87 lines
2.8 KiB
Python
87 lines
2.8 KiB
Python
import tensorflow as tf
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import pickle
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import os
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gpus = tf.config.experimental.list_physical_devices('GPU')
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for gpu in gpus:
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tf.config.experimental.set_memory_growth(gpu, True)
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from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
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import numpy as np
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from tensorflow.keras.preprocessing.text import Tokenizer
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from tensorflow.keras.preprocessing.sequence import pad_sequences
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Activation, Dropout, Dense
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from tensorflow.keras.layers import Embedding
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import sys
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from tensorflow.keras.layers import MaxPooling1D, GlobalMaxPooling2D, LSTM, Bidirectional
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from sklearn.utils import shuffle
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vocab_size = int(sys.argv[1])
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embedding_size = int(sys.argv[2])
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LSTM_SIZE = int(sys.argv[3])
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DROPOUT_LSTM = float(sys.argv[4])
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DROPOUT_REGULAR = float(sys.argv[5])
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BATCH_SIZE = int(sys.argv[6])
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checkpoints = sys.argv[7]
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#p1 = float(sys.argv[3])
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#p2 = float(sys.argv[4])
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train_year = [float(a.rstrip('\n')) for a in open('../train/expected.tsv','r')]
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max_year = max(train_year)
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min_year = min(train_year)
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train_text_tokenized= pickle.load(open('train_text_30k_for_keras.pickle', 'rb'))
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maxlen = max([len(a) for a in train_text_tokenized])
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tokenizer = pickle.load(open('tokenizer.pickle','rb'))
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test_text_tokenized = [a.rstrip('\n') for a in open('../test-A/in.tsv', 'r')]
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test_text_tokenized = tokenizer.texts_to_sequences(test_text_tokenized)
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test_text_tokenized = pad_sequences(test_text_tokenized, padding='post', maxlen=maxlen)
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eval_year = [float(a.rstrip()) for a in open('../dev-0/expected.tsv','r')]
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maxlen = max([len(a) for a in train_text_tokenized])
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model = Sequential()
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embedding_layer = Embedding(vocab_size, embedding_size, input_length=maxlen , trainable=True)
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model.add(embedding_layer)
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#model.add(Bidirectional(LSTM(LSTM_SIZE, dropout = DROPOUT_LSTM, return_sequences=True)))
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model.add(Bidirectional(LSTM(LSTM_SIZE, dropout = DROPOUT_LSTM)))
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model.add(Dropout(DROPOUT_REGULAR))
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model.add(Dense(1, activation='linear'))
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model_checkpoint = sorted(os.listdir(checkpoints))[-1]
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model.load_weights('checkpoints/saved-model-000005-0.00.hdf5','rb')
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scaler = pickle.load(open('minmaxscaler.pickle', 'rb'))
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# EVAL PREDS
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eval_text_tokenized = pickle.load(open('eval_text_30k_for_keras.pickle', 'rb'))
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eval_preds = scaler.inverse_transform(model.predict(eval_text_tokenized, batch_size=20))
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eval_preds = np.minimum(eval_preds, max_year)
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eval_preds = np.maximum(eval_preds, min_year)
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f = open('../dev-0/out.tsv', 'w')
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for i in eval_preds:
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f.write(str(i[0]) + '\n')
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f.close()
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# TEST PREDS
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test_preds = scaler.inverse_transform(model.predict(test_text_tokenized, batch_size=20))
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test_preds = np.minimum(test_preds, max_year)
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test_preds = np.maximum(test_preds, min_year)
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f = open('../test-A/out.tsv', 'w')
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for i in test_preds:
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f.write(str(i[0]) + '\n')
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f.close()
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