mirror of
https://github.com/WallyS02/Song-Lyrics-Generator.git
synced 2024-10-03 11:26:10 +00:00
Added generative RNN
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RNN/<PASTE GLOVE EMBEDDING HERE>
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RNN/<PASTE GLOVE EMBEDDING HERE>
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9101
RNN/ac_dc.csv
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RNN/ac_dc.csv
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RNN/data_processor.py
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RNN/data_processor.py
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import pandas as pd
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from keras.preprocessing.text import Tokenizer, one_hot
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import numpy as np
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from keras.utils import to_categorical
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def pad_array(array, length):
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return array + [0] * (length - len(array))
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class DataProcessor:
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def __init__(self, csv_filename, seqs_length=None, mode="words"):
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self.lyrics = list(pd.read_csv(csv_filename)["Lyrics"].dropna())
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if mode == "words":
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self.tokenizer = Tokenizer()
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elif mode == "chars":
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self.tokenizer = Tokenizer(char_level=True)
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else:
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raise ValueError("Unsupported mode: " + mode)
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self.mode = mode
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self.tokenizer.fit_on_texts(self.lyrics)
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self.seqs_length = seqs_length
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self.train_lyrics = self.lyrics[:int(0.8 * len(self.lyrics))]
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self.validation_lyrics = self.lyrics[int(0.8 * len(self.lyrics)):]
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def texts_to_ints(self, texts):
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return self.tokenizer.texts_to_sequences(texts)
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def ints_to_text(self, ints):
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return self.tokenizer.sequences_to_texts([ints])[0]
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def texts_to_onehots(self, texts):
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n = len(self.tokenizer.word_index) + 1
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one_hots = [one_hot(lyric, n) for lyric in texts]
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return one_hots
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def vocab_size(self):
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return len(self.tokenizer.word_index) + 1
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def max_length(self, texts):
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full_sequences = self.texts_to_ints(texts)
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return max([len(seq) for seq in full_sequences])
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def training_data(self, kind="ints", padded=False):
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if kind == "ints":
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full_sequences = self.texts_to_ints(self.train_lyrics)
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elif kind == "onehots":
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full_sequences = self.texts_to_onehots(self.train_lyrics)
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else:
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raise ValueError("Kind must be either ints or onehots")
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if not padded and self.seqs_length:
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X = []
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y = []
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for full_sequence in full_sequences:
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for i in range(len(full_sequence) - self.seqs_length):
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X.append(full_sequence[i:i + self.seqs_length])
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y.append(full_sequence[i + self.seqs_length])
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X = np.reshape(X, (len(X), len(X[0]), 1))
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# TODO: Do we need that?
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# X = X / float(self.vocab_size())
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elif padded:
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max_length = max([len(seq) for seq in full_sequences])
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X = []
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y = []
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for full_sequence in full_sequences:
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for i in range(len(full_sequence)):
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X.append(pad_array(full_sequence[:i], max_length))
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y.append(full_sequence[i])
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X = np.reshape(X, (len(X), len(X[0]), 1))
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else:
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raise ValueError("Can't use padding along with seqs_length")
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y = to_categorical(y, num_classes=self.vocab_size())
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return X, y
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def validation_data(self, kind="ints", padded=False):
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if kind == "ints":
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full_sequences = self.texts_to_ints(self.validation_lyrics)
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elif kind == "onehots":
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full_sequences = self.texts_to_onehots(self.validation_lyrics)
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else:
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raise ValueError("Kind must be either ints or onehots")
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if not padded and self.seqs_length:
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X = []
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y = []
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for full_sequence in full_sequences:
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for i in range(len(full_sequence) - self.seqs_length):
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X.append(full_sequence[i:i + self.seqs_length])
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y.append(full_sequence[i + self.seqs_length])
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X = np.reshape(X, (len(X), len(X[0]), 1))
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# TODO: Do we need that?
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# X = X / float(self.vocab_size())
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elif padded:
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max_length = max([len(seq) for seq in full_sequences])
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X = []
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y = []
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for full_sequence in full_sequences:
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for i in range(len(full_sequence)):
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X.append(pad_array(full_sequence[:i], max_length))
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y.append(full_sequence[i])
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X = np.reshape(X, (len(X), len(X[0]), 1))
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else:
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raise ValueError("Can't use padding along with seqs_length")
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y = to_categorical(y, num_classes=self.vocab_size())
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return X, y
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RNN/edit_distances.py
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RNN/edit_distances.py
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import os
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from random import choice, randint
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import numpy as np
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from keras_nlp.src.metrics import EditDistance
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from data_processor import DataProcessor
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from models import BetaGRUModel, DefaultLSTMModel, GammaLSTMModel, DefaultGRUModel, OmicronLSTM, BetaLSTMModel
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from song_generator import SongGenerator
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dp = DataProcessor("ac_dc.csv", 10)
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X, y = dp.training_data()
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v_X, v_y = dp.validation_data()
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# dp = DataProcessor("ac_dc.csv", None)
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# X, y = dp.training_data("ints", True)
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# v_X, v_y = dp.validation_data("ints", True)
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model = GammaLSTMModel(X, y, v_X, v_y)
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weights_to_load = "gamma-lstm-weights.hdf5"
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weights_path = os.path.join("trained_models", weights_to_load)
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model.keras_model.load_weights(weights_path)
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lines = 1
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words_in_line = 1
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generator = SongGenerator(dp, model)
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eds = []
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for i in range(16):
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original_text = choice(dp.validation_lyrics).split(" ")
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original_begin = randint(0, len(original_text) - lines * words_in_line - 1)
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original_end = original_begin + lines * words_in_line + 10
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original = " ".join(original_text[original_begin:original_end])
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original_ints = dp.texts_to_ints([original])[0]
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original = original_ints[10:10+lines*words_in_line]
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seed = original_ints[:10]
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generated = generator.generate(words_in_line, lines, custom_seed=seed).lower()
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generated = dp.texts_to_ints([generated])[0]
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ed = EditDistance(normalize=True)
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eds.append(ed(generated, original).numpy())
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print(f"Average ED: {np.average(eds)}")
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print(f"Min ED: {np.min(eds)}")
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print(f"Max ED: {np.max(eds)}")
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RNN/jimi.csv
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RNN/jimi.csv
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RNN/main.py
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RNN/main.py
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import os
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import models
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from data_processor import DataProcessor
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from quality_evaluator import QualityEvaluatorCallback
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from song_generator import SongGenerator
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dp = DataProcessor("ac_dc.csv", 10)
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X, y = dp.training_data()
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v_X, v_y = dp.validation_data()
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print("Models: \ndefault_lstm\nbeta_lstm\ngamma_lstm\ndefault_gru\nbeta_gru\nomicron_lstm")
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model_decision = input("Which model do you want to use: ")
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if model_decision == "default_lstm":
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model = models.DefaultLSTMModel(X, y, v_X, v_y)
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elif model_decision == "beta_lstm":
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model = models.BetaLSTMModel(X, y, v_X, v_y)
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elif model_decision == "gamma_lstm":
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model = models.GammaLSTMModel(X, y, v_X, v_y)
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elif model_decision == "default_gru":
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model = models.DefaultGRUModel(X, y, v_X, v_y)
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elif model_decision == "beta_gru":
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model = models.BetaGRUModel(X, y, v_X, v_y)
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elif model_decision == "omicron_lstm":
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dp = DataProcessor("ac_dc.csv", None)
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X, y = dp.training_data("ints", True)
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model = models.OmicronLSTM(X, y, dp, v_X, v_y)
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else:
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raise ValueError("No such model exists")
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generator = SongGenerator(dp, model)
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decision = input("Load or train or load and train model: ")
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if decision == "load":
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weights_list = os.listdir("trained_models")
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print("Weights saved are: ")
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for i, name in enumerate(weights_list):
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print(f"{i}: {name}")
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idx_to_load = int(input("Which one to load: "))
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if idx_to_load < 0 or idx_to_load > len(weights_list):
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raise ValueError("Wrong weights choice")
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print(f"Loading {idx_to_load}")
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weights_to_load = weights_list[idx_to_load]
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weights_path = os.path.join("trained_models", weights_to_load)
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model.keras_model.load_weights(weights_path)
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print("Loaded weights :)")
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should_run = True
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while should_run:
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should_generate = input("Do you want to generate lyrics: ")
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if should_generate == "yes":
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temp = input("Provide a temperature: ")
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print(generator.generate(6, 4, float(temp)))
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elif should_generate == "no":
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should_run = False
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print("Goodbye :>")
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else:
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print("I do not understand this input... try again")
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elif decision == "train":
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model.train(generator)
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print("Model trained :>")
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elif decision == "load and train":
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weights_list = os.listdir("trained_models")
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print("Weights saved are: ")
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for i, name in enumerate(weights_list):
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print(f"{i}: {name}")
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idx_to_load = int(input("Which one to load: "))
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if idx_to_load < 0 or idx_to_load > len(weights_list):
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raise ValueError("Wrong weights choice")
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print(f"Loading {idx_to_load}")
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weights_to_load = weights_list[idx_to_load]
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weights_path = os.path.join("trained_models", weights_to_load)
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model.keras_model.load_weights(weights_path)
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print("Loaded weights :) Training...")
