def __getitem__(self, index):
s = self.data[index]
if False: # cuda.is_available():
return cuda.FloatTensor(s[0]), cuda.FloatTensor(s[1])
else:
return FloatTensor(s[0]) / 255, FloatTensor(s[1])
def init_hiddens(self, batch_size):
h0 = Variable(tc.FloatTensor(self.num_layers * self.num_directions,
batch_size, self.hidden_size).zero_(),
requires_grad=False)
c0 = Variable(tc.FloatTensor(self.num_layers * self.num_directions,
batch_size, self.hidden_size).zero_(),
requires_grad=False)
return h0, c0
def forward(self, input=tc.FloatTensor(0)):
'''
This function runs forward propagation for Convolution operation
:param input: output of previous layer
:return: output of convolution of input with relu activation function
'''
self.input = input
self.act_output = self.relu(
self.layer.forward(tc.FloatTensor(self.input)))
return self.act_output
def init_hidden(self):
"""
PyTorch wants you to supply the last hidden state at each timestep
to the LSTM. You shouldn't need to call this function explicitly
"""
if self.use_cuda:
return (ag.Variable(cuda.FloatTensor(self.num_layers, 1, self.input_dim).zero_()),
ag.Variable(cuda.FloatTensor(self.num_layers, 1, self.input_dim).zero_()))
else:
return (ag.Variable(torch.FloatTensor(self.num_layers, 1, self.input_dim).zero_()),
ag.Variable(torch.FloatTensor(self.num_layers, 1, self.input_dim).zero_()))
def validation(self, loader):
if self.epoch_step == 0:
record_epoch_step = 0
else:
record_epoch_step = self.epoch_step - 1
self.model.eval()
total_loss = cuda.FloatTensor([0.])
total_accu = cuda.FloatTensor([0.])
widgets = [
"processing: ",
progressbar.Percentage(),
" ",
progressbar.ETA(),
" ",
progressbar.FileTransferSpeed(),
]
bar = progressbar.ProgressBar(widgets=widgets,
max_value=len(loader)).start()
for i, batch in enumerate(loader):
bar.update(i)
batch_data, batch_label = batch
batch_data = Variable(batch_data, volatile=True).cuda()
batch_label = Variable(batch_label, volatile=True).cuda()
logits = self.model(batch_data)
loss = self.model.criterion(logits, batch_label)
accu = self.accuracy(logits=logits, targets=batch_label).data
total_accu.add_(accu)
total_loss.add_(loss.data)
#
mean_loss = total_loss.cpu().numpy() / (i + 1)
mean_accu = total_accu.cpu().numpy() / (i + 1)
if self.writer is not None:
self.writer.add_scalar('val/loss',
scalar_value=mean_loss,
global_step=record_epoch_step)
self.writer.add_scalar('val/accu',
scalar_value=mean_accu,
global_step=record_epoch_step)
#
print('')
print("validation-->epoch:{},mean_loss:{}, mean_accuracy:{}".format(
record_epoch_step, mean_loss, mean_accu))
bar.finish()
def __init__(
self,
num_params, # number of model parameters
sigma_init=0.10, # initial standard deviation
sigma_alpha=0.20, # learning rate for standard deviation
sigma_decay=0.999, # anneal standard deviation
sigma_limit=0.01, # stop annealing if less than this
learning_rate=1, # learning rate for standard deviation
learning_rate_decay=0.9999, # annealing the learning rate
learning_rate_limit=0.001, # stop annealing learning rate
popsize=255, # population size
done_threshold=1e-6, # threshold when we say we are done
average_baseline=True, # set baseline to average of batch
weight_decay=0.01, # weight decay coefficient
rank_fitness=True, # use rank rather than fitness numbers
forget_best=True,
diversity_base=0.01): # don't keep the historical best solution
self.num_params = num_params
self.sigma_init = sigma_init
self.sigma_alpha = sigma_alpha
self.sigma_decay = sigma_decay
self.sigma_limit = sigma_limit
self.learning_rate = learning_rate
