def train(self):
"""
Trains LSTM.
Randomly samples a schedule and timestep within that schedule, produces training data using x_i - x_j
and trains upon that.
:return:
"""
timesteps = None
training_done = False
loss_func = AlphaLoss()
which_schedule = None
while not training_done:
# sample a timestep before the cutoff for cross_validation
# Quick Fix
found_a_suitable_candidate = False
while not found_a_suitable_candidate:
rand_timestep_within_sched = np.random.randint(len(self.X))
which_schedule = find_which_schedule_this_belongs_to(
self.schedule_array, rand_timestep_within_sched)
if rand_timestep_within_sched + 2 > self.schedule_array[
which_schedule][1]:
pass
else:
found_a_suitable_candidate = True
timesteps = [
rand_timestep_within_sched,
rand_timestep_within_sched + 1,
rand_timestep_within_sched + 2
]
self.model.reinitialize_hidden_to_random()
load_in_embedding_bnn(self.model, self.embedding_list,
which_schedule)
for timestep in timesteps:
truth = self.Y[timestep]
previous_hidden_state = tuple(
[t.detach().cuda() for t in self.model.hidden])
input_nn = self.X[timestep]
truth_nn = self.Y[timestep]
if torch.cuda.is_available():
input_nn = Variable(
torch.Tensor(np.asarray(input_nn).reshape(
1,
242)).cuda()) # change to 5 to increase batch size
P = Variable(torch.Tensor(np.ones((1, 20)))).cuda()
P *= self.distribution_epsilon
P[0][truth_nn] = 1 - 19 * self.distribution_epsilon
truth = Variable(
torch.Tensor(
np.asarray(truth_nn).reshape(1)).cuda().long())
else:
input_nn = Variable(
torch.Tensor(np.asarray(input_nn).reshape(1, 242)))
P = Variable(
torch.Tensor(
np.ones((1, 20) * self.distribution_epsilon)))
P[0][truth_nn] = 1 - 19 * self.distribution_epsilon
truth = Variable(
torch.Tensor(np.asarray(truth_nn).reshape(1)).long())
self.opt.zero_grad()
output = self.model.forward(input_nn, previous_hidden_state)
loss = loss_func.forward(P, output, self.alpha)
if loss.item() < .05 or loss.item() > 5:
pass
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(),
0.5)
self.opt.step()
self.total_loss_array.append(loss.item())
self.embedding_list = store_embedding_back_bnn(
self.model, self.embedding_list, which_schedule)
self.total_iterations += 1
if self.total_iterations > 25 and self.total_iterations % 50 == 1:
print('total iterations is', self.total_iterations)
print('total loss (average for each 40, averaged)',
np.mean(self.total_loss_array[-40:]))
if self.total_iterations > 0 and self.total_iterations % self.when_to_save == self.when_to_save - 1:
self.save_trained_nets('lstm_small' + str(self.num_schedules))
if self.total_iterations > 11000 and np.mean(
self.total_loss_array[-100:]) - np.mean(
self.total_loss_array[-500:]
) < self.convergence_epsilon:
training_done = True
def evaluate_on_test_data(self, load_in_model=False):
"""
Evaluate performance of a trained network tuned upon the alpha divergence loss.