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model.train(generator)
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print("Model trained :>")
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else:
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raise ValueError("Unknown decision")
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RNN/metallica.csv
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RNN/metallica.csv
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RNN/metallica_hendrix.txt
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RNN/metallica_hendrix.txt
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RNN/metrics_plots/beta_gru.png
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RNN/metrics_plots/beta_gru.png
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RNN/metrics_plots/default_gru.png
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RNN/metrics_plots/default_lstm.png
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RNN/metrics_plots/default_lstm.png
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RNN/metrics_plots/gamma_lstm.png
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RNN/metrics_plots/gamma_lstm.png
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RNN/metrics_plots/omicron_lstm.png
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RNN/models.py
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RNN/models.py
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import os.path
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import numpy as np
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from keras import Sequential
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from keras.callbacks import ModelCheckpoint, EarlyStopping, CSVLogger
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from keras.layers import LSTM, Dense, GRU, Embedding
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from keras.metrics import Precision, Recall
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from keras_nlp.metrics import EditDistance
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#from keras_nlp.metrics import Perplexity
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from data_processor import DataProcessor
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from quality_evaluator import QualityEvaluatorCallback
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from song_generator import GeneratorCallback
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class DefaultLSTMModel:
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def __init__(self, X, y, v_X, v_y):
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self.keras_model = Sequential()
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self.keras_model.add(LSTM(256, input_shape=(X.shape[1], X.shape[2]), return_sequences=True, dropout=0.2))
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self.keras_model.add(LSTM(256, dropout=0.2))
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self.keras_model.add(Dense(y.shape[1], activation='softmax'))
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self.keras_model.compile(loss='categorical_crossentropy',
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metrics=['accuracy', Precision(), Recall(name="recall")],
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optimizer='adam')
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self.X = X
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self.y = y
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self.validation_X = v_X
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self.validation_y = v_y
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def train(self, generator=None, max_epochs=512):
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filepath = os.path.join("trained_models", "default-lstm-weights.hdf5")
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checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
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early_stop = EarlyStopping(monitor="loss", min_delta=0.01, patience=64)
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callbacks_list = [checkpoint, early_stop, CSVLogger('default_lstm.log')]
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if generator:
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callbacks_list.append(GeneratorCallback(generator))
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self.keras_model.fit(self.X, self.y, epochs=max_epochs, batch_size=64, callbacks=callbacks_list,
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validation_data=(self.validation_X, self.validation_y))
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class BetaLSTMModel:
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def __init__(self, X, y, v_X, v_y):
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self.keras_model = Sequential()
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self.keras_model.add(LSTM(256, input_shape=(X.shape[1], X.shape[2]), return_sequences=True, dropout=0.36))
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self.keras_model.add(LSTM(256, dropout=0.2))
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self.keras_model.add(Dense(y.shape[1], activation='softmax'))
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self.keras_model.compile(loss='categorical_crossentropy',
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metrics=['accuracy', Precision(), Recall(name="recall")],
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optimizer='adam')
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self.X = X
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self.y = y
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self.validation_X = v_X
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self.validation_y = v_y
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def train(self, generator=None, max_epochs=512):
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filepath = os.path.join("trained_models", "beta-lstm-weights.hdf5")
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checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
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early_stop = EarlyStopping(monitor="loss", min_delta=0.01, patience=64)
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callbacks_list = [checkpoint, early_stop, CSVLogger('beta_lstm.log')]
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if generator:
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callbacks_list.append(GeneratorCallback(generator))
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self.keras_model.fit(self.X, self.y, epochs=max_epochs, batch_size=64, callbacks=callbacks_list,
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validation_data=(self.validation_X, self.validation_y))
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class GammaLSTMModel:
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def __init__(self, X, y, v_X, v_y):
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self.keras_model = Sequential()
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self.keras_model.add(LSTM(512, input_shape=(X.shape[1], X.shape[2]), return_sequences=True, dropout=0.2))
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self.keras_model.add(LSTM(512, dropout=0.2))
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self.keras_model.add(Dense(y.shape[1], activation='softmax'))
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self.keras_model.compile(loss='categorical_crossentropy',
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metrics=['accuracy', Precision(), Recall(name="recall")],
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optimizer='adam')
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self.X = X
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self.y = y
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self.validation_X = v_X
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self.validation_y = v_y
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def train(self, generator=None, max_epochs=512):
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filepath = os.path.join("trained_models", "gamma-lstm-weights.hdf5")
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checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
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early_stop = EarlyStopping(monitor="loss", min_delta=0.01, patience=64)
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#quality_eval = QualityEvaluatorCallback(self.validation_X, self.validation_y, self.keras_model)
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callbacks_list = [checkpoint, early_stop, CSVLogger('gamma_lstm.log')]
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if generator:
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callbacks_list.append(GeneratorCallback(generator))
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self.keras_model.fit(self.X, self.y, epochs=max_epochs, batch_size=64, callbacks=callbacks_list,
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validation_data=(self.validation_X, self.validation_y))
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class DefaultGRUModel:
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def __init__(self, X, y, v_X, v_y):
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self.keras_model = Sequential()
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self.keras_model.add(GRU(256, input_shape=(X.shape[1], X.shape[2]), return_sequences=True, dropout=0.2))
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self.keras_model.add(GRU(256, dropout=0.2))
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self.keras_model.add(Dense(y.shape[1], activation='softmax'))
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self.keras_model.compile(loss='categorical_crossentropy',
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metrics=['accuracy', Precision(), Recall(name="recall")],
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optimizer='adam')
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self.X = X
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self.y = y
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self.validation_X = v_X
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self.validation_y = v_y
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def train(self, generator=None, max_epochs=512):
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filepath = os.path.join("trained_models", "default-gru-weights.hdf5")
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checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
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early_stop = EarlyStopping(monitor="loss", min_delta=0.01, patience=64)
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callbacks_list = [checkpoint, early_stop, CSVLogger('default_gru.log')]
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if generator:
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callbacks_list.append(GeneratorCallback(generator))
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self.keras_model.fit(self.X, self.y, epochs=max_epochs, batch_size=64, callbacks=callbacks_list,
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validation_data=(self.validation_X, self.validation_y))
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|
||||
class BetaGRUModel:
|
||||
def __init__(self, X, y, v_X, v_y):
|
||||
self.keras_model = Sequential()
|
||||
self.keras_model.add(GRU(128, input_shape=(X.shape[1], X.shape[2]), return_sequences=True, dropout=0.2))
|
||||
self.keras_model.add(GRU(128, dropout=0.2))
|
||||
self.keras_model.add(Dense(y.shape[1], activation='softmax'))
|
||||
self.keras_model.compile(loss='categorical_crossentropy',
|
||||
metrics=['accuracy', Precision(), Recall(name="recall")],
|
||||
optimizer='adam')
|
||||
self.X = X
|
||||
self.y = y
|
||||
self.validation_X = v_X
|
||||
self.validation_y = v_y
|
||||
|
||||
def train(self, generator=None, max_epochs=512):
