trainer = RPropMinusTrainer(net, dataset=ds)
train_errors = [] # save errors for plotting later
EPOCHS_PER_CYCLE = 5
CYCLES = 100
EPOCHS = EPOCHS_PER_CYCLE * CYCLES
for i in xrange(CYCLES):
trainer.trainEpochs(EPOCHS_PER_CYCLE)
train_errors.append(trainer.testOnData())
epoch = (i+1) * EPOCHS_PER_CYCLE
print("epoch: "+str(epoch) + "/" + str(EPOCHS))
stdout.flush()
print()
print("final error =", train_errors[-1])
#Plot the errors (note that in this simple toy example, we are testing and training on the same dataset,
#which is of course not what you'd do for a real project!):
import matplotlib.pyplot as plt
plt.plot(range(0, EPOCHS, EPOCHS_PER_CYCLE), train_errors)
plt.xlabel('Epoch')
plt.ylabel('Error')
plt.show()
#Now ask the network to predict the next sample:
for sample, target in ds.getSequenceIterator(0):
print(" sample = %4.1f" % sample)
print("predicted next sample = %4.1f" % net.activate(sample))
print(" actual next sample = %4.1f" % target)
print()