self.learning_rate_decay = learning_rate_decay
self.learning_rate_limit = learning_rate_limit
self.popsize = popsize
self.average_baseline = average_baseline
if self.average_baseline:
assert (self.popsize % 2 == 0), "Population size must be even"
self.batch_size = int(self.popsize / 2)
else:
assert (self.popsize & 1), "Population size must be odd"
self.batch_size = int((self.popsize - 1) / 2)
self.forget_best = forget_best
# self.batch_reward = torch_c.FloatTensor(self.batch_size*2).fill_(0.0)
self.mu = torch_c.FloatTensor(self.num_params).fill_(0.0)
self.sigma = torch_c.FloatTensor(self.num_params).fill_(
self.sigma_init)
# self.curr_best_mu = torch_c.FloatTensor(self.num_params).fill_(0.0)
# self.best_mu = torch_c.FloatTensor(self.num_params).fill_(0.0)
self.best_reward = 0
self.first_interation = True
self.weight_decay = weight_decay
self.rank_fitness = rank_fitness
if self.rank_fitness:
self.forget_best = True # always forget the best one if we rank
self.done_threshold = done_threshold
self.diversity_base = diversity_base
self.mdb = 0
def data_params():
"""
Returns random data and parameters used to generate the data.
"""
S = int(1e5)
theta_1 = 0.5
theta_2 = 0.3
pi_1 = 0.6
pi_2 = 1 - pi_1
N_ls_all = t.FloatTensor([100 for _ in range(S)])
theta_ls = t.FloatTensor(
np.random.choice([theta_1, theta_2], size=S, p=[pi_1, pi_2]))
n_ls_all = Binomial(N_ls_all, theta_ls).sample()
return N_ls_all, n_ls_all, [pi_1, pi_2, theta_1, theta_2]
def initialize_pi_theta(self, N_ls, n_ls):
'''
Initialize pi_list and theta_list based on the input data
N_ls and n_ls.
This is a better initialization than completely random
initilaization, since it takes into account information from N_ls and
n_ls, and thus a better initialization should help the EM iterations
in the .fit() method to converge faster.
Input
-----
N_ls: 1D tensor
n_ls: 1D tensor
Output
------
None
'''
# Conver to numpy.array
N_array = np.array(N_ls.cpu())
n_array = np.array(n_ls.cpu())
# list of n/N sorted in ascending order
ratio_ls = np.sort(n_array / N_array)
# reproducibility
np.random.seed(seed=123)
# pick K random integer indice, [index_1, ..., index_K]
random_indice = np.sort(np.random.choice(len(ratio_ls), self.K))
# theta are the ratio at the random indice, { ratio_ls[index_k] | k=1,2,...,K }
theta_array = ratio_ls[random_indice]
# the proportion of the midpoint of each pair of consecutive indice
# (index_k + index_{k+1})/2/len(ratio_ls), for k=1,2,...,K-1
acc_portion_ls = (random_indice[1:] +
random_indice[:-1]) / 2.0 / len(ratio_ls)
acc_portion_ls = np.append(acc_portion_ls, 1.0)
# initialize pi_list using the portions of indice
pi_array = np.insert(acc_portion_ls[1:] - acc_portion_ls[:-1],
obj=0,
values=acc_portion_ls[0])
# convert numpy arrays to torch tensors.
self.theta_list = t.FloatTensor(theta_array)
self.pi_list = t.FloatTensor(pi_array)
def ask(self):
'''returns a list of parameters'''
# antithetic sampling
self.epsilon = torch_c.FloatTensor(self.batch_size,
self.num_params).normal_()
self.epsilon.mul_(self.sigma.expand(self.batch_size, self.num_params))
self.epsilon_full = torch.cat((self.epsilon, -1 * self.epsilon))
if self.average_baseline:
epsilon = self.epsilon_full
else:
zeros = torch_c.FloatTensor(1, self.num_params).fill_(0.0)
epsilon = torch.cat((zeros, self.epsilon_full))
solutions = self.mu.expand(epsilon.size()) + epsilon
self.solutions = solutions
return solutions
def _forward_alg(self, feats):
# Do the forward algorithm to compute the partition function
init_alphas = cuda.FloatTensor(1, self.tagset_size).fill_(-10000.)
