def on_train_begin(self, logs={}):
self.nlayerinput = lambda x: K.function([self.model.layers[0].input], [self.kdelayer.input])([x])[0]
N, dims = self.entropy_train_data.shape
Kdists = K.placeholder(ndim=2)
Klogvar = K.placeholder(ndim=0)
def obj(logvar, dists):
#print 'here', logvar # lossfunc([dists, logvar[0]])[0]
return lossfunc([dists, logvar.flat[0]])[0]
def jac(logvar, dists):
#print logvar, lossfunc([dists, logvar[0]]), jacfunc([dists, logvar[0]])
return np.atleast_2d(np.array(jacfunc([dists, logvar.flat[0]])))[0]
lossfunc = K.function([Kdists, Klogvar,], [kde_entropy_from_dists_loo(Kdists, N, dims, K.exp(Klogvar))])
jacfunc = K.function([Kdists, Klogvar,], K.gradients(kde_entropy_from_dists_loo(Kdists, N, dims, K.exp(Klogvar)), Klogvar))
self.obj =obj # lambda logvar, dists: np.array([lossfunc([dists, logvar[0]]),]) # [0]
self.jac =jac # lambda logvar, dists: jacfunc([dists, np.array([logvar]).flat[0]])[0]
def on_train_begin(self, logs={}):
modelobj = self.model.model
inputs = modelobj.inputs + modelobj.targets + modelobj.sample_weights + [ K.learning_phase(),]
lossfunc = K.function(inputs, [modelobj.total_loss])
jacfunc = K.function(inputs, K.gradients(modelobj.total_loss, self.noiselayer.logvar))
sampleweights = np.ones(len(self.traindata.X))
def obj(logvar):
v = K.get_value(self.noiselayer.logvar)
K.set_value(self.noiselayer.logvar, logvar.flat[0])
r = lossfunc([self.traindata.X, self.traindata.Y, sampleweights, 1])[0]
K.set_value(self.noiselayer.logvar, v)
return r
def jac(logvar):
v = K.get_value(self.noiselayer.logvar)
K.set_value(self.noiselayer.logvar, logvar.flat[0])
r = np.atleast_2d(np.array(jacfunc([self.traindata.X, self.traindata.Y, sampleweights, 1])))[0]
K.set_value(self.noiselayer.logvar, v)
return r
self.obj = obj # lambda logvar: lossfunc([self.traindata.X_train, self.traindata.Y_train, self.sampleweights, logvar[0], 1])[0]
self.jac = jac # lambda logvar: np.array(jacfunc([self.traindata.X_train, self.traindata.Y_train, self.sampleweights, logvar[0], 1]))