|
||||
filepath = os.path.join("trained_models", "beta-gru-weights.hdf5")
|
||||
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
|
||||
early_stop = EarlyStopping(monitor="loss", min_delta=0.01, patience=64)
|
||||
callbacks_list = [checkpoint, early_stop, CSVLogger('beta_gru.log')]
|
||||
|
||||
if generator:
|
||||
callbacks_list.append(GeneratorCallback(generator))
|
||||
|
||||
self.keras_model.fit(self.X, self.y, epochs=max_epochs, batch_size=64, callbacks=callbacks_list,
|
||||
validation_data=(self.validation_X, self.validation_y))
|
||||
|
||||
|
||||
class OmicronLSTM:
|
||||
def __init__(self, X, y, data_processor: DataProcessor, v_X, v_y):
|
||||
self.keras_model = Sequential()
|
||||
|
||||
embeddings_index = {}
|
||||
f = open("glove.6B.100d.txt")
|
||||
for line in f:
|
||||
values = line.split()
|
||||
word = values[0]
|
||||
coefficients = np.asarray(values[1:], dtype="float32")
|
||||
embeddings_index[word] = coefficients
|
||||
f.close()
|
||||
|
||||
word_index = data_processor.tokenizer.word_index
|
||||
max_length = data_processor.max_length(data_processor.train_lyrics)
|
||||
|
||||
embedding_matrix = np.random.normal(0, 1, size=(len(word_index) + 1, 100))
|
||||
for word, i in word_index.items():
|
||||
embedding_vector = embeddings_index.get(word)
|
||||
if embedding_vector is not None:
|
||||
# words not found in embedding index will be all-zeros.
|
||||
embedding_matrix[i] = embedding_vector
|
||||
|
||||
embedding_layer = Embedding(input_dim=len(word_index) + 1,
|
||||
output_dim=100,
|
||||
weights=[embedding_matrix],
|
||||
input_length=max_length,
|
||||
trainable=True)
|
||||
|
||||
self.keras_model.add(embedding_layer)
|
||||
self.keras_model.add(LSTM(256, input_shape=(100, 1), return_sequences=True, dropout=0.2))
|
||||
self.keras_model.add(LSTM(256, dropout=0.2))
|
||||
self.keras_model.add(Dense(y.shape[1], activation='softmax'))
|
||||
self.keras_model.compile(loss='categorical_crossentropy',
|
||||
metrics=['accuracy', Precision(), Recall(name="recall")],
|
||||
optimizer='adam')
|
||||
self.X = X
|
||||
self.y = y
|
||||
self.validation_X = v_X
|
||||
self.validation_y = v_y
|
||||
|
||||
def train(self, generator=None, max_epochs=512):
|
||||
filepath = os.path.join("trained_models", "trained_models/omicron-lstm-weights.hdf5")
|
||||
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
|
||||
early_stop = EarlyStopping(monitor="loss", min_delta=0.01, patience=64)
|
||||
callbacks_list = [checkpoint, early_stop, CSVLogger('omicron_lstm.log')]
|
||||
|
||||
if generator:
|
||||
callbacks_list.append(GeneratorCallback(generator))
|
||||
|
||||
self.keras_model.fit(self.X, self.y, epochs=max_epochs, batch_size=64, callbacks=callbacks_list,
|
||||
validation_data=(self.validation_X, self.validation_y))
|
||||
97
RNN/plots.py
Normal file
97
RNN/plots.py
Normal file
@ -0,0 +1,97 @@
|
||||
import math
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import pandas as pd
|
||||
from os.path import join
|
||||
import numpy as np
|
||||
|
||||
|
||||
def plot_omicron_metrics():
|
||||
data = pd.read_csv(join("trained_models", "omicron_lstm.log"))
|
||||
|
||||
fig, ax = plt.subplots(2, 2)
|
||||
fig.suptitle(f"Model: omicron_lstm")
|
||||
plt.subplots_adjust(left=0.1,
|
||||
bottom=0.1,
|
||||
right=0.9,
|
||||
top=0.9,
|
||||
wspace=0.25,
|
||||
hspace=0.8)
|
||||
fig.set_figwidth(10)
|
||||
|
||||
accuracy = data["accuracy"]
|
||||
ax[0][0].plot(range(1, 250), np.linspace(0, accuracy[0], 249), "deepskyblue", linestyle="--")
|
||||
ax[0][0].plot(range(250, len(accuracy) + 250), accuracy, 'deepskyblue')
|
||||
ax[0][0].set(xlabel="Numer epoki", ylabel="Dokładność (accuracy)")
|
||||
ax[0][0].set_title("Dokładność")
|
||||
|
||||
precision = data["precision"]
|
||||
ax[0][1].plot(range(1, 250), np.linspace(0, precision[0], 249), "limegreen", linestyle="--")
|
||||
ax[0][1].plot(range(250, len(precision) + 250), precision, 'limegreen')
|
||||
ax[0][1].set(xlabel="Numer epoki", ylabel="Precyzja (precision)")
|
||||
ax[0][1].set_title("Precyzja")
|
||||
|
||||
recall = data["recall"]
|
||||
ax[1][0].plot(range(1, 250), np.linspace(0, recall[0], 249), "orange", linestyle="--")
|
||||
ax[1][0].plot(range(250, len(recall) + 250), recall, 'orange')
|
||||
ax[1][0].set(xlabel="Numer epoki", ylabel="Zwrot (recall)")
|
||||
ax[1][0].set_title("Zwrot")
|
||||
|
||||
loss = data["loss"]
|
||||
ax[1][1].plot(range(1, 250), np.linspace(8.06, loss[0], 249), "r", linestyle="--")
|
||||
ax[1][1].plot(range(250, len(loss) + 250), loss, 'r')
|
||||
ax[1][1].set(xlabel="Numer epoki", ylabel="Koszt (loss)")
|
||||
ax[1][1].set_title("Koszt")
|
||||
|
||||
fig.savefig(join("metrics_plots", f"omicron_lstm.png"))
|
||||
|
||||
|
||||
def plot_metrics_for(filename, model_name):
|
||||
data = pd.read_csv(join("trained_models", filename))
|
||||
|
||||
fig, ax = plt.subplots(2, 2)
|
||||
fig.suptitle(f"Model: {model_name}")
|
||||
plt.subplots_adjust(left=0.1,
|
||||
bottom=0.1,
|
||||
right=0.9,
|
||||
top=0.9,
|
||||
wspace=0.25,
|
||||
hspace=0.8)
|
||||
fig.set_figwidth(10)
|
||||
|
||||
accuracy = data["accuracy"]
|
||||
ax[0][0].plot(range(1, len(accuracy) + 1), accuracy, 'deepskyblue')
|
||||
ax[0][0].set(xlabel="Numer epoki", ylabel="Dokładność (accuracy)")
|
||||
ax[0][0].set_title("Dokładność")
|
||||
|
||||
precision = data["precision"]
|
||||
ax[0][1].plot(range(1, len(precision) + 1), precision, 'limegreen')
|
||||
ax[0][1].set(xlabel="Numer epoki", ylabel="Precyzja (precision)")
|
||||
ax[0][1].set_title("Precyzja")
|
||||
|
||||
recall = data["recall"]
|
||||
ax[1][0].plot(range(1, len(recall) + 1), recall, 'orange')
|
||||
ax[1][0].set(xlabel="Numer epoki", ylabel="Zwrot (recall)")
|
||||
ax[1][0].set_title("Zwrot")
|
||||
|
||||
loss = data["loss"]
|
||||
perplexity = [math.e**l for l in list(loss)]
|
||||
|
||||
ax[1][1].plot(range(1, len(loss) + 1), loss, 'r', label="Koszt")
|
||||
pax = ax[1][1].twinx()
|
||||
pax.plot(range(1, len(loss) + 1), perplexity, 'm', label="Perpleksja")
|
||||
pax.set_ylabel("Perpleksja")
|
||||
pax.legend(loc="right")
|
||||
ax[1][1].set(xlabel="Numer epoki", ylabel="Koszt")
|
||||
ax[1][1].legend(loc="upper right")
|
||||
ax[1][1].set_title("Koszt")
|
||||
|
||||
fig.savefig(join("metrics_plots", f"{model_name}.png"))
|
||||
|
||||
|
||||
plot_metrics_for("default_gru.log", "default_gru")
|
||||
plot_metrics_for("beta_gru.log", "beta_gru")
|
||||
plot_metrics_for("gamma_lstm.log", "gamma_lstm")
|
||||
plot_omicron_metrics()
|
||||
plot_metrics_for("default_lstm.log", "default_lstm")
|
||||
plot_metrics_for("beta_lstm.log", "beta_lstm")
|
||||
62
RNN/quality_evaluator.py
Normal file
62
RNN/quality_evaluator.py
Normal file
@ -0,0 +1,62 @@
|
||||
import math
|
||||
|
||||
import numpy as np
|
||||
from keras import Model
|
||||
from keras.callbacks import Callback
|
||||
from keras_nlp.src.metrics import EditDistance
|
||||
from numpy.random import choice
|
||||
|
||||
|
||||
class QualityEvaluatorCallback(Callback):
|
||||
def __init__(self, validation_X, validation_y):
|
||||
super().__init__()
|
||||
self.validation_X = validation_X
|
||||
self.validation_y = validation_y
|
||||
|
||||
def on_epoch_end(self, epoch, logs=None):
|
||||
print(f"{epoch} Calculating quality metrics!")
|
||||
perplexity = logs.get("loss")**math.e
|
||||
print(f"Perplexity: {perplexity}")
|
||||
|
||||
# true_positives = {}
|
||||
# false_positives = {}
|
||||
# false_negatives = {}
|
||||
#
|
||||
# v_set_size = self.validation_X.shape[0]
|
||||
# random_ids = np.random.randint(0, v_set_size, size=100)
|
||||
# validation_X_subset = self.validation_X[random_ids]
|
||||
# validation_y_subset = self.validation_y[random_ids]
|
||||
#
|
||||
# i = 0
|
||||
# for X, label in zip(validation_X_subset, validation_y_subset):
|
||||
# prediction = self.keras_model.predict(X, verbose=0)
|
||||
#
|
||||
# predicted_idx = np.argmax(prediction)
|
||||
# valid_idx = np.argmax(label)
|
||||
#
|
||||
# if predicted_idx not in true_positives:
|
||||
# true_positives[predicted_idx] = 0
|
||||
# if predicted_idx not in false_positives:
|
||||
# false_positives[predicted_idx] = 0
|
||||
# if predicted_idx not in false_negatives:
|
||||
# false_negatives[predicted_idx] = 0
|
||||
#
|
||||
# if valid_idx not in true_positives:
|
||||
# true_positives[valid_idx] = 0
|
||||
# if valid_idx not in false_positives:
|
||||
# false_positives[valid_idx] = 0
|
||||
# if valid_idx not in false_negatives:
|
||||
# false_negatives[valid_idx] = 0
|
||||
#
|
||||
# if predicted_idx == valid_idx:
|
||||
# true_positives[predicted_idx] += 1
|
||||
# else:
|
||||
# false_positives[predicted_idx] += 1
|
||||
# false_negatives[valid_idx] += 1
|
||||
#
|
||||
# precisions = []
|
||||
# for tp, fp in zip(true_positives.values(), false_positives.values()):
|
||||
# precisions.append(tp/(tp + fp + 10e-5))
|
||||
#
|
||||
# mean_precision = np.average(precisions)
|
||||
# print(f"Mean precision: {mean_precision}")
|
||||
80
RNN/song_generator.py
Normal file
80
RNN/song_generator.py
Normal file
@ -0,0 +1,80 @@
|
||||
from random import randint
|
||||
import random
|
||||
|
||||
import keras.callbacks
|
||||
import numpy as np
|
||||
from data_processor import DataProcessor
|
||||
|
||||
|
||||
class SongGenerator:
|
||||
def __init__(self, data_processor: DataProcessor, model):
|
||||
self.data_processor = data_processor
|
||||
self.model = model
|
||||
|
||||
def sample(self, predictions, temperature=0.1):
|
||||
predictions = np.asarray(predictions).astype('float64')
|
||||
predictions = np.log(predictions) / temperature
|
||||
exp_predictions = np.exp(predictions)
|
||||
predictions = exp_predictions / np.sum(exp_predictions)
|
||||
probs = np.random.multinomial(1, predictions, 1)
|
||||
return np.argmax(probs)
|
||||
|
||||
def generate(self, tokens_per_line=6, lines=4, temp=1.0, custom_seed=None):
|
||||
result_indexes = []
|
||||
|
||||
if not custom_seed:
|
||||
seed_idx = randint(0, len(self.model.X) - 1)
|
||||
seed = self.model.X[seed_idx]
|
||||
seed = list(np.reshape(seed, (len(seed))))
|
||||
else:
|
||||
seed = custom_seed
|
||||
|
||||
for _ in range(lines * tokens_per_line):
|
||||
data_in = np.reshape(seed, (1, len(seed), 1))
|
||||
#data_in = data_in / float(self.data_processor.vocab_size())
|
||||
prediction = self.model.keras_model.predict(data_in, verbose=0)
|
||||
#out_index = np.argmax(prediction)
|
||||
|
||||
# r = random.random()
|
||||
# curr = 0.0
|
||||
# out_index = -1
|
||||
#
|
||||
# for idx, pred in sorted(enumerate(list(prediction.flatten())), reverse=True, key=lambda x: x[1]):
|
||||
# out_index = idx
|
||||
# curr += pred
|
||||
# if curr >= r:
|
||||
# break
|
||||
|
||||
out_index = self.sample(prediction[0] + 10e-5, temp)
|
||||
result_indexes.append(out_index)
|
||||
seed.append(out_index)
|
||||
seed = seed[1:len(seed)]
|
||||
|
||||
result_tokens = self.data_processor.ints_to_text(result_indexes).split(" ")
|
||||
|
||||
if self.data_processor.mode == "chars":
|
||||
raise NotImplementedError()
|
||||
|
||||
# Capitalize I words
|
||||
for i, token in enumerate(result_tokens):
|
||||
if token == "i":
|
||||
result_tokens[i] = token.capitalize()
|
||||
elif token == "i'm":
|
||||
result_tokens[i] = "I'm"
|
||||
|
||||
result = ""
|
||||
for line_idx in range(lines):
|
||||
result_tokens[line_idx*tokens_per_line] = result_tokens[line_idx*tokens_per_line].capitalize()
|
||||
result += " ".join(result_tokens[line_idx*tokens_per_line:line_idx*tokens_per_line + tokens_per_line]) \
|
||||
+ "\n"
|
||||
|
||||
return result.rstrip("\n")
|
||||
|
||||
|
||||
class GeneratorCallback(keras.callbacks.Callback):
|
||||
def __init__(self, generator):
|
||||
super().__init__()
|
||||
self.generator = generator
|
||||
|
||||
def on_epoch_end(self, epoch, logs=None):
|
||||
print(self.generator.generate())
|
||||
BIN
RNN/trained_models/beta-gru-weights.hdf5
Normal file
BIN
RNN/trained_models/beta-gru-weights.hdf5
Normal file
Binary file not shown.
BIN
RNN/trained_models/beta-lstm-weights.hdf5
Normal file
BIN
RNN/trained_models/beta-lstm-weights.hdf5
Normal file
Binary file not shown.
