def load_datasets(token_to_index,
condition_to_index,
test_corpus_name=CONTEXT_SENSITIVE_TEST_CORPUS_NAME):
# load context_sensitive_test dataset
cs_test = load_conditioned_dataset(test_corpus_name, token_to_index,
condition_to_index)
# load context_free_validation dataset
cf_validation = load_context_free_val(token_to_index)
# load context sensitive testset for one selected condition
condition_mask = cs_test.condition_ids != condition_to_index[
DEFAULT_CONDITION]
conditioned_test = Dataset(
x=cs_test.x[condition_mask],
y=cs_test.y[condition_mask],
condition_ids=cs_test.condition_ids[condition_mask])
# get a subset of conditioned_test of the same size as cf_validation;
# if there are no so many samples in conditioned_test, use all of the available conditioned_test samples
cs_test_one_condition = \
generate_subset(conditioned_test, subset_size=min(cf_validation.x.shape[0], conditioned_test.x.shape[0]))
return create_namedtuple_instance(
'EvalMetricsDatasets',
cf_validation=cf_validation,
cs_test=cs_test,
cs_test_one_condition=cs_test_one_condition)
TRAIN_WORD_EMBEDDINGS_LAYER = True # Allow fine-tuning of the word embedding layer during the model training
W2V_MODEL_DIR = os.path.join(
DATA_DIR, 'w2v_models') # Path to store & load trained word2vec models
WORD_EMBEDDING_DIMENSION = 128 # word2vec embedding dimension
W2V_WINDOW_SIZE = 10 # word2vec window size, used during the w2v pre-training
USE_SKIP_GRAM = True # Use skip-gram word2vec mode. When False, CBOW is used
MIN_WORD_FREQ = 1 # Minimum frequency of a word to be used in word2vec pre-calculation
# condition inputs. We use five major emotions to condition our model's predictions
# original emotions
#EMOTIONS_TYPES = create_namedtuple_instance(
# 'EMOTIONS_TYPES', neutral='neutral', anger='anger', joy='joy', fear='fear', disgust='disgust')
# TODO we have emotions {"0": "neutral", "1": "anger", "2": "joy", "3": "laugh", "4": "disgust"}
EMOTIONS_TYPES = create_namedtuple_instance('EMOTIONS_TYPES',
neutral='neutral',
anger='anger',
joy='joy',
laugh='laugh',
disgust='disgust')
DEFAULT_CONDITION = EMOTIONS_TYPES.joy # Default condition to be used during the prediction (if not specified)
CONDITION_EMBEDDING_DIMENSION = 128 # Conditions embedding layer dimension to be trained.
# NN architecture params
ENCODER_DEPTH = 2 # Number of recurrent (GRU) layers for the encoder
DECODER_DEPTH = 2 # Number of recurrent (GRU) layers for the decoder
HIDDEN_LAYER_DIMENSION = 512 # Dimension for the recurrent layer
DENSE_DROPOUT_RATIO = 0.2 # Use dropout with the given ratio before decoder's output
# training params
INPUT_SEQUENCE_LENGTH = 30 # Input sequence length for the model during the training;
INPUT_CONTEXT_SIZE = 3 # Maximum depth of the conversational history to be used in encoder (at least 1)
OUTPUT_SEQUENCE_LENGTH = 32 # Output sequence length. Better to keep as INPUT_SEQUENCE_LENGTH+2 for start/end tokens
QUESTIONS_CORPUS_NAME = 'context_free_questions' # Context-free questions only path
# word embeddings params
USE_PRETRAINED_W2V_EMBEDDINGS_LAYER = True # Whether to use word2vec to pre-train weights for the embedding layer
TRAIN_WORD_EMBEDDINGS_LAYER = True # Allow fine-tuning of the word embedding layer during the model training
W2V_MODEL_DIR = os.path.join(
DATA_DIR, 'w2v_models') # Path to store & load trained word2vec models
WORD_EMBEDDING_DIMENSION = 128 # word2vec embedding dimension
W2V_WINDOW_SIZE = 10 # word2vec window size, used during the w2v pre-training
USE_SKIP_GRAM = True # Use skip-gram word2vec mode. When False, CBOW is used
MIN_WORD_FREQ = 1 # Minimum frequency of a word to be used in word2vec pre-calculation
# condition inputs. We use five major emotions to condition our model's predictions
EMOTIONS_TYPES = create_namedtuple_instance('EMOTIONS_TYPES',
neutral='neutral',
anger='anger',
joy='joy',
fear='fear',
sadness='sadness')
DEFAULT_CONDITION = EMOTIONS_TYPES.neutral # Default condition to be used during the prediction (if not specified)
CONDITION_EMBEDDING_DIMENSION = 128 # Conditions embedding layer dimension to be trained.