Note this function is called after training convergence
:return:
"""
num_schedules = 75
loss_func = AlphaLoss()
# load in new data
load_directory = '/home/ghost/PycharmProjects/bayesian_prolo/scheduling_env/datasets/test/' + str(
num_schedules) + '_inf_hetero_deadline_naive.pkl'
data = pickle.load(open(load_directory, "rb"))
X, Y, schedule_array = create_new_dataset(num_schedules=num_schedules,
data=data)
for i, each_element in enumerate(X):
X[i] = each_element + list(range(20))
prediction_accuracy = [0, 0]
percentage_accuracy_top1 = []
percentage_accuracy_top3 = []
embedding_optimizer = torch.optim.Adam(
self.model.EmbeddingList.parameters(), lr=.001)
embedding_list = [
torch.ones(1, 8) * 1 / 3 for i in range(num_schedules)
]
if load_in_model: # TODO: somehow get the string when the update_model flag is true
self.model.load_state_dict(
torch.load(
'/home/ghost/PycharmProjects/bayesian_prolo/saved_models/pairwise_saved_models/NN_homog.tar'
)['nn_state_dict'])
for i, schedule in enumerate(schedule_array):
self.model.reinitialize_hidden_to_random()
load_in_embedding_bnn(self.model, embedding_list, i)
for count in range(schedule[0], schedule[1] + 1):
previous_hidden_state = tuple(
[t.detach().cuda() for t in self.model.hidden])
net_input = X[count]
truth = Y[count]
if torch.cuda.is_available():
input_nn = Variable(
torch.Tensor(np.asarray(net_input).reshape(
1, 242)).cuda())
truth = Variable(
torch.Tensor(
np.asarray(truth).reshape(1)).cuda().long())
P = Variable(torch.Tensor(np.ones((1, 20)))).cuda()
P *= self.distribution_epsilon
P[0][truth] = 1 - 19 * self.distribution_epsilon
else:
input_nn = Variable(
torch.Tensor(np.asarray(net_input).reshape(1, 242)))
truth = Variable(torch.Tensor(
np.asarray(truth).reshape(1)))
P = Variable(torch.Tensor(np.ones((1, 20))))
P *= self.distribution_epsilon
P[0][truth] = 1 - 19 * self.distribution_epsilon
#####forward#####
output = self.model.forward(input_nn, previous_hidden_state)
loss = loss_func.forward(P, output, self.alpha)
if loss.item() < .05 or loss.item() > 5:
pass
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(),
0.5)
embedding_optimizer.step()
index = torch.argmax(output).item()
# top 3
_, top_three = torch.topk(output, 3)
if index == truth.item():
prediction_accuracy[0] += 1
if truth.item() in top_three.detach().cpu().tolist()[0]:
prediction_accuracy[1] += 1
store_embedding_back_bnn(self.model, embedding_list, i)
# schedule finished
print('Prediction Accuracy: top1: ', prediction_accuracy[0] / 20,
' top3: ', prediction_accuracy[1] / 20)
print('schedule num:', i)
percentage_accuracy_top1.append(prediction_accuracy[0] / 20)
percentage_accuracy_top3.append(prediction_accuracy[1] / 20)
prediction_accuracy = [0, 0]
self.save_performance_results(
percentage_accuracy_top1, percentage_accuracy_top3,
'inf_blstm_small_' + str(self.num_schedules))
def evaluate_on_test_data(self, load_in_model=False):
"""
Evaluate performance of a trained network tuned upon the alpha divergence loss.
Note this function is called after training convergence
:return:
"""
num_schedules = 75
# load in new data
loss_func = AlphaLoss()
load_directory = '/home/ghost/PycharmProjects/bayesian_prolo/scheduling_env/datasets/test/' + str(
num_schedules) + '_inf_hetero_deadline_pairwise.pkl'
data = pickle.load(open(load_directory, "rb"))
X, Y, schedule_array = create_new_data(num_schedules, data)
start_of_each_set_twenty = create_sets_of_20_from_x_for_pairwise_comparisions(
X)
prediction_accuracy = [0, 0]
percentage_accuracy_top1 = []
percentage_accuracy_top3 = []
embedding_optimizer = torch.optim.Adam(
self.model.EmbeddingList.parameters(), lr=.001)
embedding_list = [
torch.ones(1, 8) * 1 / 3 for i in range(num_schedules)
]
if load_in_model: # TODO: somehow get the string when the update_model flag is true
self.model.load_state_dict(
torch.load(
'/home/ghost/PycharmProjects/bayesian_prolo/saved_models/pairwise_saved_models/NN_homog.tar'
)['nn_state_dict'])
for j in range(0, num_schedules):
schedule_bounds = schedule_array[j]
step = schedule_bounds[0]
load_in_embedding_bnn(self.model, embedding_list, j)
self.model.reinitialize_hidden_to_random()
