89 lines
3.4 KiB
Python
89 lines
3.4 KiB
Python
import tensorflow as tf
|
|
import pickle
|
|
|
|
gpus = tf.config.experimental.list_physical_devices('GPU')
|
|
for gpu in gpus:
|
|
tf.config.experimental.set_memory_growth(gpu, True)
|
|
|
|
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
|
|
|
|
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
|
|
import numpy as np
|
|
from tensorflow.keras.preprocessing.text import Tokenizer
|
|
|
|
from sklearn.preprocessing import MinMaxScaler
|
|
|
|
|
|
from tensorflow.keras.preprocessing.sequence import pad_sequences
|
|
from tensorflow.keras.models import Sequential
|
|
from tensorflow.keras.layers import Activation, Dropout, Dense
|
|
from tensorflow.keras.layers import Embedding
|
|
import sys
|
|
from tensorflow.keras.layers import MaxPooling1D, GlobalMaxPooling2D, LSTM, Bidirectional
|
|
from sklearn.utils import shuffle
|
|
|
|
|
|
vocab_size = int(sys.argv[1])
|
|
embedding_size = int(sys.argv[2])
|
|
LSTM_SIZE = int(sys.argv[3])
|
|
DROPOUT_LSTM = float(sys.argv[4])
|
|
DROPOUT_REGULAR = float(sys.argv[5])
|
|
BATCH_SIZE = int(sys.argv[6])
|
|
checkpoints = sys.argv[7]
|
|
#p1 = float(sys.argv[3])
|
|
#p2 = float(sys.argv[4])
|
|
|
|
FILE='2'
|
|
|
|
train_text = [a.rstrip('\n') for a in open('../train/in.tsv','r')]
|
|
train_year = [float(a.rstrip('\n')) for a in open(f'../train/expected{FILE}.tsv','r')]
|
|
|
|
maxlen = 500
|
|
|
|
#tokenizer = Tokenizer(num_words=vocab_size)
|
|
#tokenizer.fit_on_texts(train_text)
|
|
#train_text_tokenized = tokenizer.texts_to_sequences(train_text)
|
|
#train_text_tokenized = pad_sequences(train_text_tokenized, padding='post', maxlen=maxlen)
|
|
#pickle.dump(train_text_tokenized, open('train_text_30k_for_keras.pickle', 'wb'))
|
|
#pickle.dump(tokenizer, open('tokenizer.pickle', 'wb'))
|
|
train_text_tokenized= pickle.load(open('train_text_30k_for_keras.pickle', 'rb'))
|
|
|
|
|
|
|
|
#eval_text_tokenized = [a.rstrip('\n') for a in open('../dev-0/in.tsv', 'r')]
|
|
#eval_text_tokenized = tokenizer.texts_to_sequences(eval_text_tokenized)
|
|
#eval_text_tokenized = pad_sequences(eval_text_tokenized, padding='post', maxlen=maxlen)
|
|
#pickle.dump(eval_text_tokenized, open('eval_text_30k_for_keras.pickle', 'wb'))
|
|
eval_text_tokenized = pickle.load(open('eval_text_30k_for_keras.pickle', 'rb'))
|
|
|
|
eval_year = [float(a.rstrip()) for a in open(f'../dev-0/expected{FILE}.tsv','r')]
|
|
|
|
|
|
model = Sequential()
|
|
embedding_layer = Embedding(vocab_size, embedding_size, input_length=maxlen , trainable=True)
|
|
model.add(embedding_layer)
|
|
#model.add(Bidirectional(LSTM(LSTM_SIZE, dropout = DROPOUT_LSTM, return_sequences=True)))
|
|
model.add(Bidirectional(LSTM(LSTM_SIZE, dropout = DROPOUT_LSTM)))
|
|
|
|
model.add(Dropout(DROPOUT_REGULAR))
|
|
model.add(Dense(1, activation='linear'))
|
|
|
|
train_text_tokenized, train_year = shuffle(train_text_tokenized, train_year)
|
|
|
|
train_year = np.array(train_year).reshape(-1,1)
|
|
eval_year = np.array(eval_year).reshape(-1,1)
|
|
|
|
scaler = MinMaxScaler().fit(train_year)
|
|
pickle.dump(scaler, open('minmaxscaler.pickle', 'wb'))
|
|
|
|
train_year_scaled = scaler.transform(train_year)
|
|
eval_year_scaled = scaler.transform(eval_year)
|
|
|
|
|
|
|
|
filepath = "./" + checkpoints + "/saved-model-{epoch:06d}-{val_loss:.2f}.hdf5"
|
|
checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True)
|
|
es = EarlyStopping(monitor='val_loss', patience = 70)
|
|
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.000001), loss='mse', metrics=['mse'])
|
|
history = model.fit(train_text_tokenized, train_year_scaled, batch_size=BATCH_SIZE, epochs=100, verbose=1, validation_data = (eval_text_tokenized, eval_year_scaled), callbacks = [es, checkpoint])
|