# NN architecture params
ENCODER_DEPTH = 2 # Number of recurrent (GRU) layers for the encoder
DECODER_DEPTH = 2 # Number of recurrent (GRU) layers for the decoder
HIDDEN_LAYER_DIMENSION = 512 # Dimension for the recurrent layer
DENSE_DROPOUT_RATIO = 0.2 # Use dropout with the given ratio before decoder's output
# training params
INPUT_SEQUENCE_LENGTH = 30 # Input sequence length for the model during the training;
INPUT_CONTEXT_SIZE = 3 # Maximum depth of the conversational history to be used in encoder (at least 1)
OUTPUT_SEQUENCE_LENGTH = 32 # Output sequence length. Better to keep as INPUT_SEQUENCE_LENGTH+2 for start/end tokens
from cakechat.utils.data_structures import create_namedtuple_instance
SPECIAL_TOKENS = create_namedtuple_instance(
'SPECIAL_TOKENS', PAD_TOKEN=u'_pad_', UNKNOWN_TOKEN=u'_unk_', START_TOKEN=u'_start_', EOS_TOKEN=u'_end_')
DIALOG_TEXT_FIELD = 'text'
DIALOG_CONDITION_FIELD = 'condition'
def __init__(self,
index_to_token,
index_to_condition,
training_data_param,
validation_data_param,
w2v_model_param,
model_init_path=None,
model_resolver=None,
model_name=MODEL_NAME,
corpus_name=BASE_CORPUS_NAME,
skip_token=SPECIAL_TOKENS.PAD_TOKEN,
token_embedding_dim=WORD_EMBEDDING_DIMENSION,
train_token_embedding=TRAIN_WORD_EMBEDDINGS_LAYER,
condition_embedding_dim=CONDITION_EMBEDDING_DIMENSION,
input_seq_len=INPUT_SEQUENCE_LENGTH,
input_context_size=INPUT_CONTEXT_SIZE,
output_seq_len=OUTPUT_SEQUENCE_LENGTH,
hidden_layer_dim=HIDDEN_LAYER_DIMENSION,
use_cudnn=USE_CUDNN,
dense_dropout_ratio=DENSE_DROPOUT_RATIO,
is_reverse_model=False,
reverse_model=None,
learning_rate=LEARNING_RATE,
grad_clip=GRAD_CLIP,
batch_size=BATCH_SIZE,
epochs_num=EPOCHS_NUM,
horovod=None,
tensorboard_log_dir=TENSORBOARD_LOG_DIR,
log_run_metadata=LOG_RUN_METADATA):
"""
:param index_to_token: Dict with mapping: tokens indices to tokens
:param index_to_condition: Dict with mapping: conditions indicies to conditions values
:param training_data_param: Instance of ModelParam, tuple (value, id) where value is a dataset used for training
and id is a name this dataset
:param validation_data_param: Instance of ModelParam, tuple (value, id) where value is a dataset used for
metrics calculation and id is a concatenation of these datasets' names
:param w2v_model_param: Instance of ModelParam, tuple (value, id) where value is a word2vec matrix of shape
(vocab_size, token_embedding_dim) with float values, used for initializing token embedding layers, and id is
the name of word2vec model
:param model_init_path: Path to a file with model's saved weights for layers intialization
:param model_resolver: Factory that takes model path and returns a file resolver object
:param model_name: String prefix that is prepended to automatically generated model's name. The prefix helps
distinguish the current experiment from other experiments with similar params.