while step < schedule_bounds[1]:
probability_matrix = np.zeros((20, 20))
previous_hidden_state = tuple(
[t.detach().cuda() for t in self.model.hidden])
for m, counter in enumerate(range(step, step + 20)):
phi_i = X[counter]
phi_i_numpy = np.asarray(phi_i)
# for each set of twenty
for n, second_counter in enumerate(range(step, step + 20)):
# fill entire array with diagonals set to zero
if second_counter == counter: # same as m = n
continue
phi_j = X[second_counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_i_numpy - phi_j_numpy
if torch.cuda.is_available():
feature_input = Variable(
torch.Tensor(feature_input.reshape(1,
13)).cuda())
else:
feature_input = Variable(
torch.Tensor(feature_input.reshape(1, 13)))
# push through nets
preference_prob = self.model.forward(
feature_input, previous_hidden_state)
probability_matrix[m][n] = preference_prob[
0].data.detach()[0].item(
) # TODO: you can do a check if only this line leads to the same thing as the line below
# probability_matrix[n][m] = preference_prob[0].data.detach()[1].item()
# Set of twenty is completed
column_vec = np.sum(probability_matrix, axis=1)
# top 1
choice = np.argmax(column_vec)
# top 3
_, top_three = torch.topk(torch.Tensor(column_vec), 3)
# Then do training update loop
truth = Y[step]
# index top 1
if choice == truth:
prediction_accuracy[0] += 1
# index top 3
if truth in top_three:
prediction_accuracy[1] += 1
# forward
phi_i_num = truth + step # old method: set_of_twenty[0] + truth
phi_i = X[phi_i_num]
phi_i_numpy = np.asarray(phi_i)
# iterate over pairwise comparisons
for counter in range(step, step + 20):
if counter == phi_i_num: # if counter == phi_i_num:
continue
else:
phi_j = X[counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_i_numpy - phi_j_numpy
# label = add_noise_pairwise(label, self.noise_percentage)
if torch.cuda.is_available():
feature_input = Variable(
torch.Tensor(feature_input.reshape(1,
13)).cuda())
P = Variable(
torch.Tensor([
1 - self.distribution_epsilon,
self.distribution_epsilon
]).cuda())
else:
feature_input = Variable(
torch.Tensor(feature_input.reshape(1, 13)))
P = Variable(
torch.Tensor([
1 - self.distribution_epsilon,
self.distribution_epsilon
]))
output = self.model(feature_input,
previous_hidden_state)
loss = loss_func.forward(P, output, self.alpha)
# prepare optimizer, compute gradient, update params
if loss.item() < .001 or loss.item() > 50:
pass
else:
embedding_optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), 0.5)
embedding_optimizer.step()
for counter in range(step, step + 20):
if counter == phi_i_num:
continue
else:
phi_j = X[counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_j_numpy - phi_i_numpy
if torch.cuda.is_available():
feature_input = Variable(
torch.Tensor(feature_input.reshape(1,
13)).cuda())
P = Variable(
torch.Tensor([
self.distribution_epsilon,
1 - self.distribution_epsilon
]).cuda())
else:
feature_input = Variable(
torch.Tensor(feature_input.reshape(1, 13)))
P = Variable(
torch.Tensor([
self.distribution_epsilon,
1 - self.distribution_epsilon
]))
output = self.model(feature_input,
previous_hidden_state)
loss = loss_func.forward(P, output, self.alpha)
# print('loss is :', loss.item())
# clip any very high gradients
# prepare optimizer, compute gradient, update params
if loss.item() < .001 or loss.item() > 50:
pass
else:
embedding_optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), 0.5)
embedding_optimizer.step()
# add average loss to array
store_embedding_back_bnn(self.model, embedding_list, j)
step += 20
# schedule finished
print('Prediction Accuracy: top1: ', prediction_accuracy[0] / 20,
' top3: ', prediction_accuracy[1] / 20)
print('schedule num:', j)
percentage_accuracy_top1.append(prediction_accuracy[0] / 20)
percentage_accuracy_top3.append(prediction_accuracy[1] / 20)
prediction_accuracy = [0, 0]
self.save_performance_results(
percentage_accuracy_top1, percentage_accuracy_top3,
'inf_blstm_small_' + str(self.num_schedules))
def train(self):
"""
Trains NN.
Randomly samples a schedule and timestep within that schedule, produces training data using x_i - x_j
and trains upon that.
