5. Surname Generation - Unconditioned¶
In this notebook, we use the same Surname dataset, but not for nationality classification. The RNN here is used as a decoder that generates sequences of characters (i.e. a generated surname) instead of a featurizer for the classification task we have seen before.
The model in this notebook is unconditioned: it does not observe the nationality before generating a surname. In practice, being unconditioned means the GRU does not bias its computations toward any nationality. In PyTorch code, this means we use a vector of all 0s so that the initial hidden state vector does not
contribute to the computations. In the next notebook, the computational bias is introduced through the initial
hidden vector.
5.1. Imports¶
import os
from argparse import Namespace
from collections import Counter
import json
import re
import string
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from tqdm.notebook import tqdm
5.2. Data Vectorization classes¶
5.2.1. Vocabulary¶
class Vocabulary(object):
"""Class to process text and extract vocabulary for mapping"""
def __init__(self, token_to_idx=None):
"""
Args:
token_to_idx (dict): a pre-existing map of tokens to indices
"""
if token_to_idx is None:
token_to_idx = {}
self._token_to_idx = token_to_idx
self._idx_to_token = {idx: token
for token, idx in self._token_to_idx.items()}
def to_serializable(self):
""" returns a dictionary that can be serialized """
return {'token_to_idx': self._token_to_idx}
@classmethod
def from_serializable(cls, contents):
""" instantiates the Vocabulary from a serialized dictionary """
return cls(**contents)
def add_token(self, token):
"""Update mapping dicts based on the token.
Args:
token (str): the item to add into the Vocabulary
Returns:
index (int): the integer corresponding to the token
"""
if token in self._token_to_idx:
index = self._token_to_idx[token]
else:
index = len(self._token_to_idx)
self._token_to_idx[token] = index
self._idx_to_token[index] = token
return index
def add_many(self, tokens):
"""Add a list of tokens into the Vocabulary
Args:
tokens (list): a list of string tokens
Returns:
indices (list): a list of indices corresponding to the tokens
"""
return [self.add_token(token) for token in tokens]
def lookup_token(self, token):
"""Retrieve the index associated with the token
Args:
token (str): the token to look up
Returns:
index (int): the index corresponding to the token
"""
return self._token_to_idx[token]
def lookup_index(self, index):
"""Return the token associated with the index
Args:
index (int): the index to look up
Returns:
token (str): the token corresponding to the index
Raises:
KeyError: if the index is not in the Vocabulary
"""
if index not in self._idx_to_token:
raise KeyError("the index (%d) is not in the Vocabulary" % index)
return self._idx_to_token[index]
def __str__(self):
return "<Vocabulary(size=%d)>" % len(self)
def __len__(self):
return len(self._token_to_idx)
class SequenceVocabulary(Vocabulary):
def __init__(self, token_to_idx=None, unk_token="<UNK>",
mask_token="<MASK>", begin_seq_token="<BEGIN>",
end_seq_token="<END>"):
super(SequenceVocabulary, self).__init__(token_to_idx)
self._mask_token = mask_token
self._unk_token = unk_token
self._begin_seq_token = begin_seq_token
self._end_seq_token = end_seq_token
self.mask_index = self.add_token(self._mask_token)
self.unk_index = self.add_token(self._unk_token)
self.begin_seq_index = self.add_token(self._begin_seq_token)
self.end_seq_index = self.add_token(self._end_seq_token)
def to_serializable(self):
contents = super(SequenceVocabulary, self).to_serializable()
contents.update({'unk_token': self._unk_token,
'mask_token': self._mask_token,
'begin_seq_token': self._begin_seq_token,
'end_seq_token': self._end_seq_token})
return contents
def lookup_token(self, token):
"""Retrieve the index associated with the token
or the UNK index if token isn't present.
