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lstm

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kubapok 2021-09-16 18:38:49 +02:00
parent 10f548680a
commit 987b64ae50
4 changed files with 11620 additions and 11620 deletions
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12488
dev-0/out.tsv
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4
keras_lstm/predict.py
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@ -57,8 +57,8 @@ model.add(Bidirectional(LSTM(LSTM_SIZE, dropout = DROPOUT_LSTM)))
model.add(Dropout(DROPOUT_REGULAR))
model.add(Dense(1, activation='linear'))
model_checkpoint = sorted(os.listdir(checkpoints))[-1]
model.load_weights('checkpoints/saved-model-000006-0.02.hdf5','rb')
#model_checkpoint = sorted(os.listdir(checkpoints))[-1]
model.load_weights('checkpoints/saved-model-000018-0.03.hdf5','rb')
scaler = pickle.load(open('minmaxscaler.pickle', 'rb'))

26
keras_lstm/train.py
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@ -38,22 +38,22 @@ 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')]
tokenizer = Tokenizer(num_words=vocab_size)
tokenizer.fit_on_texts(train_text)
train_text_tokenized = tokenizer.texts_to_sequences(train_text)
maxlen = 500
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'))
#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 = [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')]
@ -84,5 +84,5 @@ 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=0.0001), loss='mse', metrics=['mse'])
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])
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])

10722
test-A/out.tsv
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