From c2fe311e6b3ce615c086b600714af49913f91ffc Mon Sep 17 00:00:00 2001 From: Simon Kluettermann Date: Mon, 21 Nov 2022 11:02:58 +0100 Subject: [PATCH] initial push --- model.py | 44 ++++++++++++++++++++++++++++++ train.py | 81 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 125 insertions(+) create mode 100644 model.py create mode 100644 train.py diff --git a/model.py b/model.py new file mode 100644 index 0000000..91c7d22 --- /dev/null +++ b/model.py @@ -0,0 +1,44 @@ +import numpy as np + +import tensorflow as tf +from tensorflow import keras +from tensorflow.keras import backend as K + + +from disttf import GaussianRealisation,BiasedRealisation,BoxRealisation +from disttf import MixtureLayer, MotioLayer, ScaleLayer, SplitLayer +from disttf import NonLinearityLayer, SeperateLayer, RecombineLayer + + +def gen_model(inputs, splits=10, realisation="gauss", mixture=0, nonlin=False): + + i=keras.layers.Input(shape=(inputs,)) + q=i + v=tf.constant(1.0,dtype=tf.float32) + + if splits>1: + q,v=SplitLayer(splits)([q,v]) + q,v=MotioLayer()([q,v]) + q,v=ScaleLayer()([q,v]) + for j in range(mixture): + q=MixtureLayer()(q) + if nonlin: + q=NonLinearityLayer()(q) + if realisation=="gauss": + q=GaussianRealisation()([q,v]) + elif realisation=="biased": + q=BiasedRealisation()([q,v]) + elif realisation=="box": + q=BoxRealisation()([q,v]) + else: + raise ValueError("Unknown realisation type: "+realisation) + + model=keras.Model(inputs=i,outputs=q) + + loss=K.log(K.abs(q)+1e-6) + loss=-K.mean(loss) + model.add_loss(loss) + + return model + + diff --git a/train.py b/train.py new file mode 100644 index 0000000..ca827ac --- /dev/null +++ b/train.py @@ -0,0 +1,81 @@ +import numpy as np + +import tensorflow as tf +from tensorflow import keras +from tensorflow.keras import backend as K + +from model import gen_model + +from sklearn.metrics import roc_auc_score + +from time import time + + +def with_evaluation_cost(func): + def wrapper(*args, **kwargs): + t0=time() + dic = func(*args, **kwargs) + t1=time() + dic["evaluation_cost"]=t1-t0 + return dic + return wrapper + + +@with_evaluation_cost +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): + + np.random.seed(seed) + tf.random.set_seed(seed) + + model=gen_model(int(x.shape[1]),*args,**kwargs) + model.compile(optimizer=keras.optimizers.Adam(initial_lr)) + + + def shedule(epoch,lr): + if epoch%lr_epoch_modulo==0 and epoch>0 and lr>lr_minima: + return lr*lr_factor + return lr + + + callbacks=[] + if shall_early:callbacks.append(keras.callbacks.EarlyStopping(patience=patience,restore_best_weights=True)) + callbacks.append(keras.callbacks.LearningRateScheduler(shedule)) + callbacks.append(keras.callbacks.TerminateOnNaN()) + + model.fit(x,None, + epochs=epochs, + batch_size=batch_size, + validation_split=0.2, + callbacks=callbacks) + + p=model.predict(tx) + try: + loss=model.evaluate(x) + auc=roc_auc_score(ty,-p) + except: + loss=1000000000.0 + auc=-1.0 + return {"loss":loss,"auc":auc} + +def dic_mean(dic): + ret={k:np.mean([d[k] for d in dic]) for k in dic[0]} + ret["min_loss"]=np.min([d["loss"] for d in dic]) + ret["opt_auc"]=np.mean([d["auc"] for d in dic if d["loss"]==ret["min_loss"]]) + return ret + +@with_evaluation_cost +def train_many(x,tx,ty,count=10,*args,**kwargs): + + dics=[] + for seed in range(count): + dics.append(train_one(x,tx,ty,seed=seed,*args,**kwargs)) + if dics[-1]["auc"]<0.0: + break + + return dic_mean(dics) + +if __name__=="__main__": + f=np.load("cardio.npz") + x,tx,ty=f["x"],f["tx"],f["ty"] + 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)) +