249
RNN/trained_models/beta_gru.log
Normal file
249
RNN/trained_models/beta_gru.log
Normal file
@ -0,0 +1,249 @@
|
||||
epoch,accuracy,loss,precision,recall,val_accuracy,val_loss,val_precision,val_recall
|
||||
0,0.04057013988494873,6.308282375335693,0.0,0.0,0.043287161737680435,6.523462295532227,0.0,0.0
|
||||
1,0.048344798386096954,5.989780902862549,0.0,0.0,0.04688332602381706,6.566540718078613,0.0,0.0
|
||||
2,0.0524480901658535,5.830344200134277,0.0,0.0,0.0443526916205883,6.663118362426758,0.0,0.0
|
||||
3,0.06198130548000336,5.676827430725098,0.0,0.0,0.05207778513431549,6.725044250488281,0.0,0.0
|
||||
4,0.07398266345262527,5.535449504852295,0.0,0.0,0.0506126806139946,6.765126705169678,0.0,0.0
|
||||
5,0.08487335592508316,5.402733325958252,0.8999999761581421,0.000277666375041008,0.04981353133916855,6.815914154052734,0.0,0.0
|
||||
6,0.09382037818431854,5.279976844787598,0.9615384340286255,0.0015425909077748656,0.044885456562042236,6.920517921447754,0.3333333432674408,0.00013319126446731389
|
||||
7,0.10526640713214874,5.16630220413208,0.9461538195610046,0.0037947737146168947,0.0490143857896328,6.962957382202148,0.3333333432674408,0.00013319126446731389
|
||||
8,0.11483047157526016,5.063782691955566,0.9146341681480408,0.0069416593760252,0.04302077740430832,7.028644561767578,0.0,0.0
|
||||
9,0.1203220933675766,4.964015007019043,0.8834356069564819,0.008885324001312256,0.04235482215881348,7.106280326843262,0.125,0.00013319126446731389
|
||||
10,0.1272946000099182,4.870832443237305,0.8774038553237915,0.01126091368496418,0.041688866913318634,7.1732072830200195,0.0,0.0
|
||||
11,0.13568629324436188,4.784560203552246,0.8975903391838074,0.013790762983262539,0.0411561019718647,7.226749897003174,0.0,0.0
|
||||
12,0.1425662487745285,4.715068817138672,0.9118165969848633,0.015950391069054604,0.04102290794253349,7.282209396362305,0.025641025975346565,0.00013319126446731389
|
||||
13,0.14765679836273193,4.652693271636963,0.8862359523773193,0.019467497244477272,0.03755993768572807,7.3396220207214355,0.0,0.0
|
||||
14,0.15462931990623474,4.591610908508301,0.8788265585899353,0.021256903186440468,0.035828448832035065,7.388875961303711,0.0,0.0
|
||||
15,0.16033689677715302,4.5268659591674805,0.8877314925193787,0.023663345724344254,0.03755993768572807,7.4465436935424805,0.0,0.0
|
||||
16,0.16632215678691864,4.481540203094482,0.893401026725769,0.02714960090816021,0.0379595085978508,7.517641544342041,0.0,0.0
|
||||
17,0.17153611779212952,4.4261651039123535,0.8858800530433655,0.02826026640832424,0.033164624124765396,7.520775318145752,0.14705882966518402,0.000665956293232739
|
||||
18,0.17554685473442078,4.37800407409668,0.8818897604942322,0.031098634004592896,0.04155567288398743,7.570293426513672,0.0,0.0
|
||||
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131,0.34507760405540466,3.106264352798462,0.8850886821746826,0.17085736989974976,0.03023441694676876,9.431594848632812,0.04907975345849991,0.002131060231477022
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132,0.3479159474372864,3.1040358543395996,0.886577844619751,0.16832752525806427,0.025039957836270332,9.43719482421875,0.03883495181798935,0.0015982951736077666
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154,0.35775768756866455,3.0307726860046387,0.8823356032371521,0.184617280960083,0.030633989721536636,9.530288696289062,0.02981029823422432,0.0014651039382442832
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155,0.3594236969947815,3.0420820713043213,0.8795796036720276,0.1807299554347992,0.028103357180953026,9.555032730102539,0.058139536529779434,0.002663825172930956
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156,0.35871410369873047,3.028592824935913,0.875261127948761,0.1809767633676529,0.02623867802321911,9.558860778808594,0.023076923564076424,0.0011987213511019945
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157,0.35834386944770813,3.0223593711853027,0.8866193294525146,0.18480239808559418,0.029834842309355736,9.583783149719238,0.03651685267686844,0.00173148640897125
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158,0.35572147369384766,3.0358448028564453,0.8788598775863647,0.18264277279376984,0.033963773399591446,9.549494743347168,0.03685503825545311,0.0019978689961135387
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159,0.3583747148513794,3.0250144004821777,0.8787744641304016,0.1840619444847107,0.03103356435894966,9.611461639404297,0.04324324429035187,0.002131060231477022
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161,0.3612748086452484,3.0214004516601562,0.8804348111152649,0.18492579460144043,0.025705913081765175,9.611003875732422,0.04657534137368202,0.0022642514668405056
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162,0.3621695041656494,3.0132954120635986,0.87606281042099,0.1875482052564621,0.026638252660632133,9.650796890258789,0.027568921446800232,0.0014651039382442832
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163,0.36466848850250244,3.004995346069336,0.87871253490448,0.18529602885246277,0.027304209768772125,9.57719898223877,0.03846153989434242,0.0022642514668405056
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164,0.3619226813316345,3.010690927505493,0.8783469200134277,0.18622158467769623,0.024640383198857307,9.642223358154297,0.039387308061122894,0.002397442702203989
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165,0.36164501309394836,3.0116920471191406,0.8829601407051086,0.18736308813095093,0.0285029299557209,9.636945724487305,0.03694581240415573,0.0019978689961135387
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166,0.3668898344039917,2.9921252727508545,0.8776323199272156,0.19029401242733002,0.030101224780082703,9.660819053649902,0.044736843556165695,0.0022642514668405056
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167,0.3679387867450714,2.9874837398529053,0.8797340393066406,0.18776416778564453,0.028902504593133926,9.669169425964355,0.049627792090177536,0.002663825172930956
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168,0.36312592029571533,3.006114959716797,0.884749710559845,0.18757905066013336,0.025039957836270332,9.643115043640137,0.010498687624931335,0.0005327650578692555
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169,0.36497700214385986,2.9970319271087646,0.8714326620101929,0.18841205537319183,0.026638252660632133,9.682051658630371,0.035175878554582596,0.0018646776443347335
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170,0.3642057180404663,2.9748990535736084,0.8779863715171814,0.1904791295528412,0.026505060493946075,9.67457103729248,0.03999999910593033,0.0022642514668405056
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171,0.36627277731895447,2.987156867980957,0.8750883340835571,0.19106531143188477,0.023175280541181564,9.691121101379395,0.013215859420597553,0.0007991475868038833
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172,0.36621108651161194,2.987300395965576,0.8806437849998474,0.19075679779052734,0.029834842309355736,9.653698921203613,0.05378973111510277,0.0029302078764885664
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173,0.3652855455875397,2.9829699993133545,0.8840187788009644,0.19165149331092834,0.028902504593133926,9.651982307434082,0.048128340393304825,0.002397442702203989
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174,0.3626014292240143,2.982612371444702,0.8785073161125183,0.19029401242733002,0.026371870189905167,9.67660140991211,0.02200488932430744,0.0011987213511019945
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175,0.36287909746170044,2.98844313621521,0.8744644522666931,0.18890568614006042,0.029701652005314827,9.676119804382324,0.025263158604502678,0.0015982951736077666
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176,0.36935797333717346,2.970700740814209,0.8790379166603088,0.19393453001976013,0.025705913081765175,9.70377254486084,0.04116222634911537,0.0022642514668405056
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177,0.3642982840538025,2.980334997177124,0.8786764740943909,0.19171319901943207,0.025705913081765175,9.715543746948242,0.026030369102954865,0.0015982951736077666
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178,0.3656557500362396,2.9910757541656494,0.8836175799369812,0.19020146131515503,0.026771442964673042,9.68774127960205,0.06435643881559372,0.0034629728179425
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179,0.36596426367759705,2.9844014644622803,0.8830009698867798,0.19535371661186218,0.023308470845222473,9.701854705810547,0.024282561615109444,0.0014651039382442832
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180,0.36726003885269165,2.972079277038574,0.8795620203018188,0.1933175027370453,0.028369739651679993,9.699377059936523,0.029885057359933853,0.00173148640897125
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181,0.36821645498275757,2.974087715148926,0.8769381046295166,0.19368772208690643,0.02876931242644787,9.72552490234375,0.043478261679410934,0.0025306339375674725
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182,0.37114736437797546,2.964077949523926,0.8818708658218384,0.19254620373249054,0.02876931242644787,9.693851470947266,0.019999999552965164,0.0011987213511019945
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183,0.37074631452560425,2.9579646587371826,0.8828059434890747,0.1945207118988037,0.02943526953458786,9.72474193572998,0.03890160098671913,0.0022642514668405056
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187,0.37398573756217957,2.9401588439941406,0.8842986822128296,0.19547712802886963,0.026371870189905167,9.726012229919434,0.03162055462598801,0.002131060231477022
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188,0.3705303370952606,2.956899642944336,0.8788685202598572,0.1936260163784027,0.026505060493946075,9.75719928741455,0.02163461595773697,0.0011987213511019945