Args:
token (str): the token to look up
Returns:
index (int): the index corresponding to the token
Notes:
`unk_index` needs to be >=0 (having been added into the Vocabulary)
for the UNK functionality
"""
if self.unk_index >= 0:
return self._token_to_idx.get(token, self.unk_index)
else:
return self._token_to_idx[token]
5.2.2. Vectorizer¶
class SurnameVectorizer(object):
""" The Vectorizer which coordinates the Vocabularies and puts them to use"""
def __init__(self, char_vocab, nationality_vocab):
"""
Args:
char_vocab (Vocabulary): maps words to integers
nationality_vocab (Vocabulary): maps nationalities to integers
"""
self.char_vocab = char_vocab
self.nationality_vocab = nationality_vocab
def vectorize(self, surname, vector_length=-1):
"""Vectorize a surname into a vector of observations and targets
The outputs are the vectorized surname split into two vectors:
surname[:-1] and surname[1:]
At each timestep, the first vector is the observation and the second vector is the target.
Args:
surname (str): the surname to be vectorized
vector_length (int): an argument for forcing the length of index vector
Returns:
a tuple: (from_vector, to_vector)
from_vector (numpy.ndarray): the observation vector
to_vector (numpy.ndarray): the target prediction vector
"""
indices = [self.char_vocab.begin_seq_index]
indices.extend(self.char_vocab.lookup_token(token) for token in surname)
indices.append(self.char_vocab.end_seq_index)
if vector_length < 0:
vector_length = len(indices) - 1
from_vector = np.empty(vector_length, dtype=np.int64)
from_indices = indices[:-1]
from_vector[:len(from_indices)] = from_indices
from_vector[len(from_indices):] = self.char_vocab.mask_index
to_vector = np.empty(vector_length, dtype=np.int64)
to_indices = indices[1:]
to_vector[:len(to_indices)] = to_indices
to_vector[len(to_indices):] = self.char_vocab.mask_index
return from_vector, to_vector
@classmethod
def from_dataframe(cls, surname_df):
"""Instantiate the vectorizer from the dataset dataframe
Args:
surname_df (pandas.DataFrame): the surname dataset
Returns:
an instance of the SurnameVectorizer
"""
char_vocab = SequenceVocabulary()
nationality_vocab = Vocabulary()
for index, row in surname_df.iterrows():
for char in row.surname:
char_vocab.add_token(char)
nationality_vocab.add_token(row.nationality)
return cls(char_vocab, nationality_vocab)
@classmethod
def from_serializable(cls, contents):
"""Instantiate the vectorizer from saved contents
Args:
contents (dict): a dict holding two vocabularies for this vectorizer
This dictionary is created using `vectorizer.to_serializable()`
Returns:
an instance of SurnameVectorizer
"""
char_vocab = SequenceVocabulary.from_serializable(contents['char_vocab'])
nat_vocab = Vocabulary.from_serializable(contents['nationality_vocab'])
return cls(char_vocab=char_vocab, nationality_vocab=nat_vocab)
def to_serializable(self):
""" Returns the serializable contents """
return {'char_vocab': self.char_vocab.to_serializable(),
'nationality_vocab': self.nationality_vocab.to_serializable()}
5.2.3. Dataset¶
class SurnameDataset(Dataset):
def __init__(self, surname_df, vectorizer):
"""
Args:
surname_df (pandas.DataFrame): the dataset
vectorizer (SurnameVectorizer): vectorizer instatiated from dataset
"""
self.surname_df = surname_df
self._vectorizer = vectorizer
self._max_seq_length = max(map(len, self.surname_df.surname)) + 2
self.train_df = self.surname_df[self.surname_df.split=='train']
self.train_size = len(self.train_df)
self.val_df = self.surname_df[self.surname_df.split=='val']
self.validation_size = len(self.val_df)
self.test_df = self.surname_df[self.surname_df.split=='test']
self.test_size = len(self.test_df)
self._lookup_dict = {'train': (self.train_df, self.train_size),
'val': (self.val_df, self.validation_size),
'test': (self.test_df, self.test_size)}
self.set_split('train')
@classmethod
def load_dataset_and_make_vectorizer(cls, surname_csv):
"""Load dataset and make a new vectorizer from scratch
Args:
surname_csv (str): location of the dataset
Returns:
an instance of SurnameDataset
"""
surname_df = pd.read_csv(surname_csv)
return cls(surname_df, SurnameVectorizer.from_dataframe(surname_df))
@classmethod
def load_dataset_and_load_vectorizer(cls, surname_csv, vectorizer_filepath):
"""Load dataset and the corresponding vectorizer.