# START_TAG has all of the score.
init_alphas[0][self.tag_idx[cfg.SENT_START]] = 0.
# Wrap in a variable so that we will get automatic backprop
forward_var = Variable(init_alphas)
# Iterate through the sentence
for feat in feats:
alphas_t = [] # The forward variables at this timestep
for next_tag in range(self.tagset_size):
# broadcast the emission score: it is the same regardless of
# the previous tag
emit_score = feat[next_tag].view(1, -1).expand(
1, self.tagset_size)
# the ith entry of trans_score is the score of transitioning to
# next_tag from i
trans_score = self.transitions[next_tag].view(1, -1)
# The ith entry of next_tag_var is the value for the
# edge (i -> next_tag) before we do log-sum-exp
next_tag_var = forward_var + trans_score + emit_score
# The forward variable for this tag is log-sum-exp of all the
# scores.
alphas_t.append(self.log_sum_exp(next_tag_var).view(1))
forward_var = torch.cat(alphas_t).view(1, -1)
terminal_var = forward_var + self.transitions[self.tag_idx[
cfg.SENT_END]]
alpha = self.log_sum_exp(terminal_var)
return alpha
def __init__(self, emb_mat, tag_idx):
super(BiLSTM_CRF, self).__init__()
self.emb_mat_tensor = Variable(cuda.FloatTensor(emb_mat))
self.embedding_dim = self.emb_mat_tensor.size(1)
self.hidden_dim = cfg.LSTM_HIDDEN_SIZE
self.vocab_size = self.emb_mat_tensor.size(0)
self.tag_idx = tag_idx
self.tagset_size = len(tag_idx)
self.word_embeds = nn.Embedding(self.vocab_size, self.embedding_dim)
self.lstm = nn.LSTM(self.embedding_dim,
self.hidden_dim,
num_layers=1,
bidirectional=True)
# Maps the output of the LSTM into tag space.
self.hidden2tag = nn.Linear(self.hidden_dim * 2, self.tagset_size)
# Matrix of transition parameters. Entry i,j is the score of
# transitioning *to* i *from* j.
self.transitions = nn.Parameter(
torch.randn(self.tagset_size, self.tagset_size)).cuda()
# These two statements enforce the constraint that we never transfer
# to the start tag and we never transfer from the stop tag
self.transitions.data[tag_idx[cfg.SENT_START], :] = -10000
self.transitions.data[:, tag_idx[cfg.SENT_END]] = -10000
self.hidden = self.init_hidden()
def __init__(self,
n_components,
tolerance=1e-5,
max_step=1000,
verbose=True):
'''
Initialize the object.
Input
-----
n_components: int
number of Binomial Distributions in the Mixture.
tolerance: Float
the object fits the data by EM iteration. tolerance is used to define one
of the conditions for the iteration to stop. This condition says that
the iteration continues until the change of the parameters within the
current iteration is greater than tolerance.
max_step: int
the maximum number of iteration steps.
verbose: Boolean
whether to print out the information of the fitting process.