:param corpus_name: File name of the training dataset (included into automatically generated model's name)
:param skip_token: Token to skip with masking, usually _pad_ token. Id of this token is inferred from
index_to_token dictionary
:param token_embedding_dim: Vectors dimensionality of tokens embeddings
:param train_token_embedding: Bool value indicating whether to train token embeddings along with other model's
weights or keep them freezed during training
:param condition_embedding_dim: Vectors dimensionality of conditions embeddings
:param input_seq_len: Max number of tokens in the context sentences
:param input_context_size: Max number of sentences in the context
:param output_seq_len: Max number of tokens in the output sentences
:param hidden_layer_dim: Vectors dimensionality of hidden layers in GRU and Dense layers
:param dense_dropout_ratio: Float value between 0 and 1, indicating the ratio of neurons that will be randomly
deactivated during training to prevent model's overfitting
:param is_reverse_model: Bool value indicating the type of model:
False (regular model) - predicts response for the given context
True (reverse model) - predicts context for the given response (actually, predict the last context sentence for
given response and the beginning of the context) - used for calculating Maximim Mutual Information metric
:param reverse_model: Trained reverse model used to generate predictions in *_reranking modes
:param learning_rate: Learning rate of the optimization algorithm
:param grad_clip: Clipping parameter of the optimization algorithm, used to prevent gradient explosion
:param batch_size: Number of samples to be used for gradient estimation on each train step
:param epochs_num: Number of full dataset passes during train
:param horovod: Initialized horovod module used for multi-gpu training. Trains on single gpu if horovod=None
:param tensorboard_log_dir: Path to tensorboard logs directory
:param log_run_metadata: Set 'True' to profile memory consumption and computation time on tensorboard
"""
# Calculate batches number in each epoch.
# The last batch which may be smaller than batch size is included in this number
batches_num_per_epoch = math.ceil(training_data_param.value.x.shape[0] / batch_size) \
if training_data_param.value else None
# Create callbacks
callbacks = self._create_essential_callbacks(self, horovod)
callbacks.extend([
# Custom callback for metrics calculation
CakeChatEvaluatorCallback(self, index_to_token, batch_size,
batches_num_per_epoch)
])
super(CakeChatModel, self).__init__(
model_resolver_factory=model_resolver,
metrics_plotter=TensorboardMetricsPlotter(tensorboard_log_dir),
horovod=horovod,
training_callbacks=callbacks)
WithLogger.__init__(self)
self._model_name = 'reverse_{}'.format(
model_name) if is_reverse_model else model_name
self._rnn_class = CuDNNGRU if use_cudnn else partial(GRU,
reset_after=True)
# tokens params
self._index_to_token = index_to_token
self._token_to_index = {v: k for k, v in index_to_token.items()}
self._vocab_size = len(self._index_to_token)
self._skip_token_id = self._token_to_index[skip_token]
self._token_embedding_dim = token_embedding_dim
self._train_token_embedding = train_token_embedding
self._W_init_embedding = \
self._build_embedding_matrix(self._token_to_index, w2v_model_param.value, token_embedding_dim) \
if w2v_model_param.value else None
# condition params
self._index_to_condition = index_to_condition
self._condition_to_index = {
v: k
for k, v in index_to_condition.items()
}
self._condition_embedding_dim = condition_embedding_dim
# data params
self._training_data = training_data_param.value
self._validation_data = validation_data_param.value
# train params
self._batches_num_per_epoch = batches_num_per_epoch
self._model_init_path = model_init_path
self._horovod = horovod
self._optimizer = optimizers.Adadelta(lr=learning_rate,
clipvalue=grad_clip)
if self._horovod:
self._optimizer = horovod.DistributedOptimizer(self._optimizer)
# gather model's params that define the experiment setting
self._params = create_namedtuple_instance(
name='Params',
corpus_name=corpus_name,
input_context_size=input_context_size,
input_seq_len=input_seq_len,
output_seq_len=output_seq_len,
token_embedding_dim=token_embedding_dim,
train_batch_size=batch_size,
hidden_layer_dim=hidden_layer_dim,
w2v_model=w2v_model_param.id,
is_reverse_model=is_reverse_model,
dense_dropout_ratio=dense_dropout_ratio,
voc_size=len(self._token_to_index),
training_data=training_data_param.id,
validation_data=validation_data_param.id,
epochs_num=epochs_num,
optimizer=self._optimizer.get_config())
# profiling params
self._run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE) if log_run_metadata else None
self._run_metadata = tf.RunMetadata() if log_run_metadata else None
# parts of computational graph
self._models = None
# get trained reverse model used for inference
self._reverse_model = reverse_model
TEST_DATA_DIR = os.path.join(DATA_DIR, 'quality') # Path to datasets for quality metrics calculation
CONTEXT_FREE_VAL_CORPUS_NAME = 'context_free_validation_set' # Context-free validation set path
TEST_CORPUS_NAME = 'context_free_test_set' # Context-free test set path
QUESTIONS_CORPUS_NAME = 'context_free_questions' # Context-free questions only path
# word embeddings params
USE_PRETRAINED_W2V_EMBEDDINGS_LAYER = True # Whether to use word2vec to pre-train weights for the embedding layer
TRAIN_WORD_EMBEDDINGS_LAYER = True # Allow fine-tuning of the word embedding layer during the model training
W2V_MODEL_DIR = os.path.join(DATA_DIR, 'w2v_models') # Path to store & load trained word2vec models
WORD_EMBEDDING_DIMENSION = 128 # word2vec embedding dimension
W2V_WINDOW_SIZE = 10 # word2vec window size, used during the w2v pre-training
USE_SKIP_GRAM = True # Use skip-gram word2vec mode. When False, CBOW is used
MIN_WORD_FREQ = 1 # Minimum frequency of a word to be used in word2vec pre-calculation
# condition inputs. We use five major emotions to condition our model's predictions
EMOTIONS_TYPES = create_namedtuple_instance(
'EMOTIONS_TYPES', neutral='neutral', anger='anger', joy='joy', fear='fear', sadness='sadness')
DEFAULT_CONDITION = EMOTIONS_TYPES.neutral # Default condition to be used during the prediction (if not specified)
CONDITION_EMBEDDING_DIMENSION = 128 # Conditions embedding layer dimension to be trained.