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198,0.371610164642334,2.9460935592651367,0.8801539540290833,0.19757504761219025,0.02517314814031124,9.77607536315918,0.03378378227353096,0.0019978689961135387
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|
||||
201,0.3754357695579529,2.919313669204712,0.8799514770507812,0.20149323344230652,0.027437400072813034,9.834790229797363,0.011210761964321136,0.000665956293232739
|
||||
202,0.37685495615005493,2.9179022312164307,0.877814769744873,0.20325177907943726,0.02517314814031124,9.795039176940918,0.024444444105029106,0.0014651039382442832
|
||||
203,0.37123993039131165,2.923346757888794,0.876804769039154,0.20047511160373688,0.029035694897174835,9.844982147216797,0.020533880218863487,0.001331912586465478
|
||||
204,0.3749421536922455,2.9269416332244873,0.8807290196418762,0.20275814831256866,0.0253063403069973,9.854016304016113,0.012345679104328156,0.0007991475868038833
|
||||
205,0.37528151273727417,2.92122220993042,0.8768656849861145,0.20300497114658356,0.023841235786676407,9.81929874420166,0.020964359864592552,0.001331912586465478
|
||||
206,0.377379447221756,2.9118990898132324,0.8801185488700867,0.2015857845544815,0.025839105248451233,9.84094524383545,0.026859503239393234,0.00173148640897125
|
||||
207,0.3718261122703552,2.9299206733703613,0.8712343573570251,0.20164749026298523,0.02623867802321911,9.836934089660645,0.020202020183205605,0.001331912586465478
|
||||
208,0.3759911060333252,2.9161376953125,0.8818401098251343,0.20285071432590485,0.030101224780082703,9.85545539855957,0.025404157117009163,0.0014651039382442832
|
||||
209,0.377194344997406,2.9018805027008057,0.874492347240448,0.20593589544296265,0.025039957836270332,9.859103202819824,0.02783725969493389,0.00173148640897125
|
||||
210,0.37438681721687317,2.9137322902679443,0.8780094385147095,0.20140066742897034,0.024640383198857307,9.863907814025879,0.03397027775645256,0.002131060231477022
|
||||
211,0.3769475221633911,2.9067561626434326,0.8805292248725891,0.20328263938426971,0.027304209768772125,9.825936317443848,0.0258620698004961,0.0015982951736077666
|
||||
212,0.377379447221756,2.9134140014648438,0.8803088665008545,0.20399223268032074,0.02264251373708248,9.89885139465332,0.01965065486729145,0.0011987213511019945
|
||||
213,0.3765464425086975,2.9112324714660645,0.8789108395576477,0.20713910460472107,0.024107618257403374,9.858063697814941,0.029885057359933853,0.00173148640897125
|
||||
214,0.3773486018180847,2.90181040763855,0.8788481950759888,0.20433159172534943,0.026638252660632133,9.864870071411133,0.026431718841195107,0.0015982951736077666
|
||||
215,0.3819763660430908,2.8954432010650635,0.8778160810470581,0.20436245203018188,0.02597229555249214,9.86173152923584,0.03478260710835457,0.002131060231477022
|
||||
216,0.3803103566169739,2.8964970111846924,0.8803777694702148,0.20707741379737854,0.025705913081765175,9.845916748046875,0.028865979984402657,0.0018646776443347335
|
||||
217,0.3778730630874634,2.891540288925171,0.8808736205101013,0.2053188532590866,0.027570592239499092,9.921723365783691,0.02766798436641693,0.0018646776443347335
|
||||
218,0.38139018416404724,2.8964107036590576,0.8767796754837036,0.208990216255188,0.023841235786676407,9.918452262878418,0.02469135820865631,0.0018646776443347335
|
||||
219,0.3762379288673401,2.9156336784362793,0.8719623684883118,0.2059050351381302,0.025839105248451233,9.937589645385742,0.035940803587436676,0.0022642514668405056
|
||||
220,0.38040292263031006,2.889655351638794,0.8777865171432495,0.20652207732200623,0.02517314814031124,9.892942428588867,0.035164836794137955,0.002131060231477022
|
||||
221,0.3771017789840698,2.899684429168701,0.8778274655342102,0.20593589544296265,0.025572722777724266,9.906599998474121,0.026915114372968674,0.00173148640897125
|
||||
222,0.3810199499130249,2.8883702754974365,0.8790794610977173,0.20858915150165558,0.028369739651679993,9.923462867736816,0.03097345121204853,0.0018646776443347335
|
||||
223,0.37870606780052185,2.8945255279541016,0.8794002532958984,0.208095520734787,0.024107618257403374,9.873822212219238,0.021739130839705467,0.0014651039382442832
|
||||
224,0.38040292263031006,2.895768880844116,0.876369297504425,0.20732422173023224,0.023974427953362465,9.87736701965332,0.03913894295692444,0.002663825172930956
|
||||
225,0.3786752223968506,2.8927671909332275,0.8768201470375061,0.2062135636806488,0.027437400072813034,9.908354759216309,0.03232323378324509,0.002131060231477022
|
||||
226,0.3795699179172516,2.8902108669281006,0.8735347986221313,0.20692314207553864,0.025705913081765175,9.885969161987305,0.049438200891017914,0.0029302078764885664
|
||||
227,0.3779964745044708,2.8808953762054443,0.8793215751647949,0.20794126391410828,0.02783697471022606,9.944091796875,0.04148471727967262,0.0025306339375674725
|
||||
228,0.3829019367694855,2.869015693664551,0.8798404932022095,0.21099558472633362,0.029302077367901802,9.89219856262207,0.04324324429035187,0.0031965903472155333
|
||||
229,0.3819763660430908,2.8921844959259033,0.8738657236099243,0.20797210931777954,0.029035694897174835,9.930909156799316,0.04015296325087547,0.0027970164082944393
|
||||
230,0.3780273199081421,2.885507106781006,0.8700696229934692,0.2082497775554657,0.02437400072813034,9.918914794921875,0.044834308326244354,0.00306339911185205
|
||||
231,0.38145187497138977,2.8772995471954346,0.8745178580284119,0.20985406637191772,0.02437400072813034,9.958950996398926,0.026258205994963646,0.0015982951736077666
|
||||
232,0.38145187497138977,2.8743865489959717,0.8775483965873718,0.2084965854883194,0.029035694897174835,9.937262535095215,0.0346938781440258,0.0022642514668405056
|
||||
233,0.38163700699806213,2.878633975982666,0.8716418147087097,0.2072008103132248,0.030101224780082703,9.91358470916748,0.02626262605190277,0.00173148640897125
|
||||
234,0.38552433252334595,2.868713617324829,0.8769861459732056,0.21114984154701233,0.028103357180953026,9.980724334716797,0.03505154699087143,0.0022642514668405056
|
||||
235,0.38203805685043335,2.8674991130828857,0.8774834275245667,0.21256902813911438,0.028902504593133926,9.967602729797363,0.03266787528991699,0.002397442702203989
|
||||
236,0.3826551139354706,2.872073173522949,0.8720318078994751,0.20960725843906403,0.023175280541181564,9.985732078552246,0.03442623093724251,0.0027970164082944393
|
||||
237,0.382254034280777,2.871565341949463,0.8807672262191772,0.20966896414756775,0.024906765669584274,9.982723236083984,0.02976190485060215,0.0019978689961135387
|
||||
238,0.3854934871196747,2.857140302658081,0.8780519366264343,0.21081048250198364,0.024773575365543365,10.014907836914062,0.03490401431918144,0.002663825172930956
|
||||
239,0.38348811864852905,2.8707897663116455,0.8763488531112671,0.21297010779380798,0.028103357180953026,9.963903427124023,0.022357722744345665,0.0014651039382442832
|
||||
240,0.38561686873435974,2.859131097793579,0.8738601803779602,0.2128775417804718,0.02783697471022606,9.995795249938965,0.03059273399412632,0.002131060231477022
|
||||
241,0.38179126381874084,2.8637142181396484,0.8788728713989258,0.21266157925128937,0.0285029299557209,10.014219284057617,0.028409091755747795,0.0019978689961135387
|
||||
242,0.3824082911014557,2.8598289489746094,0.8739795684814453,0.21139666438102722,0.02956845983862877,9.974042892456055,0.039923954755067825,0.0027970164082944393
|
||||
243,0.3844445049762726,2.8568966388702393,0.8701429963111877,0.21210625767707825,0.023308470845222473,9.968132972717285,0.03598484769463539,0.0025306339375674725
|
||||
244,0.38493815064430237,2.8486239910125732,0.8785200119018555,0.21463610231876373,0.02783697471022606,10.008865356445312,0.03307392820715904,0.0022642514668405056
|
||||
245,0.3832104504108429,2.868046283721924,0.8742761611938477,0.20960725843906403,0.02876931242644787,10.027926445007324,0.025390625,0.00173148640897125
|
||||
246,0.38789990544319153,2.852921962738037,0.8706536889076233,0.21244561672210693,0.02437400072813034,9.999215126037598,0.03787878900766373,0.002663825172930956
|
||||
247,0.3868817985057831,2.850252628326416,0.8762965798377991,0.21111899614334106,0.029302077367901802,9.980339050292969,0.01964285783469677,0.0014651039382442832
|
||||
235
RNN/trained_models/beta_lstm.log
Normal file
235
RNN/trained_models/beta_lstm.log
Normal file
@ -0,0 +1,235 @@
|
||||
epoch,accuracy,loss,precision,recall,val_accuracy,val_loss,val_precision,val_recall
|
||||
0,0.04072440043091774,6.2866902351379395,0.0,0.0,0.045151837170124054,6.586356163024902,0.0,0.0
|
||||
1,0.045907504856586456,6.042308807373047,0.0,0.0,0.04448588192462921,6.65412712097168,0.0,0.0
|
||||
2,0.04951716959476471,5.945173263549805,0.0,0.0,0.044086307287216187,6.691050052642822,0.0,0.0
|
||||
3,0.050226759165525436,5.857245445251465,0.0,0.0,0.0443526916205883,6.752647399902344,0.0,0.0
|
||||
4,0.053620461374521255,5.7692975997924805,0.0,0.0,0.03875865787267685,6.847846984863281,0.0,0.0
|
||||
5,0.05892697349190712,5.6876020431518555,0.0,0.0,0.03862546756863594,6.928927898406982,0.0,0.0
|
||||
6,0.06154937669634819,5.6169915199279785,1.0,9.255545592168346e-05,0.03343100845813751,6.971290111541748,0.0,0.0
|
||||
7,0.06929318606853485,5.533843040466309,1.0,0.0003393700171727687,0.03502930328249931,7.014708042144775,0.0,0.0
|
||||
8,0.0759880319237709,5.44597053527832,0.9347826242446899,0.0013266282621771097,0.03529568389058113,7.1202216148376465,0.0,0.0
|
||||
9,0.08178817480802536,5.365095615386963,0.9696969985961914,0.0019745163153856993,0.032365478575229645,7.175212383270264,0.0,0.0
|
||||
10,0.08913090080022812,5.283218860626221,0.918181836605072,0.003116033738479018,0.03982418775558472,7.22043514251709,0.0,0.0
|
||||
11,0.09456082433462143,5.21270751953125,0.9197530746459961,0.004596921149641275,0.03516249358654022,7.332255840301514,0.0,0.0
