks.EarlyStopping(patience=10)],verbose=2)
opt=keras.optimizers.adam(lr=0.0000003,decay=1e-6)
model.compile(optimizer=opt,loss='mse')
history2=model.fit(X_train,Y_train[NameShell],epochs=700,\
validation_data=[X_test,Y_test[NameShell]],batch_size=10000,\
callbacks=[keras.callbacks.EarlyStopping(patience=5)],verbose=2)
return model,history1,history2