简介： Javascript类型推断(3) - 算法模型解析构建训练模型上一节我们介绍了生成训练集，测试集，验证集的方法，以及生成词表的方法。这5个文件构成了训练的基本素材：files = { 'train': { 'file': 'data/train.ctf', 'location': 0 },

Javascript类型推断(3) - 算法模型解析

构建训练模型

上一节我们介绍了生成训练集，测试集，验证集的方法，以及生成词表的方法。

这5个文件构成了训练的基本素材：

files = {
 'train': { 'file': 'data/train.ctf', 'location': 0 },
 'valid': { 'file': 'data/valid.ctf', 'location': 0 },
 'test': { 'file': 'data/test.ctf', 'location': 0 },
 'source': { 'file': 'data/source_wl', 'location': 1 },
 'target': { 'file': 'data/target_wl', 'location': 1 }
}

词表我们需要转换一下格式，放到哈希表里：

# load dictionaries
source_wl = [line.rstrip('\n') for line in open(files['source']['file'])]
target_wl = [line.rstrip('\n') for line in open(files['target']['file'])]
source_dict = {source_wl[i]:i for i in range(len(source_wl))}
target_dict = {target_wl[i]:i for i in range(len(target_wl))}

下面是一些全局参数：

# number of words in vocab, slot labels, and intent labels
vocab_size = len(source_dict)
num_labels = len(target_dict)
epoch_size = 17.955*1000*1000
minibatch_size = 5000
emb_dim = 300
hidden_dim = 650
num_epochs = 10

下面我们定义x,y,t三个值，分别与输入词表、输出标签数和隐藏层有关

# Create the containers for input feature (x) and the label (y)
x = C.sequence.input_variable(vocab_size, name="x")
y = C.sequence.input_variable(num_labels, name="y")
t = C.sequence.input_variable(hidden_dim, name="t")

好，我们开始看下训练的流程：

model = create_model()
enc, dec = model(x, t)
trainer = create_trainer()
train()

训练模型

首先是一个词嵌入层：

def create_model():
 embed = C.layers.Embedding(emb_dim, name='embed')

然后是两个双向的循环神经网络（使用GRU），一个全连接网络，和一个dropout：

 encoder = BiRecurrence(C.layers.GRU(hidden_dim//2), C.layers.GRU(hidden_dim//2))
 recoder = BiRecurrence(C.layers.GRU(hidden_dim//2), C.layers.GRU(hidden_（）dim//2))
 project = C.layers.Dense(num_labels, name='classify')
 do = C.layers.Dropout(0.5)

然后把上面的四项组合起来：

 def recode(x, t):
 inp = embed(x)
 inp = C.layers.LayerNormalization()(inp)
 
 enc = encoder(inp)
 rec = recoder(enc + t)
 proj = project(do(rec))
 
 dec = C.ops.softmax(proj)
 return enc, dec
 return recode

其中双向循环神经网络定义如下：

def BiRecurrence(fwd, bwd):
 F = C.layers.Recurrence(fwd)
 G = C.layers.Recurrence(bwd, go_backwards=True)
 x = C.placeholder()
 apply_x = C.splice(F(x), G(x))
 return apply_x

构建训练过程

首先定义下损失函数，由两部分组成，一部分是loss，另一部分是分类错误：

def criterion(model, labels):
 ce = -C.reduce_sum(labels*C.ops.log(model))
 errs = C.classification_error(model, labels)
 return ce, errs

有了损失函数之后，我们使用带动量的Adam算法进行梯度下降训练：

def create_trainer():
 masked_dec = dec*C.ops.clip(C.ops.argmax(y), 0, 1)
 loss, label_error = criterion(masked_dec, y)
 loss *= C.ops.clip(C.ops.argmax(y), 0, 1)
 lr_schedule = C.learning_parameter_schedule_per_sample([1e-3]*2 + [5e-4]*2 + [1e-4], epoch_size=int(epoch_size))
 momentum_as_time_constant = C.momentum_as_time_constant_schedule(1000)
 learner = C.adam(parameters=dec.parameters,
 lr=lr_schedule,
 momentum=momentum_as_time_constant,
 gradient_clipping_threshold_per_sample=15, 
 gradient_clipping_with_truncation=True)
 progress_printer = C.logging.ProgressPrinter(tag='Training', num_epochs=num_epochs)
 trainer = C.Trainer(dec, (loss, label_error), learner, progress_printer)
 C.logging.log_number_of_parameters(dec)
 return trainer