|
||||
12,0.10184185206890106,5.138445854187012,0.9009901285171509,0.0056150308810174465,0.029168887063860893,7.394521236419678,0.0,0.0
|
||||
13,0.10628451406955719,5.067190170288086,0.9015747904777527,0.00706506660208106,0.02943526953458786,7.530158519744873,0.0,0.0
|
||||
14,0.11433684080839157,5.012109756469727,0.9111111164093018,0.008854472078382969,0.03183271363377571,7.564946174621582,0.0,0.0
|
||||
15,0.11840928345918655,4.939283847808838,0.9093137383460999,0.011446024291217327,0.02943526953458786,7.66089391708374,0.0,0.0
|
||||
16,0.1231604591012001,4.8694868087768555,0.9061833620071411,0.013112022541463375,0.0285029299557209,7.777390956878662,0.0,0.0
|
||||
17,0.12800419330596924,4.820078372955322,0.8931159377098083,0.015209946781396866,0.030900372192263603,7.806278705596924,0.0,0.0
|
||||
18,0.13121278584003448,4.767286777496338,0.8977272510528564,0.017061056569218636,0.022908898070454597,7.940349102020264,0.0,0.0
|
||||
19,0.13901829719543457,4.7145490646362305,0.8774929046630859,0.01900472119450569,0.02623867802321911,8.037461280822754,0.0,0.0
|
||||
20,0.14056088030338287,4.6607770919799805,0.8878504633903503,0.02051645889878273,0.02770378254354,8.154940605163574,0.0,0.0
|
||||
21,0.14571313560009003,4.621826648712158,0.8775510191917419,0.02255268022418022,0.023708045482635498,8.240988731384277,0.0,0.0
|
||||
22,0.15209946036338806,4.570230484008789,0.8622950911521912,0.024342084303498268,0.023708045482635498,8.332744598388672,0.024390242993831635,0.00026638252893462777
|
||||
23,0.1571900099515915,4.529275417327881,0.8608095049858093,0.026902785524725914,0.020644646137952805,8.455666542053223,0.0,0.0
|
||||
24,0.15743683278560638,4.496821880340576,0.8834170699119568,0.027118748053908348,0.02597229555249214,8.469337463378906,0.0,0.0
|
||||
25,0.16468700766563416,4.448680877685547,0.865486741065979,0.030173078179359436,0.0245071928948164,8.610626220703125,0.0,0.0
|
||||
26,0.17005522549152374,4.408073425292969,0.879239022731781,0.032795485109090805,0.026505060493946075,8.65352725982666,0.03703703731298447,0.0005327650578692555
|
||||
27,0.16888286173343658,4.3816070556640625,0.8744971752166748,0.033535927534103394,0.025039957836270332,8.786849021911621,0.01886792480945587,0.00026638252893462777
|
||||
28,0.17647239565849304,4.335326194763184,0.8754525780677795,0.03729984909296036,0.024906765669584274,8.827933311462402,0.019801979884505272,0.00026638252893462777
|
||||
29,0.1802363246679306,4.296008110046387,0.8871539831161499,0.0385647751390934,0.023042088374495506,8.850105285644531,0.00826446246355772,0.00013319126446731389
|
||||
30,0.18458643555641174,4.2803192138671875,0.8760942816734314,0.04013821482658386,0.021443793550133705,8.921741485595703,0.0,0.0
|
||||
31,0.18936845660209656,4.232756614685059,0.8723404407501221,0.04300743713974953,0.023175280541181564,9.057310104370117,0.0,0.0
|
||||
32,0.19035571813583374,4.209996223449707,0.8667068481445312,0.04433406516909599,0.02357485331594944,9.14242935180664,0.03305784985423088,0.0005327650578692555
|
||||
33,0.19529201090335846,4.184617519378662,0.8780767321586609,0.04732669144868851,0.020777836441993713,9.188912391662598,0.0,0.0
|
||||
34,0.1972973793745041,4.157787799835205,0.8684210777282715,0.04785117134451866,0.025572722777724266,9.15644359588623,0.018518518656492233,0.00026638252893462777
|
||||
35,0.20275814831256866,4.121598720550537,0.8803278803825378,0.04970227926969528,0.02024507150053978,9.334010124206543,0.006410256493836641,0.00013319126446731389
|
||||
36,0.2077561467885971,4.089554786682129,0.8782475590705872,0.05318853631615639,0.0245071928948164,9.351238250732422,0.012578615918755531,0.00026638252893462777
|
||||
37,0.21176688373088837,4.059149742126465,0.8808574080467224,0.05451516434550285,0.024640383198857307,9.436446189880371,0.03550295904278755,0.0007991475868038833
|
||||
38,0.21031685173511505,4.055099964141846,0.871367335319519,0.0564279779791832,0.0261054877191782,9.558694839477539,0.023668639361858368,0.0005327650578692555
|
||||
39,0.21485206484794617,4.024014472961426,0.8849021196365356,0.05858760327100754,0.02597229555249214,9.559093475341797,0.012578615918755531,0.00026638252893462777
|
||||
40,0.21843087673187256,4.0041704177856445,0.8694080710411072,0.05935889855027199,0.0261054877191782,9.602422714233398,0.026490066200494766,0.0005327650578692555
|
||||
41,0.22086817026138306,3.975396156311035,0.874096155166626,0.06340048462152481,0.02197655849158764,9.617487907409668,0.03658536449074745,0.0007991475868038833
|
||||
42,0.22336716949939728,3.962003231048584,0.8808201551437378,0.06361644715070724,0.02104421891272068,9.688793182373047,0.012500000186264515,0.00026638252893462777
|
||||
43,0.22663745284080505,3.925198793411255,0.8799837231636047,0.0667324811220169,0.020511453971266747,9.744327545166016,0.032608695328235626,0.0007991475868038833
|
||||
44,0.2300928682088852,3.90729022026062,0.8795507550239563,0.070064477622509,0.022109748795628548,9.821006774902344,0.020304568111896515,0.0005327650578692555
|
||||
45,0.234072744846344,3.8793044090270996,0.8797835111618042,0.07021874189376831,0.023042088374495506,9.893169403076172,0.019512195140123367,0.0005327650578692555
|
||||
46,0.23339401185512543,3.875826597213745,0.8538056015968323,0.06991022080183029,0.020511453971266747,9.938312530517578,0.005681818351149559,0.00013319126446731389
|
||||
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100,0.3313176929950714,3.1767730712890625,0.8759236931800842,0.15725171566009521,0.019046351313591003,11.698598861694336,0.020930232480168343,0.0011987213511019945
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102,0.3307006359100342,3.1682076454162598,0.8676370978355408,0.1581464260816574,0.01598295196890831,11.728557586669922,0.021791767328977585,0.0011987213511019945
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104,0.33372411131858826,3.1435840129852295,0.8765329718589783,0.16317526996135712,0.01851358637213707,11.766417503356934,0.020964359864592552,0.001331912586465478
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105,0.33646994829177856,3.150479316711426,0.8687468767166138,0.16234226524829865,0.01771443709731102,11.865253448486328,0.015841584652662277,0.001065530115738511
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106,0.33625397086143494,3.1451525688171387,0.8743007779121399,0.16394656896591187,0.017181672155857086,11.826273918151855,0.015841584652662277,0.001065530115738511
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107,0.3399253487586975,3.1249592304229736,0.87761390209198,0.16703175008296967,0.017448054626584053,11.898265838623047,0.01411290280520916,0.0009323388221673667
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108,0.3440903425216675,3.0972087383270264,0.8786228895187378,0.169283926486969,0.01611614227294922,11.905926704406738,0.008179958909749985,0.0005327650578692555
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111,0.3393700122833252,3.109830617904663,0.8684879541397095,0.16869774460792542,0.016249334439635277,11.913370132446289,0.008064515888690948,0.0005327650578692555
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114,0.3452318608760834,3.0777909755706787,0.870553195476532,0.1713818460702896,0.018247203901410103,12.045138359069824,0.011320754885673523,0.0007991475868038833
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117,0.34483078122138977,3.0699918270111084,0.8751935362815857,0.17437447607517242,0.020511453971266747,12.037976264953613,0.016574585810303688,0.0011987213511019945
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119,0.351402223110199,3.0371615886688232,0.8708713054656982,0.17977355420589447,0.017448054626584053,12.109549522399902,0.01200686115771532,0.0009323388221673667
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120,0.3542405962944031,3.033348560333252,0.8696498274803162,0.17927992343902588,0.018913159146904945,12.140617370605469,0.02023608796298504,0.0015982951736077666
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121,0.35004472732543945,3.0369207859039307,0.8722347617149353,0.17881713807582855,0.018913159146904945,12.202795028686523,0.022304832935333252,0.0015982951736077666
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123,0.35547465085983276,3.0158157348632812,0.8738791942596436,0.1834140568971634,0.017847629263997078,12.206542015075684,0.025974025949835777,0.0018646776443347335
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227,0.4205719828605652,2.58320951461792,0.8692920207977295,0.2560701072216034,0.01758124679327011,13.885247230529785,0.013786764815449715,0.0019978689961135387
|
||||
228,0.4269891679286957,2.5505380630493164,0.8619298934936523,0.2571190595626831,0.019179541617631912,13.899367332458496,0.020758122205734253,0.00306339911185205
|
||||
229,0.4252614676952362,2.569561719894409,0.8655522465705872,0.2603893578052521,0.018114011734724045,14.009458541870117,0.024096384644508362,0.003729355288669467
|
||||
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|
||||
231,0.4270508885383606,2.56195330619812,0.8649235367774963,0.2581988573074341,0.017048481851816177,14.03003215789795,0.017683465033769608,0.002663825172930956
|
||||
232,0.4229784309864044,2.558168888092041,0.8615384697914124,0.26261067390441895,0.017181672155857086,13.984004974365234,0.01878354139626026,0.0027970164082944393
|
||||
233,0.42473697662353516,2.5677125453948975,0.8593429327011108,0.25822973251342773,0.016515716910362244,14.019777297973633,0.022355975583195686,0.0034629728179425
|
||||
BIN
RNN/trained_models/default-gru-weights.hdf5
Normal file
BIN
RNN/trained_models/default-gru-weights.hdf5
Normal file
Binary file not shown.
BIN
RNN/trained_models/default-lstm-weights.hdf5
Normal file
BIN
RNN/trained_models/default-lstm-weights.hdf5
Normal file
Binary file not shown.
