76 lines
2.3 KiB
Python
76 lines
2.3 KiB
Python
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import tensorflow.compat.v1 as tf
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tf.disable_v2_behavior()
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import grapa as g
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from grapa.functionals import *
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from grapa.layers import *
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from grapa.constants import *
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keras=tf.keras
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K=keras.backend
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from data import adj
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A,X=adj()
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data=np.concatenate((X,A),axis=2)
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def createmodel():
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i=Input(shape=data.shape[1:])#define your input
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gs=int(data.shape[1])#find the number of initial nodes
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param=int(data.shape[2])-gs#find the number of features per node
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g=grap(state(gs=gs,param=param))#create a grap object for use in the functional api
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g.X,g.A=gcutparam(gs=gs,param1=param,param2=gs)([i])#set the current feature vector and adjacency matrix of your grap object by cutting the input
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m=getm()#get the standart constant file
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xx=g.X#save this value, since it is the initial comparison variable
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g=gnl(g,m)#a single graph update step
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oparam=g.s.param#save the number of parameters, to assert that after the ae step this number is still the same
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#g=compress(g,m,5,12)#execute one compression step, reducing gs nodes into gs/5 nodes and adding 12 features to each node
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#g=gll(g,m,k=4)#a new graph update step. Here gll means relearning of your graph using a topk (k=4) algorithm
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#m.decompress="paramlike"#choose paramlike decompression
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#g,com,i2=decompress(g,m,5)#decompress by a factor of 5 (gs/5 nodes -> gs nodes)
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g=gnl(g,m)#a final graph update step, using the graph generated in the decompression step
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g=remparam(g,oparam)#remove to many parameters again
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return i,g
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return handlereturn(i,xx,com,i2,g.X,False)#this function uses compression input, initial comparison, compressed state,decompression input,decompressed version, run as a variational autoencoder
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def prepare():
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i,g=createmodel()#returns z1,z2, which are the same since we dont use a vae,
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model=Model(i,g)
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plot_model(model,to_file="model.png",show_shapes=True)#save model plots
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loss=mse(g,K.ones_like(g))#loss is mse
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loss=K.mean(loss)
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#if shallvae:
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# kl_loss=-0.5*K.mean(1+z2-K.square(z1)-K.exp(z2))
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# loss+=kl_loss
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model.add_loss(loss)
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model.compile(Adam(lr=lr))#we use the adam optimizer
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model.summary()
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#plot_model(vae,to_file=f"{nam}.png",show_shapes=True)
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return model
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if __name__ == '__main__':
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model=prepare()
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