Used in the case in the vectorizer has been cached for re-use
Args:
surname_csv (str): location of the dataset
vectorizer_filepath (str): location of the saved vectorizer
Returns:
an instance of SurnameDataset
"""
surname_df = pd.read_csv(surname_csv)
vectorizer = cls.load_vectorizer_only(vectorizer_filepath)
return cls(surname_df, vectorizer)
@staticmethod
def load_vectorizer_only(vectorizer_filepath):
"""a static method for loading the vectorizer from file
Args:
vectorizer_filepath (str): the location of the serialized vectorizer
Returns:
an instance of SurnameVectorizer
"""
with open(vectorizer_filepath) as fp:
return SurnameVectorizer.from_serializable(json.load(fp))
def save_vectorizer(self, vectorizer_filepath):
"""saves the vectorizer to disk using json
Args:
vectorizer_filepath (str): the location to save the vectorizer
"""
with open(vectorizer_filepath, "w") as fp:
json.dump(self._vectorizer.to_serializable(), fp)
def get_vectorizer(self):
""" returns the vectorizer """
return self._vectorizer
def set_split(self, split="train"):
self._target_split = split
self._target_df, self._target_size = self._lookup_dict[split]
def __len__(self):
return self._target_size
def __getitem__(self, index):
"""the primary entry point method for PyTorch datasets
Args:
index (int): the index to the data point
Returns:
a dictionary holding the data point: (x_data, y_target, class_index)
"""
row = self._target_df.iloc[index]
from_vector, to_vector = \
self._vectorizer.vectorize(row.surname, self._max_seq_length)
nationality_index = \
self._vectorizer.nationality_vocab.lookup_token(row.nationality)
return {'x_data': from_vector,
'y_target': to_vector,
'class_index': nationality_index}
def get_num_batches(self, batch_size):
"""Given a batch size, return the number of batches in the dataset
Args:
batch_size (int)
Returns:
number of batches in the dataset
"""
return len(self) // batch_size
def generate_batches(dataset, batch_size, shuffle=True,
drop_last=True, device="cpu"):
"""
A generator function which wraps the PyTorch DataLoader. It will
ensure each tensor is on the write device location.
"""
dataloader = DataLoader(dataset=dataset, batch_size=batch_size,
shuffle=shuffle, drop_last=drop_last)
for data_dict in dataloader:
out_data_dict = {}
for name, tensor in data_dict.items():
out_data_dict[name] = data_dict[name].to(device)
yield out_data_dict
5.3. The Model: SurnameGenerationModel¶
The SurnameGenerationModel embeds character indices, computes
their sequential state using a GRU, and computes the probability of token predictions using a Linear
layer. More explicitly, the unconditioned SurnameGenerationModel starts with initializing an
Embedding layer, a GRU, and a Linear layer. Similar to the sequence models for classification, when a matrix of integers is input to the model:
We first use a PyTorch Embedding instance, the
char_embedding, to convert the integers to a three dimensional tensor (a sequence of vectors for each batch item);This tensor is passed to the GRU, which computes a hidden state vector for each character in each sequence.
The primary difference between the sequence classification tasks and sequence prediction
tasks of this chapter is how the state vectors computed by the RNN are handled. In classification, we
retrieved a single vector per batch index and performed predictions using those single vectors. In this
example, we reshape our three-dimensional tensor (N,L,F) into a two-dimensional tensor (a matrix (NxL,F)) with each row represents a character (batch and sequence index) and the columns of each row are features (embedding for the character). Using this matrix and the Linear layer, we compute prediction vectors for every character. We finish the computation by reshaping the matrix back into a threedimensional tensor. Because the ordering information is preserved with reshaping operations, each batch and sequence index is still in the same position. The reason why we needed to reshape is because the Linear layer requires a matrix as input.