107
RNN/trained_models/default_gru.log
Normal file
107
RNN/trained_models/default_gru.log
Normal file
@ -0,0 +1,107 @@
|
||||
epoch,accuracy,loss,precision,recall
|
||||
0,0.0401073656976223,6.305737495422363,0.0,0.0
|
||||
1,0.0471724309027195,5.979435920715332,0.0,0.0
|
||||
2,0.05679819732904434,5.774374008178711,0.0,0.0
|
||||
3,0.07540184259414673,5.5467963218688965,1.0,0.0003085181815549731
|
||||
4,0.0953938215970993,5.326295852661133,0.9224137663841248,0.003301144577562809
|
||||
5,0.11156017333269119,5.125314235687256,0.9395973086357117,0.008638509549200535
|
||||
6,0.1275414228439331,4.947536945343018,0.8872340321540833,0.012865208089351654
|
||||
7,0.1402832269668579,4.771039962768555,0.8955650925636292,0.019313238561153412
|
||||
8,0.1541048288345337,4.634401798248291,0.9005848169326782,0.02375590056180954
|
||||
9,0.16832752525806427,4.4993414878845215,0.893048107624054,0.030913522467017174
|
||||
10,0.17952673137187958,4.38805627822876,0.8911042809486389,0.035849813371896744
|
||||
11,0.1915280967950821,4.269601345062256,0.8790269494056702,0.041248880326747894
|
||||
12,0.20420819520950317,4.161935329437256,0.868681013584137,0.04612346738576889
|
||||
13,0.21574677526950836,4.0765910148620605,0.8845381736755371,0.054360903799533844
|
||||
14,0.22580446302890778,4.005147933959961,0.8859527111053467,0.05895782634615898
|
||||
15,0.23580044507980347,3.914696455001831,0.8824734091758728,0.06648566573858261
|
||||
16,0.24968376755714417,3.826213836669922,0.8833755850791931,0.07524758577346802
|
||||
17,0.2564711570739746,3.768400192260742,0.8837209343910217,0.07854872941970825
|
||||
18,0.26390644907951355,3.715920925140381,0.8791243433952332,0.0854903906583786
|
||||
19,0.268719345331192,3.66813063621521,0.8809801340103149,0.09317249059677124
|
||||
20,0.2794249355792999,3.6059670448303223,0.8879820108413696,0.09733748435974121
|
||||
21,0.28445377945899963,3.561427593231201,0.8979266285896301,0.10421744734048843
|
||||
22,0.2859655022621155,3.5156397819519043,0.8829629421234131,0.11032610386610031
|
||||
23,0.2970104515552521,3.475023031234741,0.887202262878418,0.11720605939626694
|
||||
24,0.30117544531822205,3.438606023788452,0.8903358578681946,0.12022953480482101
|
||||
25,0.30734580755233765,3.3970675468444824,0.8926955461502075,0.12781909108161926
|
||||
26,0.31117144227027893,3.3613216876983643,0.8868449330329895,0.12791164219379425
|
||||
27,0.3193163275718689,3.324481248855591,0.8846942782402039,0.13303303718566895
|
||||
28,0.3184216320514679,3.2933309078216553,0.8885375261306763,0.13870978355407715
|
||||
29,0.3261962831020355,3.2731363773345947,0.8854002952575684,0.13920339941978455
|
||||
30,0.3284793198108673,3.2447049617767334,0.8876784443855286,0.1458057016134262
|
||||
31,0.32943570613861084,3.2223074436187744,0.8868518471717834,0.14774936437606812
|
||||
32,0.3356986343860626,3.195056200027466,0.8882701396942139,0.1525622457265854
|
||||
33,0.34044981002807617,3.171229124069214,0.8921893835067749,0.15752938389778137
|
||||
34,0.3417455852031708,3.1457228660583496,0.8908651471138,0.15916453301906586
|
||||
35,0.3429179787635803,3.1181530952453613,0.8883618712425232,0.16178694367408752
|
||||
36,0.3511245548725128,3.088674545288086,0.883190393447876,0.16842007637023926
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||||
37,0.35124796628952026,3.080048084259033,0.886599600315094,0.16860519349575043
|
||||
38,0.3557831645011902,3.061220645904541,0.8836915493011475,0.1725233644247055
|
||||
39,0.35760343074798584,3.038451671600342,0.8848223686218262,0.17752136290073395
|
||||
40,0.36010241508483887,3.03027606010437,0.8774360418319702,0.1777990311384201
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||||
41,0.36312592029571533,3.0046699047088623,0.8838651180267334,0.1803288757801056
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||||
42,0.36596426367759705,2.990769386291504,0.8792750835418701,0.1856045424938202
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||||
43,0.3660876750946045,2.975689649581909,0.8857308626174927,0.1884429156780243
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||||
44,0.36935797333717346,2.964508056640625,0.8845002055168152,0.1913738250732422
|
||||
45,0.37222719192504883,2.9486632347106934,0.8850492835044861,0.19121956825256348
|
||||
46,0.37472617626190186,2.9287517070770264,0.8821819424629211,0.19658778607845306
|
||||
47,0.37611451745033264,2.9111037254333496,0.8772677183151245,0.19692716002464294
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||||
48,0.37666985392570496,2.9034407138824463,0.8786143064498901,0.1987474113702774
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||||
49,0.38367322087287903,2.8815035820007324,0.8766975998878479,0.20115385949611664
|
||||
50,0.3846913278102875,2.8655447959899902,0.8832359313964844,0.20513373613357544
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||||
51,0.38219234347343445,2.8838205337524414,0.8751316070556641,0.20519544184207916
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||||
52,0.38533923029899597,2.860194206237793,0.8760666847229004,0.20587418973445892
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||||
53,0.38546261191368103,2.848552942276001,0.8769170045852661,0.20639866590499878
|
||||
54,0.38941165804862976,2.825244903564453,0.8791741132736206,0.21281585097312927
|
||||
55,0.3919106423854828,2.8079307079315186,0.8773525357246399,0.21429672837257385
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||||
56,0.39095425605773926,2.8213083744049072,0.8739827275276184,0.21204455196857452
|
||||
57,0.39274364709854126,2.795447587966919,0.8750764727592468,0.22065220773220062
|
||||
58,0.3920648992061615,2.7954206466674805,0.8718799948692322,0.21877025067806244
|
||||
59,0.3961990475654602,2.776341438293457,0.8785445094108582,0.22271928191184998
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||||
60,0.39950019121170044,2.775493860244751,0.8797029256820679,0.22290438413619995
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||||
61,0.3961373567581177,2.7636666297912598,0.8674741983413696,0.2253725379705429
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||||
62,0.40224599838256836,2.7508745193481445,0.8729295134544373,0.22762471437454224
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||||
63,0.3975565433502197,2.754347562789917,0.8745553493499756,0.2275630086660385
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||||
64,0.4061024785041809,2.7297604084014893,0.8774985671043396,0.23160460591316223
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||||
65,0.40101194381713867,2.739513397216797,0.8720617294311523,0.23006200790405273
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||||
66,0.4047450125217438,2.727776288986206,0.8743188381195068,0.23265355825424194
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||||
67,0.40742912888526917,2.7078909873962402,0.876331627368927,0.23348656296730042
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||||
68,0.4113781452178955,2.695284843444824,0.8737974166870117,0.23817604780197144
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||||
69,0.4099898040294647,2.7012276649475098,0.8692611455917358,0.23774410784244537
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||||
70,0.40702804923057556,2.6924970149993896,0.8665012717247009,0.23709622025489807
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||||
71,0.41214945912361145,2.68475604057312,0.8766770958900452,0.24190911650657654
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||||
72,0.4127047657966614,2.671207904815674,0.875207781791687,0.243636816740036
|
||||
73,0.41264307498931885,2.666658639907837,0.8700757026672363,0.244624063372612
|
||||
74,0.4172091484069824,2.6568655967712402,0.8696619272232056,0.24126122891902924
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||||
75,0.4199241101741791,2.6349399089813232,0.8681011199951172,0.24894332885742188
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||||
76,0.41801127791404724,2.6491317749023438,0.8710688352584839,0.24866566061973572
|
||||
77,0.4212198853492737,2.625572919845581,0.8702511191368103,0.25017738342285156
|
||||
78,0.4161601960659027,2.6460413932800293,0.8652051091194153,0.24595069885253906
|
||||
79,0.4200475215911865,2.62778639793396,0.8733354806900024,0.25292322039604187
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||||
80,0.4206337034702301,2.6222565174102783,0.871833086013794,0.252676397562027
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||||
81,0.4209730625152588,2.62742018699646,0.8704767823219299,0.2523370385169983
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||||
82,0.42180606722831726,2.6059658527374268,0.8711695671081543,0.2534785568714142
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||||
83,0.4221145808696747,2.610149621963501,0.8635879755020142,0.2503933608531952
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||||
84,0.4221763014793396,2.604743003845215,0.8661792874336243,0.25221362709999084
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||||
85,0.42393484711647034,2.5938525199890137,0.8695288896560669,0.2568105459213257
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||||
86,0.4243050515651703,2.5988290309906006,0.8645100593566895,0.2539413273334503
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||||
87,0.4240582585334778,2.5954203605651855,0.8666118383407593,0.26017338037490845
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||||
88,0.4297966957092285,2.5743813514709473,0.8677226901054382,0.26026594638824463
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||||
89,0.4256625473499298,2.5844502449035645,0.8688827157020569,0.2612840533256531
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||||
90,0.43130841851234436,2.5662851333618164,0.8650592565536499,0.2634436786174774
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||||
91,0.43056797981262207,2.568209648132324,0.8652037382125854,0.26396816968917847
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||||
92,0.43180206418037415,2.552396059036255,0.8651458621025085,0.2644309401512146
|
||||
93,0.43189460039138794,2.561032772064209,0.8597230315208435,0.2643383741378784
|
||||
94,0.43251165747642517,2.5485799312591553,0.8668067455291748,0.26723846793174744
|
||||
95,0.43106162548065186,2.548239231109619,0.8629729151725769,0.26813316345214844
|
||||
96,0.4368000626564026,2.5391900539398193,0.8631017208099365,0.2693980932235718
|
||||
97,0.4361213147640228,2.5466911792755127,0.8654338121414185,0.27143430709838867
|
||||
98,0.43902137875556946,2.5098068714141846,0.8669796586036682,0.27347052097320557
|
||||
99,0.44111931324005127,2.51949405670166,0.8697875738143921,0.2728534936904907
|
||||
100,0.43698516488075256,2.521623373031616,0.863104522228241,0.2731002867221832
|
||||
101,0.4400394856929779,2.5074939727783203,0.8638388514518738,0.27519822120666504
|
||||
102,0.43895968794822693,2.5122082233428955,0.8608426451683044,0.2748279869556427
|
||||
103,0.4388362765312195,2.507242202758789,0.8603842854499817,0.2749205529689789
|
||||
104,0.44469812512397766,2.501905918121338,0.8589928150177002,0.2762780487537384
|
||||
105,0.4389288127422333,2.507836103439331,0.862544596195221,0.2758769690990448
|
||||
513
RNN/trained_models/default_lstm.log
Normal file
513
RNN/trained_models/default_lstm.log
Normal file
@ -0,0 +1,513 @@
|
||||
epoch,accuracy,loss,precision,recall,val_accuracy,val_loss,val_precision,val_recall
