87 lines
3.4 KiB
Python
87 lines
3.4 KiB
Python
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import tensorflow as tf
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import pickle
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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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print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
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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 sklearn.preprocessing import MinMaxScaler
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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_text = [a.rstrip('\n') for a in open('../train/in.tsv','r')]
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train_year = [float(a.rstrip('\n')) for a in open('../train/expected.tsv','r')]
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#tokenizer = Tokenizer(num_words=vocab_size)
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#tokenizer.fit_on_texts(train_text)
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#train_text_tokenized = tokenizer.texts_to_sequences(train_text)
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maxlen = 500
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#train_text_tokenized = pad_sequences(train_text_tokenized, padding='post', maxlen=maxlen)
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#pickle.dump(train_text_tokenized, open('train_text_30k_for_keras.pickle', 'wb'))
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#pickle.dump(tokenizer, open('tokenizer.pickle', 'wb'))
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train_text_tokenized= pickle.load(open('train_text_30k_for_keras.pickle', 'rb'))
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#eval_text_tokenized = [a.rstrip('\n') for a in open('../dev-0/in.tsv', 'r')]
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#eval_text_tokenized = tokenizer.texts_to_sequences(eval_text_tokenized)
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#eval_text_tokenized = pad_sequences(eval_text_tokenized, padding='post', maxlen=maxlen)
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#pickle.dump(eval_text_tokenized, open('eval_text_30k_for_keras.pickle', 'wb'))
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eval_text_tokenized = pickle.load(open('eval_text_30k_for_keras.pickle', 'rb'))
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eval_year = [float(a.rstrip()) for a in open('../dev-0/expected.tsv','r')]
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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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train_text_tokenized, train_year = shuffle(train_text_tokenized, train_year)
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train_year = np.array(train_year).reshape(-1,1)
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eval_year = np.array(eval_year).reshape(-1,1)
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scaler = MinMaxScaler().fit(train_year)
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pickle.dump(scaler, open('minmaxscaler.pickle', 'wb'))
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train_year_scaled = scaler.transform(train_year)
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eval_year_scaled = scaler.transform(eval_year)
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filepath = "./" + checkpoints + "/saved-model-{epoch:06d}-{val_loss:.2f}.hdf5"
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checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True)
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es = EarlyStopping(monitor='val_loss', patience = 70)
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model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001), loss='mse', metrics=['mse'])
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history = model.fit(train_text_tokenized, train_year_scaled, batch_size=BATCH_SIZE, epochs=5000, verbose=1, validation_data = (eval_text_tokenized, eval_year_scaled), callbacks = [es, checkpoint])
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