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validation.py
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validation.py
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import mxnet as mx
import os
import sys
import numpy as np
from hier_lstm import HyperPara, hier_lstm_model, get_input_shapes
from data_io import array_iter_with_init_states as array_iter
#setup logging
from imp import reload
import logging
reload(logging)
logging.basicConfig(format='%(asctime)s %(levelname)s:%(message)s',
level=logging.DEBUG, datefmt='%I:%M:%S')
#model para
_dict_len = 55496
_test = False
_num_lstm_layer = 1
_input_size = _dict_len + 3
_num_hidden = 512
_num_embed = 300
_num_label = _dict_len + 3
_dropout = 0.75
#opt para
_learning_rate = 0.001
#training para
_devs = [mx.gpu()]
_batch_size = 32
_num_epoch = 4
#data
name = 'val'
data_path = os.path.join('data', name + '_data.npy')
label_path = os.path.join('data', name + '_label.npy')
data = np.load(data_path)
label = np.load(label_path)
_nsamples = label.shape[0]
# #model
sent_enc_para = HyperPara(num_lstm_layer = _num_lstm_layer,
seq_len = 100,
input_size = _input_size,
num_hidden = _num_hidden,
num_embed = _num_embed,
num_label = _num_label,
dropout = _dropout)
doc_enc_para = HyperPara(num_lstm_layer = _num_lstm_layer,
seq_len = 3,
input_size = _input_size,
num_hidden = _num_hidden,
num_embed = _num_embed,
num_label = _num_label,
dropout = _dropout)
dec_para = HyperPara(num_lstm_layer = _num_lstm_layer,
seq_len = 30,
input_size = _input_size,
num_hidden = _num_hidden,
num_embed = _num_embed,
num_label = _num_label,
dropout = _dropout)
init_dict = get_input_shapes(sent_enc_para, doc_enc_para, dec_para, _batch_size)
data_iter = array_iter(data, label, _batch_size, list(init_dict.items()),
data_name='data', label_name='label', random=False)
print('Data loading complete.')
epoch = int(sys.argv[1])
_devs = [mx.gpu()]
if sys.argv[2] == 'cpu':
_devs = [mx.cpu(i) for i in range(8)]
if sys.argv[2] == 'gpu':
_devs = [mx.gpu()]
checkpoint_path = os.path.join('checkpoint', 'auto_sum')
pretrained_model = mx.model.FeedForward.load(checkpoint_path, epoch)
def Perplexity(label, pred):
label = label.T.reshape((-1,))
loss = 0.
for i in range(pred.shape[0]):
loss += -np.log(max(1e-10, pred[i][int(label[i])]))
return np.exp(loss / label.size)
opt = mx.optimizer.Adam(learning_rate=_learning_rate)
model = mx.model.FeedForward(ctx = _devs,
symbol = pretrained_model.symbol,
arg_params = pretrained_model.arg_params,
aux_params = pretrained_model.aux_params,
num_epoch = _num_epoch,
begin_epoch = epoch,
optimizer = opt)
print('Previous model load complete.')
# y = pretrained_model.predict(X = data_iter,
# num_batch = 1)
y = pretrained_model.score(X = data_iter,
eval_metric = mx.metric.np(Perplexity),
batch_end_callback = mx.callback.Speedometer(_batch_size, 50),
num_batch = None)
print(y)