训练

定义好模型之后，我们就可以训练了。

首先我们可以利用CNTK.io包的功能定义一个数据的读取器：

def create_reader(path, is_training):
 return C.io.MinibatchSource(C.io.CTFDeserializer(path, C.io.StreamDefs(
 source = C.io.StreamDef(field='S0', shape=vocab_size, is_sparse=True), 
 slot_labels = C.io.StreamDef(field='S1', shape=num_labels, is_sparse=True)
 )), randomize=is_training, max_sweeps = C.io.INFINITELY_REPEAT if is_training else 1)

然后我们就可以利用这个读取器读取数据开始训练了：

def train():
 train_reader = create_reader(files['train']['file'], is_training=True)
 step = 0
 pp = C.logging.ProgressPrinter(freq=10, tag='Training')
 for epoch in range(num_epochs):
 epoch_end = (epoch+1) * epoch_size
 while step < epoch_end:
 data = train_reader.next_minibatch(minibatch_size, input_map={
 x: train_reader.streams.source,
 y: train_reader.streams.slot_labels
 })
 # Enhance data
 enhance_data(data, enc)
 # Train model
 trainer.train_minibatch(data)
 pp.update_with_trainer(trainer, with_metric=True)
 step += data[y].num_samples
 pp.epoch_summary(with_metric=True)
 trainer.save_checkpoint("models/model-" + str(epoch + 1) + ".cntk")
 validate()
 evaluate()

上面的代码中，enhance_data需要解释一下。

我们的数据并非是完全线性的数据，还需要进行一个数据增强的处理过程：

def enhance_data(data, enc):
 guesses = enc.eval({x: data[x]})
 inputs = C.ops.argmax(x).eval({x: data[x]})
 tables = []
 for i in range(len(inputs)):
 ts = []
 table = {}
 counts = {}
 for j in range(len(inputs[i])):
 inp = int(inputs[i][j])
 if inp not in table:
 table[inp] = guesses[i][j]
 counts[inp] = 1
 else:
 table[inp] += guesses[i][j]
 counts[inp] += 1
 for inp in table:
 table[inp] /= counts[inp]
 for j in range(len(inputs[i])):
 inp = int(inputs[i][j])
 ts.append(table[inp])
 tables.append(np.array(np.float32(ts)))
 s = C.io.MinibatchSourceFromData(dict(t=(tables, C.layers.typing.Sequence[C.layers.typing.tensor])))
 mems = s.next_minibatch(minibatch_size)
 data[t] = mems[s.streams['t']]

测试和验证

测试和验证的过程中，也需要我们上面介绍的数据增强的过程：

def validate():
 valid_reader = create_reader(files['valid']['file'], is_training=False)
 while True:
 data = valid_reader.next_minibatch(minibatch_size, input_map={
 x: valid_reader.streams.source,
 y: valid_reader.streams.slot_labels
 })
 if not data:
 break
 enhance_data(data, enc)
 trainer.test_minibatch(data)
 trainer.summarize_test_progress()

evaluate与validate逻辑完全一样，只是读取的文件不同：

def evaluate():
 test_reader = create_reader(files['test']['file'], is_training=False)
 while True:
 data = test_reader.next_minibatch(minibatch_size, input_map={
 x: test_reader.streams.source,
 y: test_reader.streams.slot_labels
 })
 if not data:
 break
 # Enhance data
 enhance_data(data, enc)
 # Test model
 trainer.test_minibatch(data)
 trainer.summarize_test_progress()

点击“了解更多”技术干货