# If True, save logdir, otherwise don't
save_graph = True
print(str(len(pivot_ids)))
# Initialize the model
m = model(num_docs,
vocab_size,
num_topics,
embedding_size=embed_size,
pretrained_embeddings=pretrained_embeddings,
freqs=freqs,
batch_size=batch_size,
save_graph_def=save_graph,
restore=MODEL_RESTORE,
logdir=model_dir)
# Train the model
m.train(pivot_ids,
target_ids,
doc_ids,
len(pivot_ids),
num_epochs,
idx_to_word=idx_to_word,
switch_loss_epoch=switch_loss_epoch)
# Visualize topics with pyldavis
trained_model_data = utils.generate_ldavis_data(clean_data_dir, m, idx_to_word,
freqs, vocab_size)
np.savez("{}/model_params".format(model_dir), **trained_model_data)
lmbda = 1e-4
logdir = "bias_rc"
# Initialize the model
m = b_model(num_docs,
vocab_size,
num_topics,
bias_idxes,
bias_topics=num_bias_topics,
bias_lmbda=bias_lambda,
bias_unity=bias_unity,
target_bias_topic_cov=0.8,
embedding_size=embed_size,
pretrained_embeddings=pretrained_embeddings,
freqs=freqs,
batch_size=batch_size,
save_graph_def=save_graph,
logdir=logdir)
# Train the model
m.train(pivot_ids,
target_ids,
doc_ids,
len(pivot_ids),
num_epochs,
idx_to_word=idx_to_word,
switch_loss_epoch=switch_loss_epoch)
# Visualize topics with pyldavis
utils.generate_ldavis_data(data_path, m, idx_to_word, freqs, vocab_size)
# If True, save logdir, otherwise don't
save_graph = True
# Initialize the model
m = model(num_docs,
vocab_size,
num_topics,
embedding_size=embed_size,
pretrained_embeddings=pretrained_embeddings,
freqs=freqs,
batch_size=batch_size,
save_graph_def=save_graph,
fixed_words=True,
restore=True,
logdir="logdir_190403_1240")
"""
# Train the model
m.train(pivot_ids,
target_ids,
doc_ids,
len(pivot_ids),
num_epochs,
idx_to_word=idx_to_word,
switch_loss_epoch=switch_loss_epoch)
"""
# Visualize topics with pyldavis
trained_model_data = utils.generate_ldavis_data(data_path, m, idx_to_word,
freqs, vocab_size)
np.savez("{}/model_params".format("logdir_190403_1240"), **trained_model_data)