class SurnameGenerationModel(nn.Module):
def __init__(self, char_embedding_size, char_vocab_size, rnn_hidden_size,
batch_first=True, padding_idx=0, dropout_p=0.5):
"""
Args:
char_embedding_size (int): The size of the character embeddings
char_vocab_size (int): The number of characters to embed
rnn_hidden_size (int): The size of the RNN's hidden state
batch_first (bool): Informs whether the input tensors will
have batch or the sequence on the 0th dimension
padding_idx (int): The index for the tensor padding;
see torch.nn.Embedding
dropout_p (float): the probability of zeroing activations using
the dropout method. higher means more likely to zero.
"""
super(SurnameGenerationModel, self).__init__()
self.char_emb = nn.Embedding(num_embeddings=char_vocab_size,
embedding_dim=char_embedding_size,
padding_idx=padding_idx)
self.rnn = nn.GRU(input_size=char_embedding_size,
hidden_size=rnn_hidden_size,
batch_first=batch_first)
self.fc = nn.Linear(in_features=rnn_hidden_size,
out_features=char_vocab_size)
self._dropout_p = dropout_p
def forward(self, x_in, apply_softmax=False):
"""The forward pass of the model
Args:
x_in (torch.Tensor): an input data tensor.
x_in.shape should be (batch, input_dim)
apply_softmax (bool): a flag for the softmax activation
should be false if used with the Cross Entropy losses
Returns:
the resulting tensor. tensor.shape should be (batch, char_vocab_size)
"""
x_embedded = self.char_emb(x_in)
y_out, _ = self.rnn(x_embedded)
batch_size, seq_size, feat_size = y_out.shape
y_out = y_out.contiguous().view(batch_size * seq_size, feat_size)
y_out = self.fc(F.dropout(y_out, p=self._dropout_p))
if apply_softmax:
y_out = F.softmax(y_out, dim=1)
new_feat_size = y_out.shape[-1]
y_out = y_out.view(batch_size, seq_size, new_feat_size)
return y_out
Even though the percharacter accuracy of the predictions is a measure of model performance, it is
better in this example to do qualitative evaluation by inspecting what kinds of surnames the model will generate. To do this, we write a new loop over a modified version of the steps in the forward() method to compute predictions at each time step and use those predictions as the input to the following time step. Please see the code in the sample_from_model() function.
The output of the model at each time step is a prediction vector which is turned into a probability distribution using the softmax function. Using the probability distribution, we take advantage of the torch.multinomial() sampling function, which selects an index at a rate proportional to the probability of the index. Sampling is a stochastic procedure that produces different outputs each time.
def sample_from_model(model, vectorizer, num_samples=1, sample_size=20,
temperature=1.0):
"""Sample a sequence of indices from the model
Args:
model (SurnameGenerationModel): the trained model
vectorizer (SurnameVectorizer): the corresponding vectorizer
num_samples (int): the number of samples
sample_size (int): the max length of the samples
temperature (float): accentuates or flattens
the distribution.
0.0 < temperature < 1.0 will make it peakier.
temperature > 1.0 will make it more uniform
Returns:
indices (torch.Tensor): the matrix of indices;
shape = (num_samples, sample_size)
"""
begin_seq_index = [vectorizer.char_vocab.begin_seq_index
for _ in range(num_samples)]
begin_seq_index = torch.tensor(begin_seq_index,
dtype=torch.int64).unsqueeze(dim=1)
indices = [begin_seq_index]
h_t = None
for time_step in range(sample_size):
x_t = indices[time_step]
x_emb_t = model.char_emb(x_t)
rnn_out_t, h_t = model.rnn(x_emb_t, h_t)
prediction_vector = model.fc(rnn_out_t.squeeze(dim=1))
probability_vector = F.softmax(prediction_vector / temperature, dim=1)
indices.append(torch.multinomial(probability_vector, num_samples=1))
indices = torch.stack(indices).squeeze().permute(1, 0)
return indices
We need to transform the sampled indices from the sample_from_model() function into a string for humanreadable output. The decode_sample() function demonstrates, to do this, we use the SequenceVocabulary that was used to vectorize the surnames. In creating the string, we use only the indices up to the ENDOFSEQUENCE index. This assumes that the model learns some sense of
when surnames should end.
