def td_update_network(model, evals_list, lam=.7):
"""
Updates the model weights to match the temporal difference loss
"""
game_lens = [len(el[1]) for el in evals_list]
total_positions = sum(game_lens)
all_errors = np.zeros(total_positions)
cur_pos = 0
all_reps = np.zeros((total_positions, na.num_features))
for evals, pos_reps in evals_list:
# print evals,pos_reps
#how many moves were acutally played
num_moves = len(pos_reps)
#get error signals
all_errors[cur_pos:cur_pos+num_moves] = td_errors(evals[:num_moves], lam=lam)
for pos_rep in pos_reps:
all_reps[cur_pos] = np.fromstring(pos_rep, sep=",")
cur_pos += 1
training_dict = na.make_training_dict(all_reps, all_errors)
current_evals = model.predict(training_dict)["out"]
n_pos = len(all_errors)
print "l1 loss "+str(np.sum(np.abs(all_errors))/n_pos/50.)
print "l2 loss " + str(np.sum(all_errors**2)/n_pos/2500.)
#I cannot directly back-propigate an arbitrary error signal
#I can, however force the model to fit features to outputs
#So, I obtain the model's current output for the inputs in consideration
#Next, I add the desired errors, and voila, it works!
#This is inefficient, but I'm not in the mood to extend Keras
#Tee-hee-heee
training_dict["out"] += np.ravel(current_evals)
#print training_dict["out"].shape, all_errors.shape, all_errors
model.train_on_batch(training_dict)
def td_update_network(model, evals_list, lam=.7):
"""
Updates the model weights to match the temporal difference loss
"""
game_lens = [len(el[1]) for el in evals_list]
total_positions = sum(game_lens)
all_errors = np.zeros(total_positions)
cur_pos = 0
all_reps = np.zeros((total_positions, na.num_features))
for evals, pos_reps in evals_list:
# print evals,pos_reps
#how many moves were acutally played
num_moves = len(pos_reps)
#get error signals
all_errors[cur_pos:cur_pos + num_moves] = td_errors(evals[:num_moves],
lam=lam)
for pos_rep in pos_reps:
all_reps[cur_pos] = np.fromstring(pos_rep, sep=",")
cur_pos += 1
training_dict = na.make_training_dict(all_reps, all_errors)
current_evals = model.predict(training_dict)["out"]
n_pos = len(all_errors)
print "l1 loss " + str(np.sum(np.abs(all_errors)) / n_pos / 50.)
print "l2 loss " + str(np.sum(all_errors**2) / n_pos / 2500.)
#I cannot directly back-propigate an arbitrary error signal
#I can, however force the model to fit features to outputs
#So, I obtain the model's current output for the inputs in consideration
#Next, I add the desired errors, and voila, it works!
#This is inefficient, but I'm not in the mood to extend Keras
#Tee-hee-heee
training_dict["out"] += np.ravel(current_evals)
#print training_dict["out"].shape, all_errors.shape, all_errors
model.train_on_batch(training_dict)
def propigate_errors(model, reps, errors):
training_dict = na.make_training_dict(reps, errors)
current_evals = model.predict(training_dict)["out"]
n_pos = len(errors)
print "l1 loss "+str(np.sum(np.abs(errors))/n_pos/50.)
print "l2 loss " + str(np.sum(errors**2)/n_pos/2500.)
#I cannot directly back-propigate an arbitrary error signal
#I can, however force the model to fit features to outputs
#So, I obtain the model's current output for the inputs in consideration
#Next, I add the desired errors, and voila, it works!
#This is inefficient, but I'm not in the mood to extend Keras
#Tee-hee-heee
training_dict["out"] += np.ravel(current_evals)
#print training_dict["out"].shape, all_errors.shape, all_errors
model.train_on_batch(training_dict)
def propigate_errors(model, reps, errors):
training_dict = na.make_training_dict(reps, errors)
current_evals = model.predict(training_dict)["out"]
n_pos = len(errors)
print "l1 loss " + str(np.sum(np.abs(errors)) / n_pos / 50.)
print "l2 loss " + str(np.sum(errors**2) / n_pos / 2500.)
#I cannot directly back-propigate an arbitrary error signal
#I can, however force the model to fit features to outputs
#So, I obtain the model's current output for the inputs in consideration
#Next, I add the desired errors, and voila, it works!
#This is inefficient, but I'm not in the mood to extend Keras
#Tee-hee-heee
training_dict["out"] += np.ravel(current_evals)
#print training_dict["out"].shape, all_errors.shape, all_errors
model.train_on_batch(training_dict)