# NN architecture params
ENCODER_DEPTH = 2 # Number of recurrent (GRU) layers for the encoder
DECODER_DEPTH = 2 # Number of recurrent (GRU) layers for the decoder
HIDDEN_LAYER_DIMENSION = 512 # Dimension for the recurrent layer
DENSE_DROPOUT_RATIO = 0.2 # Use dropout with the given ratio before decoder's output
# training params
INPUT_SEQUENCE_LENGTH = 30 # Input sequence length for the model during the training;
INPUT_CONTEXT_SIZE = 3 # Maximum depth of the conversational history to be used in encoder (at least 1)
OUTPUT_SEQUENCE_LENGTH = 32 # Output sequence length. Better to keep as INPUT_SEQUENCE_LENGTH+2 for start/end tokens
BATCH_SIZE = 192 # Default batch size which fits into 8GB of GPU memory
SHUFFLE_TRAINING_BATCHES = True # Shuffle training batches in the dataset each epoch
TEST_DATA_DIR = os.path.join(DATA_DIR, 'quality') # Path to datasets for quality metrics calculation
CONTEXT_FREE_VAL_CORPUS_NAME = 'context_free_validation_set' # Context-free validation set path
TEST_CORPUS_NAME = 'context_free_test_set' # Context-free test set path
QUESTIONS_CORPUS_NAME = 'context_free_questions' # Context-free questions only path
# word embeddings params
USE_PRETRAINED_W2V_EMBEDDINGS_LAYER = True # Whether to use word2vec to pre-train weights for the embedding layer
TRAIN_WORD_EMBEDDINGS_LAYER = True # Allow fine-tuning of the word embedding layer during the model training
W2V_MODEL_DIR = os.path.join(DATA_DIR, 'w2v_models') # Path to store & load trained word2vec models
WORD_EMBEDDING_DIMENSION = 128 # word2vec embedding dimension
W2V_WINDOW_SIZE = 10 # word2vec window size, used during the w2v pre-training
USE_SKIP_GRAM = True # Use skip-gram word2vec mode. When False, CBOW is used
MIN_WORD_FREQ = 1 # Minimum frequency of a word to be used in word2vec pre-calculation
# condition inputs. We use five major emotions to condition our model's predictions
EMOTIONS_TYPES = create_namedtuple_instance(
'EMOTIONS_TYPES', therapist='thera', client='client')
DEFAULT_CONDITION = EMOTIONS_TYPES.therapist # Default condition to be used during the prediction (if not specified)
CONDITION_EMBEDDING_DIMENSION = 128 # Conditions embedding layer dimension to be trained.
# NN architecture params
ENCODER_DEPTH = 2 # Number of recurrent (GRU) layers for the encoder
DECODER_DEPTH = 2 # Number of recurrent (GRU) layers for the decoder
HIDDEN_LAYER_DIMENSION = 512 # Dimension for the recurrent layer
DENSE_DROPOUT_RATIO = 0.2 # Use dropout with the given ratio before decoder's output
# training params
INPUT_SEQUENCE_LENGTH = 30 # Input sequence length for the model during the training;
INPUT_CONTEXT_SIZE = 3 # Maximum depth of the conversational history to be used in encoder (at least 1)
OUTPUT_SEQUENCE_LENGTH = 32 # Output sequence length. Better to keep as INPUT_SEQUENCE_LENGTH+2 for start/end tokens
BATCH_SIZE = 192 # Default batch size which fits into 8GB of GPU memory
SHUFFLE_TRAINING_BATCHES = True # Shuffle training batches in the dataset each epoch