def decode_samples(sampled_indices, vectorizer):
"""Transform indices into the string form of a surname
Args:
sampled_indices (torch.Tensor): the inidces from `sample_from_model`
vectorizer (SurnameVectorizer): the corresponding vectorizer
"""
decoded_surnames = []
vocab = vectorizer.char_vocab
for sample_index in range(sampled_indices.shape[0]):
surname = ""
for time_step in range(sampled_indices.shape[1]):
sample_item = sampled_indices[sample_index, time_step].item()
if sample_item == vocab.begin_seq_index:
continue
elif sample_item == vocab.end_seq_index:
break
else:
surname += vocab.lookup_index(sample_item)
decoded_surnames.append(surname)
return decoded_surnames
5.4. Training Routine¶
5.4.1. Helper functions¶
def make_train_state(args):
return {'stop_early': False,
'early_stopping_step': 0,
'early_stopping_best_val': 1e8,
'learning_rate': args.learning_rate,
'epoch_index': 0,
'train_loss': [],
'train_acc': [],
'val_loss': [],
'val_acc': [],
'test_loss': -1,
'test_acc': -1,
'model_filename': args.model_state_file}
def update_train_state(args, model, train_state):
"""Handle the training state updates.
Components:
- Early Stopping: Prevent overfitting.
- Model Checkpoint: Model is saved if the model is better
:param args: main arguments
:param model: model to train
:param train_state: a dictionary representing the training state values
:returns:
a new train_state
"""
# Save one model at least
if train_state['epoch_index'] == 0:
torch.save(model.state_dict(), train_state['model_filename'])
train_state['stop_early'] = False
# Save model if performance improved
elif train_state['epoch_index'] >= 1:
loss_tm1, loss_t = train_state['val_loss'][-2:]
# If loss worsened
if loss_t >= loss_tm1:
# Update step
train_state['early_stopping_step'] += 1
# Loss decreased
else:
# Save the best model
if loss_t < train_state['early_stopping_best_val']:
torch.save(model.state_dict(), train_state['model_filename'])
train_state['early_stopping_best_val'] = loss_t
# Reset early stopping step
train_state['early_stopping_step'] = 0
# Stop early ?
train_state['stop_early'] = \
train_state['early_stopping_step'] >= args.early_stopping_criteria
return train_state
def normalize_sizes(y_pred, y_true):
"""Normalize tensor sizes
Args:
y_pred (torch.Tensor): the output of the model
If a 3-dimensional tensor, reshapes to a matrix
y_true (torch.Tensor): the target predictions
If a matrix, reshapes to be a vector
"""
if len(y_pred.size()) == 3:
y_pred = y_pred.contiguous().view(-1, y_pred.size(2))
if len(y_true.size()) == 2:
y_true = y_true.contiguous().view(-1)
return y_pred, y_true
def compute_accuracy(y_pred, y_true, mask_index):
y_pred, y_true = normalize_sizes(y_pred, y_true)
_, y_pred_indices = y_pred.max(dim=1)
correct_indices = torch.eq(y_pred_indices, y_true).float()
valid_indices = torch.ne(y_true, mask_index).float()
n_correct = (correct_indices * valid_indices).sum().item()
n_valid = valid_indices.sum().item()
return n_correct / n_valid * 100
def sequence_loss(y_pred, y_true, mask_index):
y_pred, y_true = normalize_sizes(y_pred, y_true)
return F.cross_entropy(y_pred, y_true, ignore_index=mask_index)
5.4.2. General utilities¶
def set_seed_everywhere(seed, cuda):
np.random.seed(seed)
torch.manual_seed(seed)
if cuda:
torch.cuda.manual_seed_all(seed)
def handle_dirs(dirpath):
if not os.path.exists(dirpath):
os.makedirs(dirpath)
5.4.3. Settings and some prep work¶
args = Namespace(
# Data and Path information
surname_csv="../data/surnames/surnames_with_splits.csv",
vectorizer_file="vectorizer.json",
model_state_file="model.pth",
save_dir="model_storage/model1_unconditioned_surname_generation",
# Model hyper parameters
char_embedding_size=32,
rnn_hidden_size=32,
# Training hyper parameters
seed=1337,