'''
self.K = n_components # int, number of Binomial distributions in the Mixture
self.tolerance = tolerance
self.max_step = max_step
self.verbose = verbose
# initialize the pi_list
pi_list = Uniform(low=1e-6,
high=1e0 - 1e-6).sample([self.K - 1]).to(device)
pi_K = t.FloatTensor([1e0]) - pi_list.sum()
self.pi_list = torch.cat([pi_list, pi_K], dim=0)
# initialize the theta_list
self.theta_list = Uniform(low=1e-6, high=1e0 - 1e-6).sample([self.K])
def forward(self,
input=tc.FloatTensor(0).detach(),
target=tc.FloatTensor(0).detach()):
'''
This function runs forward pass with softmax and categorical cross entropy
:param input: the previous layer output
:param target: the labels
:return: the prediction
'''
self.input = input.detach()
self.target = target
self.output, self.loss = self.softmax_with_cross_entropy(
self.input, self.target)
return self.output
def forward(self, batch_item_index, batch_x, batch_word_seq,
batch_neighbor_index):
z_1 = F.tanh(self.linear1(batch_x))
# z_1 = F.dropout(z_1, self.drop_rate)
z_rating = F.tanh(self.linear2(z_1))
z_content = self.get_content_z(batch_word_seq)
gate = F.sigmoid(
z_rating.mm(self.gate_matrix1) + z_content.mm(self.gate_matrix2) +
self.gate_bias)
gated_embedding = gate * z_rating + (1 - gate) * z_content
# save the embedding for direct lookup
self.item_gated_embedding.weight[
batch_item_index] = gated_embedding.data
gated_neighbor_embedding = self.item_gated_embedding(
batch_neighbor_index)
# aug_gated_embedding: [256, 1, 50]
aug_gated_embedding = torch.unsqueeze(gated_embedding, 1)
score = torch.matmul(aug_gated_embedding,
torch.unsqueeze(self.neighbor_attention, 0))
# score: [256, 1, 480]
score = torch.bmm(score, gated_neighbor_embedding.permute(0, 2, 1))
# make the 0 in score, which will make a difference in softmax
score = torch.where(score == 0, T.FloatTensor([float('-inf')]), score)
score = F.softmax(score, dim=2)
# if the vectors all are '-inf', softmax will generate 'nan', so replace with 0
score = torch.where(score != score, T.FloatTensor([0]), score)
gated_neighbor_embedding = torch.bmm(score, gated_neighbor_embedding)
gated_neighbor_embedding = torch.squeeze(gated_neighbor_embedding, 1)
# gated_embedding = F.dropout(gated_embedding, self.drop_rate)
# gated_neighbor_embedding = F.dropout(gated_neighbor_embedding, self.drop_rate)
z_3 = F.tanh(self.linear3(gated_embedding))
# z_3 = F.dropout(z_3, self.drop_rate)
z_3_neighbor = F.tanh(self.linear3(gated_neighbor_embedding))
# z_3_neighbor = F.dropout(z_3_neighbor, self.drop_rate)
y_pred = F.sigmoid(
self.linear4(z_3) + z_3_neighbor.mm(self.linear4.weight.t()))
return y_pred
def __getitem__(self, index):
if not hasattr(self, 'hdf5_dataset'):
self.open_hdf5()
idx_eFTrack_Eta = tcuda.LongTensor([self.eFTrack_Eta[index]],
device=self.rank)
idx_eFTrack_Phi = tcuda.LongTensor([self.eFTrack_Phi[index]],
device=self.rank)
val_eFTrack_PT = tcuda.FloatTensor(self.eFTrack_PT[index],
device=self.rank)
idx_eFPhoton_Eta = tcuda.LongTensor([self.eFPhoton_Eta[index]],
device=self.rank)
idx_eFPhoton_Phi = tcuda.LongTensor([self.eFPhoton_Phi[index]],
device=self.rank)
val_eFPhoton_ET = tcuda.FloatTensor(self.eFPhoton_ET[index],
device=self.rank)
idx_eFNHadron_Eta = tcuda.LongTensor([self.eFNHadron_Eta[index]],
device=self.rank)
idx_eFNHadron_Phi = tcuda.LongTensor([self.eFNHadron_Phi[index]],
device=self.rank)
val_eFNHadron_ET = tcuda.FloatTensor(self.eFNHadron_ET[index],
device=self.rank)
