inter_infer.py

from __future__ import print_function  # Use a function definition from future version (say 3.x from 2.7 interpreter)

import os
import re
import time

import cntk as C
import numpy as np

# C.device.try_set_default_device(C.device.cpu())

regex = re.compile(r"^[^\d\W]\w*$", re.UNICODE)
keywords = ["async", "await", "break", "continue", "class", "extends", "constructor", "super", "extends", "const",
            "let", "var", "debugger", "delete", "do", "while", "export", "import", "for", "each", "in", "of",
            "function", "return", "get", "set", "if", "else", "instanceof", "typeof", "null", "undefined", "switch",
            "case", "default", "this", "true", "false", "try", "catch", "finally", "void", "yield", "any", "boolean",
            "null", "never", "number", "string", "symbol", "undefined", "void", "as", "is", "enum", "type", "interface",
            "abstract", "implements", "static", "readonly", "private", "protected", "public", "declare", "module",
            "namespace", "require", "from", "of", "package"]

files = {
    'train': {'file': 'data/inter_train.ctf', 'location': 0},
    'valid': {'file': 'data/inter_valid.ctf', 'location': 0},
    'test': {'file': 'data/inter_test.ctf', 'location': 0},
    'source': {'file': 'data/inter_source_wl', 'location': 1},
    'target': {'file': 'data/inter_target_wl', 'location': 1}
}

# 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))}

# number of words in vocab, slot labels, and intent labels
vocab_size = len(source_dict)
num_labels = len(target_dict)
epoch_size = 16209589
minibatch_size = 3500
emb_dim = 300
hidden_dim = 650
num_epochs = 10
training_log_file = "training_logs/inter-project.txt"

# Create Training log directory
if not os.path.isdir("training_logs"):
    os.mkdir("training_logs")

# 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")


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))  # concatenate the tensors
    return apply_x


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

    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


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


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']]


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


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)


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()


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()


def train():
    train_reader = create_reader(files['train']['file'], is_training=True)
    step = 0
    pp = C.logging.ProgressPrinter(freq=10, tag='Training', log_to_file=training_log_file)
    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/inter-project-model-" + str(epoch + 1) + ".cntk")
        validate()
        print("Epoch: " + str(epoch + 1))
        evaluate()


model = create_model()
enc, dec = model(x, t)
trainer = create_trainer()
start_time = time.time()
train()
time = time.time() - start_time
print("--- %s seconds ---" % time)
with open(training_log_file, "a+") as log:
    log.write("Total time: " + str(time) + " seconds")