learning_rate=0.001,
batch_size=128,
num_epochs=100,
early_stopping_criteria=5,
# Runtime options
catch_keyboard_interrupt=True,
cuda=True,
expand_filepaths_to_save_dir=True,
reload_from_files=False,
)
if args.expand_filepaths_to_save_dir:
args.vectorizer_file = os.path.join(args.save_dir,
args.vectorizer_file)
args.model_state_file = os.path.join(args.save_dir,
args.model_state_file)
print("Expanded filepaths: ")
print("\t{}".format(args.vectorizer_file))
print("\t{}".format(args.model_state_file))
# Check CUDA
if not torch.cuda.is_available():
args.cuda = False
args.device = torch.device("cuda" if args.cuda else "cpu")
print("Using CUDA: {}".format(args.cuda))
# Set seed for reproducibility
set_seed_everywhere(args.seed, args.cuda)
# handle dirs
handle_dirs(args.save_dir)
Expanded filepaths:
model_storage/model1_unconditioned_surname_generation\vectorizer.json
model_storage/model1_unconditioned_surname_generation\model.pth
Using CUDA: True
5.4.4. Initializations¶
if args.reload_from_files:
# training from a checkpoint
dataset = SurnameDataset.load_dataset_and_load_vectorizer(args.surname_csv,
args.vectorizer_file)
else:
# create dataset and vectorizer
dataset = SurnameDataset.load_dataset_and_make_vectorizer(args.surname_csv)
dataset.save_vectorizer(args.vectorizer_file)
vectorizer = dataset.get_vectorizer()
model = SurnameGenerationModel(char_embedding_size=args.char_embedding_size,
char_vocab_size=len(vectorizer.char_vocab),
rnn_hidden_size=args.rnn_hidden_size,
padding_idx=vectorizer.char_vocab.mask_index)
5.4.5. Training loop¶
mask_index = vectorizer.char_vocab.mask_index
model = model.to(args.device)
optimizer = optim.Adam(model.parameters(), lr=args.learning_rate)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer=optimizer,
mode='min', factor=0.5,
patience=1)
train_state = make_train_state(args)
epoch_bar = tqdm(desc='training routine',
total=args.num_epochs,
position=0)
dataset.set_split('train')
train_bar = tqdm(desc='split=train',
total=dataset.get_num_batches(args.batch_size),
position=1,
leave=True)
dataset.set_split('val')
val_bar = tqdm(desc='split=val',
total=dataset.get_num_batches(args.batch_size),
position=1,
leave=True)
try:
for epoch_index in range(args.num_epochs):
train_state['epoch_index'] = epoch_index
# Iterate over training dataset
# setup: batch generator, set loss and acc to 0, set train mode on
dataset.set_split('train')
batch_generator = generate_batches(dataset,
batch_size=args.batch_size,
device=args.device)
running_loss = 0.0
running_acc = 0.0
model.train()
for batch_index, batch_dict in enumerate(batch_generator):
# the training routine is these 5 steps:
# --------------------------------------
# step 1. zero the gradients
optimizer.zero_grad()
# step 2. compute the output
y_pred = model(x_in=batch_dict['x_data'])
# step 3. compute the loss
loss = sequence_loss(y_pred, batch_dict['y_target'], mask_index)
# step 4. use loss to produce gradients
loss.backward()
# step 5. use optimizer to take gradient step
optimizer.step()
# -----------------------------------------
# compute the running loss and running accuracy
running_loss += (loss.item() - running_loss) / (batch_index + 1)
acc_t = compute_accuracy(y_pred, batch_dict['y_target'], mask_index)
running_acc += (acc_t - running_acc) / (batch_index + 1)
# update bar
train_bar.set_postfix(loss=running_loss,
acc=running_acc,
epoch=epoch_index)
train_bar.update()
train_state['train_loss'].append(running_loss)
train_state['train_acc'].append(running_acc)
# Iterate over val dataset
# setup: batch generator, set loss and acc to 0; set eval mode on
dataset.set_split('val')
batch_generator = generate_batches(dataset,
batch_size=args.batch_size,
device=args.device)
running_loss = 0.
