82 lines
2.2 KiB
Python
82 lines
2.2 KiB
Python
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import numpy as np
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import tensorflow as tf
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from tensorflow import keras
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from tensorflow.keras import backend as K
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from model import gen_model
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from sklearn.metrics import roc_auc_score
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from time import time
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def with_evaluation_cost(func):
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def wrapper(*args, **kwargs):
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t0=time()
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dic = func(*args, **kwargs)
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t1=time()
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dic["evaluation_cost"]=t1-t0
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return dic
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return wrapper
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@with_evaluation_cost
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def train_one(x,tx,ty,seed=0,lr_epoch_modulo=10,lr_factor=0.8,lr_minima=1e-4,initial_lr=0.001,epochs=1000,batch_size=30,shall_early=True,patience=10,*args,**kwargs):
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np.random.seed(seed)
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tf.random.set_seed(seed)
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model=gen_model(int(x.shape[1]),*args,**kwargs)
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model.compile(optimizer=keras.optimizers.Adam(initial_lr))
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def shedule(epoch,lr):
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if epoch%lr_epoch_modulo==0 and epoch>0 and lr>lr_minima:
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return lr*lr_factor
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return lr
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callbacks=[]
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if shall_early:callbacks.append(keras.callbacks.EarlyStopping(patience=patience,restore_best_weights=True))
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callbacks.append(keras.callbacks.LearningRateScheduler(shedule))
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callbacks.append(keras.callbacks.TerminateOnNaN())
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model.fit(x,None,
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epochs=epochs,
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batch_size=batch_size,
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validation_split=0.2,
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callbacks=callbacks)
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p=model.predict(tx)
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try:
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loss=model.evaluate(x)
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auc=roc_auc_score(ty,-p)
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except:
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loss=1000000000.0
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auc=-1.0
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return {"loss":loss,"auc":auc}
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def dic_mean(dic):
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ret={k:np.mean([d[k] for d in dic]) for k in dic[0]}
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ret["min_loss"]=np.min([d["loss"] for d in dic])
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ret["opt_auc"]=np.mean([d["auc"] for d in dic if d["loss"]==ret["min_loss"]])
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return ret
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@with_evaluation_cost
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def train_many(x,tx,ty,count=10,*args,**kwargs):
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dics=[]
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for seed in range(count):
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dics.append(train_one(x,tx,ty,seed=seed,*args,**kwargs))
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if dics[-1]["auc"]<0.0:
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break
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return dic_mean(dics)
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if __name__=="__main__":
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f=np.load("cardio.npz")
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x,tx,ty=f["x"],f["tx"],f["ty"]
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print(train_one(x,tx,ty,epochs=1000,shall_early=True,patience=10,lr_epoch_modulo=10,lr_factor=0.8,lr_minima=1e-4,initial_lr=0.001,batch_size=30,splits=1))
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