117 lines
2.2 KiB
Python
117 lines
2.2 KiB
Python
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# TensorFlow and tf.keras
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#import tensorflow as tf
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import keras
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# Helper libraries
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import numpy as np
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import matplotlib.pyplot as plt
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import math
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import time
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from data import loadfile
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from sklearn.metrics import roc_curve, auc
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latent=5
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def cauc(n,a):
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p=np.concatenate((n,a),axis=0)
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l=np.concatenate((np.zeros(len(n)),np.ones(len(a))),axis=0)
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fpr, tpr, threshold = roc_curve(l, p)
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auc_score=auc(fpr,tpr)
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return auc_score
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def train(x,y,pth,typ,retry=0):
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x=x.astype("float32")
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y=y.astype("float32")
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#x-=np.mean(x,axis=0)#no correcting here
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#x/=(np.std(x,axis=0)+0.00001)
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train=x[np.where(y[:,0]==0)]
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test_a=x[np.where(y[:,0]==1)]
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test_n=train[:len(test_a)]
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train=train[len(test_a):]
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#global dd
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#dd=train
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#exit()
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dim=x.shape[1]
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#denses=[0.8,0.6,0.5,0.6,0.8,]
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#denses=[int(math.ceil(zw*dim)) for zw in denses]
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d1=(latent*dim*dim)**(1/3)
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d2=(latent*latent*dim)**(1/3)
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d1=int(d1)
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d2=int(d2)
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denses=[d1,d2,latent,d2,d1]
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#print(train.shape)
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model = keras.Sequential([
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keras.layers.Dense(dim,activation='relu',input_shape=train.shape[1:]),
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*[keras.layers.Dense(dense, activation='relu') for dense in denses],
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keras.layers.Dense(dim,activation="linear")
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])
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model.compile(optimizer='adam',
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loss="mse",
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metrics=[])
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model.summary()
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model.fit(train,
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train,
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epochs=25,
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validation_split=0.2,
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callbacks=[
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#keras.callbacks.CSVLogger(pth+"history.csv"),
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#keras.callbacks.ModelCheckpoint(pth+"epoch{epoch:02d}.tf",
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# save_weights_only=False)
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])
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#model.save(pth+"whole.tf")
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#model.save_weights(pth+"weigths.tf")
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lss = model.evaluate(train,train, verbose=2)
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#if lss>500 and retry<3:
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# print("retrying")
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# return train_func(x,y,pth,typ,retry=retry+1)
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p=model.predict(train)
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pn=model.predict(test_n)
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pa=model.predict(test_a)
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mp=np.mean(p,axis=0)
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dn=np.mean((pn-mp)**2,axis=1)
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da=np.mean((pa-mp)**2,axis=1)
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auc=cauc(dn,da)
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print(f"auc={auc}")
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if __name__ == '__main__':
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x,y=loadfile()
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train(x,y,".","")
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