running_acc = 0.
model.eval()
for batch_index, batch_dict in enumerate(batch_generator):
# compute the output
y_pred = model(x_in=batch_dict['x_data'])
# step 3. compute the loss
loss = sequence_loss(y_pred, batch_dict['y_target'], mask_index)
# compute the running loss and running accuracy
running_loss += (loss.item() - running_loss) / (batch_index + 1)
acc_t = compute_accuracy(y_pred, batch_dict['y_target'], mask_index)
running_acc += (acc_t - running_acc) / (batch_index + 1)
# Update bar
val_bar.set_postfix(loss=running_loss, acc=running_acc,
epoch=epoch_index)
val_bar.update()
train_state['val_loss'].append(running_loss)
train_state['val_acc'].append(running_acc)
train_state = update_train_state(args=args, model=model,
train_state=train_state)
scheduler.step(train_state['val_loss'][-1])
if train_state['stop_early']:
break
# move model to cpu for sampling
model = model.cpu()
sampled_surnames = decode_samples(
sample_from_model(model, vectorizer, num_samples=2),
vectorizer)
epoch_bar.set_postfix(sample1=sampled_surnames[0],
sample2=sampled_surnames[1])
# move model back to whichever device it should be on
model = model.to(args.device)
train_bar.n = 0
val_bar.n = 0
epoch_bar.update()
except KeyboardInterrupt:
print("Exiting loop")
np.random.choice(np.arange(len(vectorizer.nationality_vocab)), replace=True, size=2)
array([8, 7])
# compute the loss & accuracy on the test set using the best available model
model.load_state_dict(torch.load(train_state['model_filename']))
model = model.to(args.device)
dataset.set_split('test')
batch_generator = generate_batches(dataset,
batch_size=args.batch_size,
device=args.device)
running_acc = 0.
model.eval()
for batch_index, batch_dict in enumerate(batch_generator):
# compute the output
y_pred = model(x_in=batch_dict['x_data'])
# compute the loss
loss = sequence_loss(y_pred, batch_dict['y_target'], mask_index)
# compute the accuracy
running_loss += (loss.item() - running_loss) / (batch_index + 1)
acc_t = compute_accuracy(y_pred, batch_dict['y_target'], mask_index)
running_acc += (acc_t - running_acc) / (batch_index + 1)
train_state['test_loss'] = running_loss
train_state['test_acc'] = running_acc
print("Test loss: {};".format(train_state['test_loss']))
print("Test Accuracy: {}".format(train_state['test_acc']))
Test loss: 2.6623813112576804;
Test Accuracy: 23.699346354510485
5.5. Inference¶
# number of names to generate
num_names = 10
model = model.cpu()
# Generate nationality hidden state
sampled_surnames = decode_samples(
sample_from_model(model, vectorizer, num_samples=num_names),
vectorizer)
# Show results
print ("-"*15)
for i in range(num_names):
print (sampled_surnames[i])
---------------
Clan
Turkuek
Srigbo
T
Gchare
Wagil
Noélaveyo
Faiausmeiduz
Spylik
Wardhin
Using the functions sample_from_model() and decode_samples(), we can inspect the output of the model above, to get a sense of whether the model is learning to generate sensible surnames.
What can we learn from inspecting the output?
We can see that although the surnames appear to follow several morphological patterns, the names don’t appear distinctly to be of one nationality or another. One possibility is that learning a general model of surnames confuses the character distributions between different nationalities. The conditioned SurnameGenerationModel is meant to handle this kind of situation.