dFLOa = FeatureLayerOut.Grad(dAL[:3], t)
FeatureLayerIn.Grad(dFLOa, t)
#Unreavale the context layer with respect to the analysis input to compute the gradient.
for ts in reversed(range(len(data[str(i)]['context']))):
dCLO = ContextOut.Grad(dAL[3:], ts)
dCLI = ContextIn.Grad(dCLO, ts)
#Get new update reacted to Time Sub with respect to both the context input and the Analysis result
#Add these gradients to the existing calculated values generated at the Analysis stage.
dFLOc = FeatureLayerOut.Grad(dCLI, ts)
FeatureLayerIn.Grad(dFLOc, ts)
#if we are on a Display epoch display the data give for each analysis, the result, and the target
# for what it should have been
if epoch % display == 0:
print(str(data[str(i)]['analysis'][t]) + " " + str(sample[t]) + " " + str(data[str(i)]['target'][t]))
#update the layers for this Data Block
FeatureLayerIn.update(learning_rate=0.1)
FeatureLayerOut.update(learning_rate=.1)
ContextIn.update(learning_rate=.1)
ContextOut.update(learning_rate=.1)
AnalysisLayer.update(learning_rate=.1)
#add an epoch number to help with display
if epoch % display == 0:
print("Epoch: " + str(epoch))
print()
epoch += 1