calorimeter, scaler = self.process_images(idx_eFTrack_Eta,
idx_eFPhoton_Eta,
idx_eFNHadron_Eta,
idx_eFTrack_Phi,
idx_eFPhoton_Phi,
idx_eFNHadron_Phi,
val_eFTrack_PT,
val_eFPhoton_ET,
val_eFNHadron_ET)
# Set labels
labels_raw = tcuda.FloatTensor(self.labels[index], device=self.rank)
labels_processed = self.process_labels(labels_raw, scaler)
if self.return_baseline:
base_raw = tcuda.FloatTensor(self.base[index], device=self.rank)
base_processed = self.process_baseline(base_raw)
return calorimeter, labels_processed, base_processed, scaler
return calorimeter, labels_processed
def forward(self, input=tc.FloatTensor(0)):
'''
This function runs forward propagation of maxpooling
:param input: output of previous layer
:return: max pooling operation result
'''
self.input = input.detach()
self.output, self.indices = self.layer.forward(self.input)
return self.output
def train(data, model, optimizer, verbose=True):
criterion = nn.NLLLoss()
if model.use_cuda:
criterion.cuda()
correct_actions = 0
total_actions = 0
tot_loss = 0.
instance_count = 0
for sentence, actions in data:
if len(sentence) <= 2:
continue
optimizer.zero_grad()
model.refresh()
outputs, _, actions_done = model(sentence, actions)
if model.use_cuda:
loss = ag.Variable(cuda.FloatTensor([0]))
action_idxs = [
ag.Variable(cuda.LongTensor([a])) for a in actions_done
]
else:
loss = ag.Variable(torch.FloatTensor([0]))
action_idxs = [
ag.Variable(torch.LongTensor([a])) for a in actions_done
]
for output, act in zip(outputs, action_idxs):
loss += criterion(output.view(-1, 3), act)
tot_loss += utils.to_scalar(loss.data)
instance_count += 1
for gold, output in zip(actions_done, outputs):
pred_act = utils.argmax(output.data)
if pred_act == gold:
correct_actions += 1
total_actions += len(outputs)
loss.backward()
optimizer.step()
acc = float(correct_actions) / total_actions
loss = float(tot_loss) / instance_count
if verbose:
print(
"Number of instances: {} Number of network actions: {}".format(
instance_count, total_actions))
print("Acc: {} Loss: {}".format(
float(correct_actions) / total_actions, tot_loss / instance_count))
def forward(self, input=tc.FloatTensor(0).detach()):
'''
This function runs forward prop fully connected layer
:param input: the previous layer of output
:return: the activation function output applied dense operation
'''
self.input = input.detach()
self.act_output = self.relu(
torch.mm(self.input, self.weight) + self.bias).detach()
return self.act_output
def __init__(self, vocabulary_size, context_size,
user_latent_factors_count, item_latent_factors_count):
super(ContextMerger, self).__init__()
self.user_model = nn.Linear(user_latent_factors_count,
context_size,
bias=False)
self.item_model = nn.Linear(item_latent_factors_count,
context_size,
bias=False)
self.rating_weight = nn.Parameter(device.FloatTensor(1))
self.review_model = nn.Linear(vocabulary_size, context_size, bias=True)
def positional_encoding(self, seq_len, emb_dim):
position_encoding = np.array([[
pos / np.power(10000, 2.0 * (j // 2) / emb_dim)
for j in range(emb_dim)
] for pos in range(seq_len)])
# apply sin to even indices in the array; 2i
position_encoding[:, 0::2] = np.sin(position_encoding[:, 0::2])
# apply cos to odd indices in the array; 2i+1
position_encoding[:, 1::2] = np.cos(position_encoding[:, 1::2])
return T.FloatTensor(position_encoding, requires_grad=False)
def __init__(
self,
num_params, # number of model parameters
sigma_init=0.1, # initial standard deviation
