def main(args):
"""
Main training method
"""
print("Preparing for training...")
training_config = config_holder.ConfigHolder(args.config_file).config
training_config["num_epoch"] = args.num_epoch
train_dataset = sentiment_dataset.train_input_fn(args.train, training_config)
validation_dataset = sentiment_dataset.validation_input_fn(args.validation, training_config)
eval_dataset = sentiment_dataset.eval_input_fn(args.eval, training_config)
model = sentiment_model_cnn.keras_model_fn(None, training_config)
print("Starting training...")
model.summary()
model.fit(
x=train_dataset[0]['embedding_input'], y=train_dataset[1], steps_per_epoch=train_dataset[2]["num_batches"],
epochs=training_config["num_epoch"],
validation_data=(validation_dataset[0]['embedding_input'], validation_dataset[1]),
validation_steps=validation_dataset[2]["num_batches"])
score = model.evaluate(
eval_dataset[0]['embedding_input'], eval_dataset[1], steps=eval_dataset[2]["num_batches"], verbose=0)
print("Test loss:{}".format(score[0]))
print("Test accuracy:{}".format(score[1]))
sentiment_model_cnn.save_model(model, os.path.join(args.model_output_dir, "sentiment_model.h5"))
def main(args):
"""
Main training method
"""
print("Preparing for training...")
training_config = config_holder.ConfigHolder(args.config_file).config
training_config["num_epoch"] = args.num_epoch
embedding_matrix = sentiment_dataset._load_embedding_matrix(training_config)
models = {}
for model_name in training_config["models"]:
train_dataset = sentiment_dataset.train_input_fn(args.train, training_config)
validation_dataset = sentiment_dataset.validation_input_fn(args.validation, training_config)
eval_dataset = sentiment_dataset.eval_input_fn(args.eval, training_config)
model = sentiment_model.fit_model(
model_name,
embedding_matrix,
training_config,
train_dataset,
validation_dataset,
eval_dataset
)
models[model_name] = model
sentiment_model.save_model(model, os.path.join(args.model_output_dir, f"sentiment_model_{model_name}_non_stopwords_5.h5"))
print("{} model saved to {}".format(model_name, os.path.join(args.model_output_dir, f"sentiment_model_{model_name}.h5")))
def main(args):
"""
Main training method
"""
print("Preparing for training...")
training_config = config_holder.ConfigHolder(args.config_file).config
training_config["num_epoch"] = args.num_epoch
#here args.train is redundant and we just need the file name to be imported from the s3 bucket. This change can be observed in sentiment dataset
train_dataset = sentiment_dataset.train_input_fn(args.train, 'train.json',
training_config)
validation_dataset = sentiment_dataset.validation_input_fn(
args.validation, 'dev.json', training_config)
eval_dataset = sentiment_dataset.eval_input_fn(args.eval, 'eval.json',
training_config)
model = sentiment_model_cnn.keras_model_fn(None, training_config)
print("Starting training...")
model.fit(x=train_dataset[0],
y=train_dataset[1],
steps_per_epoch=train_dataset[2]["num_batches"],
epochs=training_config["num_epoch"],
validation_data=(validation_dataset[0], validation_dataset[1]),
validation_steps=validation_dataset[2]["num_batches"])
score = model.evaluate(eval_dataset[0],
eval_dataset[1],
steps=eval_dataset[2]["num_batches"],
verbose=0)
print("Test loss:{}".format(score[0]))
print("Test accuracy:{}".format(score[1]))
sentiment_model_cnn.save_model(
model, os.path.join(args.model_output_dir, "sentiment_model.h5"))