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CTRW_alpha_training.py
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import numpy as np
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense,BatchNormalization,Conv1D
from tensorflow.keras.layers import Input,GlobalMaxPooling1D,concatenate
from tensorflow.keras.optimizers import Adam
from utils import generate_CTRW
from tensorflow.keras.callbacks import EarlyStopping,ReduceLROnPlateau,ModelCheckpoint
batchsize = 32
T = np.arange(19,21,0.1) # this provides another layer of stochasticity to make the network more robust
steps = 300 # number of steps to generate
initializer = 'he_normal'
f = 32 #number of filters
sigma = 0 #noise variance
inputs = Input((steps-1,1))
x1 = Conv1D(f,4,padding='causal',activation='relu',kernel_initializer=initializer)(inputs)
x1 = BatchNormalization()(x1)
x1 = Conv1D(f,4,dilation_rate=2,padding='causal',activation='relu',kernel_initializer=initializer)(x1)
x1 = BatchNormalization()(x1)
x1 = Conv1D(f,4,dilation_rate=4,padding='causal',activation='relu',kernel_initializer=initializer)(x1)
x1 = BatchNormalization()(x1)
x1 = GlobalMaxPooling1D()(x1)
x2 = Conv1D(f,2,padding='causal',activation='relu',kernel_initializer=initializer)(inputs)
x2 = BatchNormalization()(x2)
x2 = Conv1D(f,2,dilation_rate=2,padding='causal',activation='relu',kernel_initializer=initializer)(x2)
x2 = BatchNormalization()(x2)
x2 = Conv1D(f,2,dilation_rate=4,padding='causal',activation='relu',kernel_initializer=initializer)(x2)
x2 = BatchNormalization()(x2)
x2 = GlobalMaxPooling1D()(x2)
x3 = Conv1D(f,3,padding='causal',activation='relu',kernel_initializer=initializer)(inputs)
x3 = BatchNormalization()(x3)
x3 = Conv1D(f,3,dilation_rate=2,padding='causal',activation='relu',kernel_initializer=initializer)(x3)
x3 = BatchNormalization()(x3)
x3 = Conv1D(f,3,dilation_rate=4,padding='causal',activation='relu',kernel_initializer=initializer)(x3)
x3 = BatchNormalization()(x3)
x3 = GlobalMaxPooling1D()(x3)
x4 = Conv1D(f,10,padding='causal',activation='relu',kernel_initializer=initializer)(inputs)
x4 = BatchNormalization()(x4)
x4 = Conv1D(f,10,dilation_rate=4,padding='causal',activation='relu',kernel_initializer=initializer)(x4)
x4 = BatchNormalization()(x4)
x4 = Conv1D(f,10,dilation_rate=8,padding='causal',activation='relu',kernel_initializer=initializer)(x4)
x4 = BatchNormalization()(x4)
x4 = GlobalMaxPooling1D()(x4)
x5 = Conv1D(f,20,padding='causal',activation='relu',kernel_initializer=initializer)(inputs)
x5 = BatchNormalization()(x5)
x5 = Conv1D(f,20,dilation_rate=2,padding='causal',activation='relu',kernel_initializer=initializer)(x5)
x5 = BatchNormalization()(x5)
x5 = Conv1D(f,20,dilation_rate=8,padding='causal',activation='relu',kernel_initializer=initializer)(x5)
x5 = BatchNormalization()(x5)
x5 = GlobalMaxPooling1D()(x5)
x6 = Conv1D(f,20,padding='same',activation='relu',kernel_initializer=initializer)(inputs)
x6 = BatchNormalization()(x6)
x6 = GlobalMaxPooling1D()(x6)
con = concatenate([x1,x2,x3,x4,x5,x6])
dense = Dense(512,activation='relu')(con)
dense = Dense(128,activation='relu')(dense)
dense2 = Dense(1,activation='sigmoid')(dense)
model = Model(inputs=inputs, outputs=dense2)
optimizer = Adam(lr=1e-5)
model.compile(optimizer=optimizer,loss='mse',metrics=['mse'])
model.summary()
callbacks = [EarlyStopping(monitor='val_loss',
patience=20,
verbose=1,
min_delta=1e-4),
ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=4,
verbose=1,
min_lr=1e-12),
ModelCheckpoint(filepath='./Models/model-alphaCTRW-estimate_300.h5',
monitor='val_loss',
save_best_only=False,
mode='min',
save_weights_only=False)]
gen = generate_CTRW(batchsize=batchsize,steps=steps,T=T,sigma=sigma)
history = model.fit_generator(generator=gen,
steps_per_epoch=50,
epochs=1000,
verbose=1,
callbacks=callbacks,
validation_data=generate_CTRW(steps=steps,T=T,sigma=sigma),
validation_steps=10)