:return:
"""
sets_of_twenty = None
which_schedule = None
training_done = False
loss_func = AlphaLoss()
# variables to keep track of loss and number of tasks trained over
running_loss_predict_tasks = 0
num_iterations_predict_task = 0
while not training_done:
# sample a timestep before the cutoff for cross_validation
# Quick Fix
found_a_suitable_candidate = False
while not found_a_suitable_candidate:
rand_timestep_within_sched = np.random.randint(
len(self.start_of_each_set_twenty))
set_of_twenty = self.start_of_each_set_twenty[
rand_timestep_within_sched]
which_schedule = find_which_schedule_this_belongs_to(
self.schedule_array, set_of_twenty)
if set_of_twenty + 59 > self.schedule_array[which_schedule][1]:
pass
else:
found_a_suitable_candidate = True
sets_of_twenty = [
set_of_twenty, set_of_twenty + 20, set_of_twenty + 40
]
self.model.reinitialize_hidden_to_random()
load_in_embedding_bnn(self.model, self.embedding_list,
which_schedule)
# 3 schedules
for set_of_twenty in sets_of_twenty:
truth = self.Y[set_of_twenty]
# find feature vector of true action taken
phi_i_num = truth + set_of_twenty
phi_i = self.X[phi_i_num]
phi_i_numpy = np.asarray(phi_i)
# changes each timestep
previous_hidden_state = tuple(
[t.detach().cuda() for t in self.model.hidden])
# iterate over pairwise comparisons
for counter in range(set_of_twenty, set_of_twenty + 20):
if counter == phi_i_num: # if counter == phi_i_num:
continue
else:
phi_j = self.X[counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_i_numpy - phi_j_numpy
if torch.cuda.is_available():
feature_input = Variable(
torch.Tensor(feature_input.reshape(1,
13)).cuda())
P = Variable(
torch.Tensor([
1 - self.distribution_epsilon,
self.distribution_epsilon
]).cuda())
else:
feature_input = Variable(
torch.Tensor(feature_input.reshape(1, 13)))
P = Variable(
torch.Tensor([
1 - self.distribution_epsilon,
self.distribution_epsilon
]))
output = self.model.forward(feature_input,
previous_hidden_state)
self.opt.zero_grad()
self.embedding_optimizer.zero_grad()
loss = loss_func.forward(P, output, self.alpha)
if torch.isnan(loss):
print(self.alpha, ' :nan occurred at iteration ',
self.total_iterations)
if loss.item() < .001 or loss.item() > 55:
pass
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), 0.5)
self.opt.step()
self.embedding_optimizer.step()
running_loss_predict_tasks += loss.item()
num_iterations_predict_task += 1
# second loop
for counter in range(set_of_twenty, set_of_twenty + 20):
if counter == phi_i_num:
continue
else:
phi_j = self.X[counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_j_numpy - phi_i_numpy
if torch.cuda.is_available():
feature_input = Variable(
torch.Tensor(feature_input.reshape(1,
13)).cuda())
P = Variable(
torch.Tensor([
self.distribution_epsilon,
1 - self.distribution_epsilon
]).cuda())
else:
feature_input = Variable(
torch.Tensor(feature_input.reshape(1, 13)))
P = Variable(
torch.Tensor([
self.distribution_epsilon,
1 - self.distribution_epsilon
]))
output = self.model.forward(feature_input,
previous_hidden_state)
self.opt.zero_grad()
self.embedding_optimizer.zero_grad()
loss = loss_func.forward(P, output, self.alpha)
if torch.isnan(loss):
print(self.alpha, ' :nan occurred at iteration ',
self.total_iterations, ' at',
num_iterations_predict_task)
if loss.item() < .001 or loss.item() > 55:
pass
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), 0.5)
self.opt.step()
self.embedding_optimizer.step()
running_loss_predict_tasks += loss.item()
num_iterations_predict_task += 1
self.embedding_list = store_embedding_back_bnn(
self.model, self.embedding_list, which_schedule)
self.total_loss_array.append(running_loss_predict_tasks /
num_iterations_predict_task)
num_iterations_predict_task = 0
running_loss_predict_tasks = 0
self.total_iterations += 1
if self.total_iterations > 25 and self.total_iterations % 50 == 1:
print('total iterations is', self.total_iterations)
print('total loss (average for each 40, averaged)',
np.mean(self.total_loss_array[-40:]))
if self.total_iterations > 0 and self.total_iterations % self.when_to_save == self.when_to_save - 1:
self.save_trained_nets('bnn_small' + str(self.num_schedules))
if self.total_iterations > 2000 and np.mean(
self.total_loss_array[-100:]) - np.mean(
self.total_loss_array[-500:]
) < self.convergence_epsilon:
training_done = True
def evaluate_on_test_data(self):
"""
Evaluate performance of a trained network.
This is tested on 20% of the data and will be stored in a text file.
