vocab = Vocab(params["vocab_path"], params["vocab_size"])
# 构建模型
print("Building the model ...")
# model = Seq2Seq(params)
model = PGN(params)
# print("Creating the batcher ...")
# dataset = batcher(params["train_seg_x_dir"], params["train_seg_y_dir"], vocab, params)
# print('dataset is ', dataset)
# 获取保存管理者
print("Creating the checkpoint manager")
checkpoint = tf.train.Checkpoint(Seq2Seq=model)
checkpoint_manager = tf.train.CheckpointManager(checkpoint, CKPT_DIR, max_to_keep=5)
checkpoint.restore(checkpoint_manager.latest_checkpoint)
if checkpoint_manager.latest_checkpoint:
print("Restored from {}".format(checkpoint_manager.latest_checkpoint))
else:
print("Initializing from scratch.")
# 训练模型
print("Starting the training ...")
train_model(model, vocab, params, checkpoint_manager)
if __name__ == '__main__':
# 获得参数
params = get_params()
# 训练模型
train(params)
from utils.params import get_params
import model.FastText.FastText_params as FastText_params
import model.TextCNN.TextCNN_params as TextCNN_params
app = Flask(__name__)
app.config['JSON_AS_ASCII'] = False
err_result = {"errCode": "", "errMsg": "", "status": False}
FastText_model = tf.keras.models.load_model(
'./results/FastText/2020-03-27-15-01')
# FastText_model = tf.keras.models.load_model('./results/FastText/model.h5')
TextCNN_model = tf.keras.models.load_model(
'./results/TextCNN/2020-03-27-21-41')
_, _, _, _, vocab, mlb = data_loader(get_params())
labels = np.array(mlb.classes_)
fastText_params = FastText_params.get_params()
textCNN_params = TextCNN_params.get_params()
@app.route("/FastText_service/", methods=['GET', 'POST'])
def FastText_service():
try:
text_list = request.json
predict_data = convert(text_list, fastText_params)
except Exception as e:
return jsonify(err_result)
else:
preds = FastText_model.predict(predict_data)
# Build vector of repetitions
for a in assignments:
descriptor[a] += 1
# L2 normalize
descriptor = normalize(descriptor)
return descriptor
if __name__ == "__main__":
params = get_params()
# Change to training set
params['split'] = 'train'
print "Stacking features together..."
# Save features for training set
t = time.time()
X, pca, scaler = stack_features(params)
print "Done. Time elapsed:", time.time() - t
print "Number of training features", np.shape(X)
print "Training codebook..."
t = time.time()
train_codebook(params,X)
print "Done. Time elapsed:", time.time() - t
def main():
args = get_params()
args = create_exp_dirs(args)
agent = Agent(args)
agent.run()
