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import argparse
import csv
import keras
import keras.backend as K
import numpy as np
from keras.layers import LSTM, Embedding, Input, Lambda, concatenate
from keras.models import Model
from Helpers import Data, Get_Embedding
def get_learning_rate(epoch=None, model=None):
return np.round(float(K.get_value(model.optimizer.lr)), 5)
def make_cosine_func(hidden_size=50):
def exponent_neg_cosine_similarity(x):
""" Helper function for the similarity estimate of the LSTMs outputs """
leftNorm = K.l2_normalize(x[:, :hidden_size], axis=-1)
rightNorm = K.l2_normalize(x[:, hidden_size:], axis=-1)
return K.sum(K.prod([leftNorm, rightNorm], axis=0), axis=1, keepdims=True)
return exponent_neg_cosine_similarity
def main(args):
source_lang = args.source_lang
target_lang = args.target_lang
hidden_size = args.hidden_size
max_len = args.max_len
num_iters = args.num_iters
data_file = args.data_file
learning_rate = args.learning_rate
batch = args.batch
data = Data(source_lang, target_lang, data_file, max_len)
x_train = data.x_train
y_train = data.y_train
x_predict = data.x_val
y_predict = data.y_val
vocab_size = data.vocab_size
max_len = data.max_len
# https://stackoverflow.com/a/10741692/3005749
x = data.y_val
y = np.bincount(x.astype(np.int32))
ii = np.nonzero(y)[0]
assert ii == 1
assert y[ii] == 1000 # hardcoded for now
if not batch:
print(f"Source Lang: {source_lang}")
print(f"Target Lang: {target_lang}")
print(f"Using {len(x_train[0])} pairs to learn")
print(f"Predicting {len(y_predict)} pairs")
print(f"Vocabulary size: {vocab_size}")
print(f"Maximum sequence length: {max_len}")
source_emb_file = args.source_emb_file
target_emb_file = args.target_emb_file
embedding = Get_Embedding(
source_lang, target_lang, source_emb_file, target_emb_file, data.word_to_id
)
embedding_size = embedding.embedding_matrix.shape[1]
seq_1 = Input(shape=(max_len,), dtype="int32", name="sequence1")
seq_2 = Input(shape=(max_len,), dtype="int32", name="sequence2")
embed_layer = Embedding(
output_dim=embedding_size,
input_dim=vocab_size + 1,
input_length=max_len,
trainable=False,
)
embed_layer.build((None,))
embed_layer.set_weights([embedding.embedding_matrix])
input_1 = embed_layer(seq_1)
input_2 = embed_layer(seq_2)
l1 = LSTM(units=hidden_size)
l1_out = l1(input_1)
l2_out = l1(input_2)
concats = concatenate([l1_out, l2_out], axis=-1)
out_func = make_cosine_func(hidden_size)
main_output = Lambda(out_func, output_shape=(1,))(concats)
model = Model(inputs=[seq_1, seq_2], outputs=[main_output])
opt = keras.optimizers.Adadelta(lr=learning_rate, clipnorm=1.25)
model.compile(optimizer=opt, loss="mean_squared_error", metrics=["accuracy"])
model.summary()
adjuster = keras.callbacks.ReduceLROnPlateau(
monitor="val_accuracy", patience=5, verbose=1, factor=0.5, min_lr=0.0001
)
history = model.fit(
x_train,
y_train,
validation_data=(x_predict, y_predict),
epochs=num_iters,
batch_size=32,
verbose=1,
callbacks=[adjuster],
)
target_sents = x_predict[1]
precision_at_one = 0
precision_at_ten = 0
for index, sent in enumerate(x_predict[0]):
source_sents = np.array([sent] * 1000)
to_predict = [source_sents, target_sents]
preds = model.predict(to_predict)
ind = np.argpartition(preds.ravel(), -10)[-10:]
if index in ind:
precision_at_ten += 1
if np.argmax(preds.ravel()) == index:
precision_at_one += 1
training_samples = len(x_train[0])
validation_samples = len(y_predict)
fields = [
source_lang,
target_lang,
training_samples,
validation_samples,
precision_at_one,
precision_at_ten,
]
if not batch:
print(f"Supervised Retrieval {source_lang} - {target_lang}")
print(f"P@1: {precision_at_one/1000}")
else:
with open("supervised.csv", "a") as f:
writer = csv.writer(f)
writer.writerow(fields)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"-sl", "--source_lang", type=str, help="Source language.", required=True
)
parser.add_argument(
"-tl", "--target_lang", type=str, help="Target language.", required=True
)
parser.add_argument(
"-df", "--data_file", type=str, help="Path to dataset.", required=True
)
parser.add_argument(
"-es",
"--source_emb_file",
type=str,
help="Path to source embedding file.",
required=True,
)
parser.add_argument(
"-et",
"--target_emb_file",
type=str,
help="Path to target embedding file.",
required=True,
)
parser.add_argument(
"-l",
"--max_len",
type=int,
help="Maximum number of words in a sentence.",
default=25,
)
parser.add_argument(
"-z",
"--hidden_size",
type=int,
help="Number of units in LSTM layer.",
default=50,
)
parser.add_argument(
"-b",
"--batch",
action="store_true",
help="running in batch (store results to csv) or "
+ "running in a single instance (output the results)",
)
parser.add_argument(
"-n", "--num_iters", type=int, help="Number of iterations/epochs.", default=7
)
parser.add_argument(
"-lr",
"--learning_rate",
type=float,
help="Learning rate for optimizer.",
default=1.0,
)
args = parser.parse_args()
main(args)
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