sigma_decay=0.999, # anneal standard deviation
sigma_limit=0.01, # stop annealing if less than this
learning_rate=0.01, # learning rate for standard deviation
learning_rate_decay=0.9999, # annealing the learning rate
learning_rate_limit=0.001, # stop annealing learning rate
popsize=255, # population size
antithetic=False, # whether to use antithetic sampling
forget_best=True # forget historical best
):
self.num_params = num_params
self.sigma_decay = sigma_decay
self.sigma = sigma_init
self.sigma_limit = sigma_limit
self.learning_rate = learning_rate
self.learning_rate_decay = learning_rate_decay
self.learning_rate_limit = learning_rate_limit
self.popsize = popsize
if self.popsize % 2 == 1:
self.popsize += 1
self.antithetic = antithetic
self.half_popsize = int(self.popsize / 2)
self.reward = np.zeros(self.popsize)
self.best_mu = torch_c.FloatTensor(self.num_params).fill_(0.0)
self.mu = torch_c.FloatTensor(self.num_params).fill_(
0.0
) # I believe borrow the DNN initial val will help, try it later
self.best_reward = 0
self.first_interation = True
self.forget_best = forget_best
self.diversity_base = 0
self.mdb = 0
print(self.sigma, self.learning_rate)
def sample(self) -> ParameterOrVariable:
"""Return a sample from the normal distribution with these parameters.
Returns:
Tensor of N(self.mean, self.alpha) samples, same shape.
This uses the reparametrization trick, so the return value is
differentiable w.r.t. `self.mean` and `self.alpha`, and can be used as
such in a backpropagation operation.
"""
epsilon = TP.FloatTensor(*self.size()).normal_(mean=0, std=1)
return self.mean + Variable(epsilon) * self.alpha
def _score_sentence(self, feats, tags):
# Gives the score of a provided tag sequence
score = Variable(cuda.FloatTensor([0]))
start_tag = Variable(
torch.cuda.LongTensor([self.tag_idx[cfg.SENT_START]]))
tags = torch.cat([start_tag, tags], dim=0)
for i, feat in enumerate(feats):
score = score + self.transitions[tags.data[i + 1], tags.data[i]]
score += feat[tags[i + 1]]
score = score + self.transitions[self.tag_idx[cfg.SENT_END],
tags.data[-1]]
return score
def validation(loader, model, writer):
model.eval()
total_loss = cuda.FloatTensor([0.])
total_accu = cuda.FloatTensor([0.])
widgets = [
"processing: ",
progressbar.Percentage(),
" ",
progressbar.ETA(),
" ",
progressbar.FileTransferSpeed(),
]
bar = progressbar.ProgressBar(widgets=widgets,
max_value=len(loader)).start()
for i, batch in enumerate(loader):
bar.update(i)
batch_data, batch_label = batch
batch_data = Variable(batch_data).cuda()
batch_label = Variable(batch_label).cuda()
logits = model(batch_data)
loss = model.criterion(logits, batch_label)
accu = tools.accuracy(logits=logits, targets=batch_label).data
total_accu.add_(accu)
total_loss.add_(loss.data)
mean_loss = total_loss.cpu().numpy() / (i + 1)
mean_accu = total_accu.cpu().numpy() / (i + 1)
writer.add_scalar('val/loss', scalar_value=mean_loss, global_step=EPOCH)
writer.add_scalar('val/accu', scalar_value=mean_accu, global_step=EPOCH)
print('')
print("validation-->epoch:{},mean_loss:{}, mean_accuracy:{}".format(
EPOCH, mean_loss, mean_accu))
bar.finish()
def forward(self, input=tc.FloatTensor(0).detach()):
'''
This function runs forward prop flatten operation from 4d to 2d
:param input: the previous layer output
:return: 2d matrix
'''
self.input = input.detach()
self.batch_size = self.input.shape[0]
self.channel_size = self.input.shape[1]