:return:
"""
loss_func = AlphaLoss()
num_schedules = 75
# load in new data
load_directory = '/home/ghost/PycharmProjects/bayesian_prolo/scheduling_env/datasets/test/' + str(
num_schedules) + '_inf_hetero_deadline_naive.pkl'
data = pickle.load(open(load_directory, "rb"))
X, Y, schedule_array = create_new_dataset(data, num_schedules)
for i, each_element in enumerate(X):
X[i] = each_element + list(range(20))
prediction_accuracy = [0, 0]
percentage_accuracy_top1 = []
percentage_accuracy_top3 = []
embedding_optimizer = torch.optim.SGD(
self.model.EmbeddingList.parameters(), lr=.001)
embedding_list = [
torch.ones(1, 8) * 1 / 3 for i in range(num_schedules)
]
for i, schedule in enumerate(schedule_array):
load_in_embedding_bnn(self.model, embedding_list, i)
for count in range(schedule[0], schedule[1] + 1):
net_input = X[count]
truth = Y[count]
if torch.cuda.is_available():
input_nn = Variable(
torch.Tensor(np.asarray(net_input).reshape(
1, 242)).cuda())
truth = Variable(
torch.Tensor(
np.asarray(truth).reshape(1)).cuda().long())
P = Variable(torch.Tensor(np.ones((1, 20)))).cuda()
P *= self.distribution_epsilon
P[0][truth] = 1 - 19 * self.distribution_epsilon
else:
input_nn = Variable(
torch.Tensor(np.asarray(net_input).reshape(1, 242)))
truth = Variable(torch.Tensor(
np.asarray(truth).reshape(1)))
P = Variable(torch.Tensor(np.ones((1, 20))))
P *= self.distribution_epsilon
P[0][truth] = 1 - 19 * self.distribution_epsilon
#####forward#####
output = self.model.forward(input_nn)
loss = loss_func.forward(P, output, self.alpha)
if loss.item() < .05 or loss.item() > 5:
pass
else:
embedding_optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(),
0.5)
embedding_optimizer.step()
index = torch.argmax(output).item()
# top 3
_, top_three = torch.topk(output, 3)
if index == truth.item():
prediction_accuracy[0] += 1
if truth.item() in top_three.detach().cpu().tolist()[0]:
prediction_accuracy[1] += 1
print('Prediction Accuracy: top1: ', prediction_accuracy[0] / 20,
' top3: ', prediction_accuracy[1] / 20)
print('schedule num:', i)
percentage_accuracy_top1.append(prediction_accuracy[0] / 20)
percentage_accuracy_top3.append(prediction_accuracy[1] / 20)
prediction_accuracy = [0, 0]
store_embedding_back_bnn(self.model, embedding_list, i)
print(np.mean(percentage_accuracy_top1))
self.save_performance_results(
percentage_accuracy_top1, percentage_accuracy_top3,
'inf_bnn_small_' + str(self.num_schedules))
def train(self):
"""
Trains BDT.
Randomly samples a schedule and timestep within that schedule, and passes in the corresponding data in an attempt to classify which task was scheduled
:return:
"""
total_iterations = 0
loss_func = AlphaLoss()
training_done = False
while not training_done:
# sample a timestep before the cutoff for cross_validation
rand_timestep_within_sched = np.random.randint(len(self.X))
input_nn = self.X[rand_timestep_within_sched]
truth_nn = self.Y[rand_timestep_within_sched]
which_schedule = find_which_schedule_this_belongs_to(
self.schedule_array, rand_timestep_within_sched)
load_in_embedding_bnn(self.model, self.embedding_list,
which_schedule)
# iterate over pairwise comparisons
if torch.cuda.is_available():
input_nn = Variable(
torch.Tensor(np.asarray(input_nn).reshape(
1, 242)).cuda()) # change to 5 to increase batch size
P = Variable(torch.Tensor(np.ones((1, 20)))).cuda()
P *= self.distribution_epsilon
P[0][truth_nn] = 1 - 19 * self.distribution_epsilon
truth = Variable(
torch.Tensor(
np.asarray(truth_nn).reshape(1)).cuda().long())
else:
input_nn = Variable(
torch.Tensor(np.asarray(input_nn).reshape(1, 242)))
P = Variable(
torch.Tensor(np.ones((1, 20) * self.distribution_epsilon)))
P[0][truth_nn] = 1 - 19 * self.distribution_epsilon
truth = Variable(
