test.py

#! -*- coding: UTF-8 -*-
"""
Build model, load weights. Feed model inputs that comes from input file.
"""

import sys
assert sys.argv[1]
input_fileName = sys.argv[1]
try:
	cikti_dosyaAdi = sys.argv[2]
except IndexError:
	output_fileName = "outputs.txt"

from EncDec_Seq2Seq import EncDec_Seq2Seq
from utils import *

# Prepare parameters and embedding.
hParams = load_parameters("model.json")
all_data = open("data/all_data.txt", "r", encoding="utf-8").read()
bucket_structure, data_count = prepare_parameters(hParams, all_data)
model_embedding, embedding_matrix, keyed_vector = embedding_load("model/EmbeddingModel", hParams, all_data)

VOCAB = list(keyed_vector.vocab.keys())
vocab_length = len(VOCAB)

# Build model class.
model = EncDec_Seq2Seq(
	mode="test",
	hParams=hParams,
	embedding_matrix=embedding_matrix,
	VOCAB=VOCAB,
	vocab_length=vocab_length,
	decode_max_timestep=bucket_structure[-1][1]
)

# Build model components and initialize all weights.
model.build()

# Load weights.
model.load("model/Weights", True)

# Read input file.
inputs = apply_filter(open(input_fileName, "r", encoding="utf-8").read().split("\n"))
outputF = open(output_fileName, "w", encoding="utf-8")

# Test!
for _input in inputs:
	inp = clean_data(_input).strip()
	inp = pad_to_bucket(inp, bucket_structure, True, hParams)

	enc_x = sentence2matrix(hParams, inp, VOCAB, model_embedding, True)
	
	_output = model.predict(enc_x, dec_x)
	_output = decode_seq(_output[0], VOCAB)
	
	try:
		_output = _output[:_output.index(hParams.vocab_special_token[1])].strip()
	except:
		pass

	print(_input, _output)
	
	outputF.write(_output + "\n")
outputF.close()