self.width = self.input.shape[2]
self.height = self.input.shape[3]
self.output = self.input.view(self.batch_size, -1).detach()
return self.output
def neg_log_likelihood(self, sentences, tags):
total_loss = Variable(cuda.FloatTensor([0]))
for sentence, tag in zip(sentences, tags):
sentence = sentence[1:-1]
tag = tag[1:-1]
sent_var = Variable(cuda.LongTensor(sentence))
tag_var = Variable(cuda.LongTensor(tag))
feats = self._get_lstm_features(sent_var)
forward_score = self._forward_alg(feats)
gold_score = self._score_sentence(feats, tag_var)
total_loss += forward_score - gold_score
return total_loss
def __init__(self, input, dense_number):
'''
This function creates dense instance with given dense size
:param input: output of previous layer
:param dense_number: number of dense layer
'''
self.input = input.detach()
self.dense_number = dense_number
self.batch_size = self.input.shape[0]
self.input_size = self.input.shape[1]
#initialize weights and biases
self.weight = tc.FloatTensor(
np.random.normal(size=(self.input_size, self.dense_number),
loc=0,
scale=0.01).astype(np.float32)).type(
torch.float32).detach()
self.bias = tc.FloatTensor(np.zeros(
(1, dense_number))).type(torch.float32).detach()
#momentum terms
self.delta_w = 0
self.delta_b = 0
def evaluate(data, model, verbose=False):
correct_actions = 0
total_actions = 0
tot_loss = 0.
instance_count = 0
criterion = nn.NLLLoss()
if model.use_cuda:
criterion.cuda()
for sentence, actions in data:
if len(sentence) > 1:
outputs, _, actions_done = model(sentence, actions)
if model.use_cuda:
loss = ag.Variable(cuda.FloatTensor([0]))
action_idxs = [
ag.Variable(cuda.LongTensor([a])) for a in actions_done
]
else:
loss = ag.Variable(torch.FloatTensor([0]))
action_idxs = [
ag.Variable(torch.LongTensor([a])) for a in actions_done
]
for output, act in zip(outputs, action_idxs):
loss += criterion(output.view((-1, 3)), act)
tot_loss += utils.to_scalar(loss.data)
instance_count += 1
for gold, output in zip(actions_done, outputs):
pred_act = utils.argmax(output.data)
if pred_act == gold:
correct_actions += 1
total_actions += len(outputs)
acc = float(correct_actions) / total_actions
loss = float(tot_loss) / instance_count
if verbose:
print(
"Number of instances: {} Number of network actions: {}".format(
instance_count, total_actions))
print("Acc: {} Loss: {}".format(
float(correct_actions) / total_actions, tot_loss / instance_count))
return acc, loss
def forward(self, input):
'''
This function runs forward prop for dropout
:param input: the output of the previous layer
:return: dropout result
'''
self.input = input.detach()
#create mask
self.dropout_matrix = tc.FloatTensor(
np.random.binomial(1, 1 - self.dropout,
size=self.kernel_number)).expand_as(
self.input).detach()
#apply mask
self.output = torch.mul(self.input, self.dropout_matrix).detach() / (
1 - self.dropout + np.exp(-32))
return self.output
def __init__(self, input, dropout):
'''
This function create instance for Dropout layer
:param input: the output from previous layer
:param dropout: dropout layer probability rate
'''
#check the input that has dimension of two
if len(input.shape) != 2:
raise ValueError('Input shape should be 2 dimensional')
#initialize class attributes
self.input = input.detach()
self.dropout = dropout
self.batch_size = self.input.shape[0]
self.kernel_number = self.input.shape[1]
#create dropout matrix
self.dropout_matrix = tc.FloatTensor(np.zeros(
self.input.shape)).detach()