torch.Tensor(np.asarray(truth_nn).reshape(1)).long())
self.opt.zero_grad()
self.embedding_optimizer.zero_grad()
output = self.model.forward(input_nn)
loss = loss_func.forward(P, output, self.alpha)
if loss.item() < .05 or loss.item() > 5:
pass
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(), 0.5)
self.opt.step()
self.embedding_optimizer.step()
self.total_loss_array.append(loss.item())
total_iterations = len(self.total_loss_array)
self.embedding_list = store_embedding_back_bnn(
self.model, self.embedding_list, which_schedule)
if total_iterations % 50 == 49:
print('loss at', total_iterations,
', total loss (average for each 40, averaged)',
np.mean(self.total_loss_array[-40:]))
if total_iterations > 20000:
training_done = True
def tune_both():
bnn = NeuronBNN()
# now we let network tune both
bnn.__init__()
data, labels = create_dual_neuron_dataset(100)
params = list(bnn.parameters())
del params[6]
opt = torch.optim.SGD(params, lr=.0001)
embedding_opt = torch.optim.SGD(bnn.EmbeddingList[0].parameters(), lr=.01)
embedding_list = [torch.ones(1, 1) * 1 / 2 for _ in range(100)]
loss = nn.L1Loss() # can use L1 as well, shouldn't matter too much
epochs = 1000
# sorry for the copy paste
# even sets of twenty are lam = 1
# odd sets of twenty are lam = 0
even_lambdas = np.linspace(0, 1960, num=50)
for epoch in range(epochs):
for j in range(5):
# chose an even schedule
even = int(np.random.choice(even_lambdas))
load_in_embedding_bnn(bnn, embedding_list, int(even / 20))
for i in range(even, even + 20):
x = data[i][0:2]
label = labels[i]
x = torch.Tensor([x]).reshape((2))
label = torch.Tensor([label]).reshape((1, 1))
output = bnn.forward(x)
if j % 2 == 0:
opt.zero_grad()
error = loss(output, label)
error.backward()
opt.step()
else:
# opt.zero_grad()
embedding_opt.zero_grad()
error = loss(output, label)
error.backward()
embedding_opt.step()
# opt.step()
embedding_list = store_embedding_back_bnn(bnn, embedding_list,
int(even / 20))
for j in range(5):
# chose an even schedule
odd = int(np.random.choice(even_lambdas)) + 20
load_in_embedding_bnn(bnn, embedding_list, int(odd / 20))
for i in range(odd, odd + 20):
x = data[i][0:2]
label = labels[i]
x = torch.Tensor([x]).reshape((2))
label = torch.Tensor([label]).reshape((1, 1))
output = bnn.forward(x)
if j % 2 == 0:
opt.zero_grad()
error = loss(output, label)
error.backward()
opt.step()
else:
# opt.zero_grad()
embedding_opt.zero_grad()
error = loss(output, label)
error.backward()
embedding_opt.step()
# opt.step()
embedding_list = store_embedding_back_bnn(bnn, embedding_list,
int(odd / 20))
test_data, test_labels = create_dual_neuron_dataset(20)
print(bnn.state_dict())
avg_loss = 0
test_embedding_list = [torch.ones(1, 1) * 1 / 2 for _ in range(20)]
embedding_opt = torch.optim.SGD(bnn.EmbeddingList[0].parameters(), lr=.1)
counter = 0
for i in range(20 * 20):
load_in_embedding_bnn(bnn, test_embedding_list, int(i / 20))
x = test_data[i][0:2]
label = test_labels[i]
x = torch.Tensor([x]).reshape((2))
label = torch.Tensor([label]).reshape((1, 1))
output = bnn.forward(x)
error = loss(output, label)
print('output is ', output)
print('label is ', label)
print('error is ', error.item())
avg_loss += error.item()
if error.item() < .05:
counter += 1
if error.item() > .05:
flag = False
tracker = 0
while not flag:
embedding_opt.zero_grad()
error.backward()
embedding_opt.step()
output = bnn.forward(x)
error = loss(output, label)
tracker += 1
if tracker > 100:
flag = True
if error.item() < .1:
flag = True
test_embedding_list = store_embedding_back_bnn(bnn,
test_embedding_list,
int(i / 20))
print(test_embedding_list)
print(embedding_list)
avg_loss /= 400
print('avg loss is', avg_loss)
print('accuracy', counter / 400)