|
||||
0,0.04368617385625839,6.254223346710205,0.0,0.0,0.050213105976581573,6.510298252105713,0.0,0.0
|
||||
1,0.04754265397787094,5.950713634490967,0.0,0.0,0.04874800145626068,6.637467861175537,0.0,0.0
|
||||
2,0.05297257378697395,5.7926788330078125,0.0,0.0,0.045817796140909195,6.700255870819092,0.0,0.0
|
||||
3,0.06121000647544861,5.639612197875977,0.0,0.0,0.05141182616353035,6.730255126953125,0.0,0.0
|
||||
4,0.07318051159381866,5.495490550994873,0.75,0.00018511091184336692,0.04022376239299774,6.826550006866455,0.0,0.0
|
||||
5,0.0850893184542656,5.354386329650879,0.9508196711540222,0.0017894054763019085,0.045151837170124054,6.8647379875183105,0.0,0.0
|
||||
6,0.09801622480154037,5.216432571411133,0.9324324131011963,0.0042575509287416935,0.04395311698317528,6.958686351776123,0.0,0.0
|
||||
7,0.10890691727399826,5.084468364715576,0.9372385144233704,0.006910807453095913,0.03982418775558472,7.07486629486084,0.0,0.0
|
||||
8,0.1200752779841423,4.963571548461914,0.9131736755371094,0.009409804828464985,0.046750131994485855,7.1421098709106445,0.0,0.0
|
||||
9,0.12889890372753143,4.851385116577148,0.9197247624397278,0.012371579185128212,0.045950986444950104,7.187646389007568,0.029411764815449715,0.00013319126446731389
|
||||
10,0.13796932995319366,4.739042282104492,0.9033392071723938,0.015857834368944168,0.046616941690444946,7.30228328704834,0.05882352963089943,0.00026638252893462777
|
||||
11,0.14981643855571747,4.636832237243652,0.8919288516044617,0.020115386694669724,0.04688332602381706,7.367163181304932,0.04444444552063942,0.00026638252893462777
|
||||
12,0.1573442816734314,4.534491062164307,0.8934516906738281,0.02483571320772171,0.04368673264980316,7.425047874450684,0.04444444552063942,0.00026638252893462777
|
||||
13,0.16715514659881592,4.442898750305176,0.9059907793998718,0.030327338725328445,0.04368673264980316,7.5351338386535645,0.033898305147886276,0.00026638252893462777
|
||||
14,0.17931076884269714,4.34983491897583,0.8825422525405884,0.033844444900751114,0.0427543967962265,7.647549152374268,0.10638298094272614,0.000665956293232739
|
||||
15,0.1864066869020462,4.276167392730713,0.8853046298027039,0.03810199722647667,0.044885456562042236,7.738982200622559,0.08571428805589676,0.0007991475868038833
|
||||
16,0.19563138484954834,4.1977620124816895,0.8891001343727112,0.043285101652145386,0.03729355335235596,7.8362627029418945,0.12765957415103912,0.0007991475868038833
|
||||
17,0.20266559720039368,4.1283674240112305,0.876173734664917,0.046061765402555466,0.046616941690444946,7.911702632904053,0.0923076942563057,0.0007991475868038833
|
||||
18,0.21232222020626068,4.056253433227539,0.8869333267211914,0.051306575536727905,0.039957378059625626,8.013676643371582,0.07608695328235626,0.0009323388221673667
|
||||
19,0.2211458384990692,3.985872507095337,0.89794921875,0.05673649534583092,0.04195524752140045,8.100364685058594,0.06422018259763718,0.0009323388221673667
|
||||
20,0.23070989549160004,3.91831374168396,0.8903312683105469,0.06136426702141762,0.04235482215881348,8.187294960021973,0.0555555559694767,0.0007991475868038833
|
||||
21,0.24008885025978088,3.8661394119262695,0.8971126675605774,0.06805910915136337,0.03716036304831505,8.291644096374512,0.04901960864663124,0.000665956293232739
|
||||
22,0.24598154425621033,3.8198132514953613,0.8945711851119995,0.07015703618526459,0.03929142281413078,8.36000919342041,0.12612612545490265,0.0018646776443347335
|
||||
23,0.2521827518939972,3.760848045349121,0.887721836566925,0.07561780512332916,0.03969099745154381,8.442827224731445,0.0555555559694767,0.0007991475868038833
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||||
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91,0.47548821568489075,2.3248167037963867,0.8929833173751831,0.28858789801597595,0.028369739651679993,11.399109840393066,0.03653585910797119,0.0035961640533059835
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92,0.48122668266296387,2.312066078186035,0.8981261253356934,0.2913028597831726,0.029168887063860893,11.46032428741455,0.061744965612888336,0.0061267982237041
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93,0.4791595935821533,2.2946841716766357,0.9015777707099915,0.2961774468421936,0.031699519604444504,11.444500923156738,0.05166475474834442,0.005993606988340616
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94,0.48116496205329895,2.309284210205078,0.8902551531791687,0.297072172164917,0.03369738906621933,11.42712116241455,0.04280155524611473,0.004395311698317528
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100,0.4950791299343109,2.2314462661743164,0.8899312019348145,0.3073149621486664,0.026638252660632133,11.605094909667969,0.025773195549845695,0.002663825172930956
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102,0.49514085054397583,2.2353382110595703,0.8958147764205933,0.3103692829608917,0.030767181888222694,11.604228019714355,0.043478261679410934,0.004794885404407978
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103,0.49693024158477783,2.2055575847625732,0.8950552344322205,0.31496623158454895,0.028902504593133926,11.71531867980957,0.047101449221372604,0.005194459110498428
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104,0.49341315031051636,2.2163286209106445,0.8924418687820435,0.3125597834587097,0.030101224780082703,11.688882827758789,0.05045871436595917,0.005860415752977133
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108,0.5018665194511414,2.170114040374756,0.8985010981559753,0.3236355781555176,0.026638252660632133,11.81995677947998,0.050549451261758804,0.0061267982237041
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111,0.5099496841430664,2.1457791328430176,0.8910502791404724,0.32928144931793213,0.0347629189491272,11.838603973388672,0.04272634908556938,0.005594032816588879
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115,0.5132200121879578,2.130542278289795,0.8899316787719727,0.333508163690567,0.028103357180953026,11.998661041259766,0.029556650668382645,0.0039957379922270775
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116,0.5117082595825195,2.1338095664978027,0.8900665640830994,0.33421775698661804,0.028902504593133926,11.887906074523926,0.03413654491305351,0.004528502933681011
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117,0.5156881213188171,2.1086390018463135,0.8911980390548706,0.33736464381217957,0.026371870189905167,11.974492073059082,0.029622063040733337,0.0038625465240329504
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118,0.5131274461746216,2.1130216121673584,0.8899363279342651,0.33202728629112244,0.029035694897174835,11.979191780090332,0.042507968842983246,0.005327650345861912
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119,0.5175083875656128,2.0950114727020264,0.8891139030456543,0.33866041898727417,0.027970165014266968,11.969651222229004,0.03681591898202896,0.0049280766397714615
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120,0.5184339880943298,2.0944504737854004,0.8887985944747925,0.3373337984085083,0.029302077367901802,12.040390014648438,0.0416666679084301,0.005594032816588879
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121,0.5188041925430298,2.0775392055511475,0.8897827863693237,0.3412211239337921,0.029035694897174835,12.060857772827148,0.039603959769010544,0.005860415752977133
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122,0.5186190605163574,2.0738584995269775,0.889119029045105,0.34436801075935364,0.030767181888222694,12.1253662109375,0.039961013942956924,0.005460841581225395
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123,0.5244500637054443,2.0571885108947754,0.8918961882591248,0.34566378593444824,0.028369739651679993,12.098810195922852,0.047887325286865234,0.006792754400521517
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124,0.5244191884994507,2.064870834350586,0.8906987905502319,0.349211722612381,0.027171017602086067,12.087373733520508,0.046226415783166885,0.00652637192979455
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125,0.5277203321456909,2.0490267276763916,0.8965489864349365,0.3518649935722351,0.029302077367901802,12.172993659973145,0.04845815151929855,0.0073255193419754505
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126,0.5292629599571228,2.0358173847198486,0.8928487300872803,0.3509085774421692,0.029302077367901802,12.197569847106934,0.05033238232135773,0.007059136871248484
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127,0.5261777639389038,2.039593458175659,0.8876640200614929,0.3527596890926361,0.029968034476041794,12.19669246673584,0.038058992475271225,0.005327650345861912
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128,0.5286767482757568,2.045234203338623,0.8928794860839844,0.3520500957965851,0.026638252660632133,12.254743576049805,0.04775023087859154,0.006925945635885
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129,0.5273810029029846,2.019009828567505,0.8936895132064819,0.3560916781425476,0.028236547484993935,12.245428085327148,0.04212454333901405,0.0061267982237041
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130,0.5316693782806396,2.0217788219451904,0.8865828514099121,0.35451823472976685,0.02770378254354,12.255803108215332,0.03027823194861412,0.0049280766397714615
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131,0.529139518737793,2.0269694328308105,0.8874412178993225,0.3551352918148041,0.024906765669584274,12.318575859069824,0.028229255229234695,0.004395311698317528
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132,0.530774712562561,2.020674705505371,0.8930976390838623,0.3600715696811676,0.030633989721536636,12.259456634521484,0.03291814774274826,0.0049280766397714615
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133,0.5358652472496033,2.004709005355835,0.8911094069480896,0.3617992699146271,0.025839105248451233,12.3148775100708,0.03996669501066208,0.0063931806944310665
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134,0.5350013971328735,2.0104472637176514,0.8920989036560059,0.36297163367271423,0.0245071928948164,12.377913475036621,0.03404255211353302,0.005327650345861912
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135,0.5341683626174927,1.9952261447906494,0.8889890909194946,0.36491531133651733,0.027304209768772125,12.308615684509277,0.037351444363594055,0.005860415752977133
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136,0.5363588929176331,1.977656602859497,0.8867825269699097,0.36513128876686096,0.02597229555249214,12.350476264953613,0.0416666679084301,0.00652637192979455
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137,0.5374078154563904,1.9927960634231567,0.8867318630218506,0.3663961887359619,0.02770378254354,12.320837020874023,0.048945147544145584,0.007725093048065901
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138,0.5346928834915161,1.9949877262115479,0.8857911825180054,0.36562490463256836,0.02517314814031124,12.390408515930176,0.044763512909412384,0.007059136871248484
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139,0.5360811948776245,1.9844156503677368,0.8899413347244263,0.36522382497787476,0.025839105248451233,12.411690711975098,0.04545454680919647,0.007059136871248484
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140,0.5411100387573242,1.9690265655517578,0.8886411190032959,0.36880263686180115,0.02597229555249214,12.430559158325195,0.03371710702776909,0.005460841581225395
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141,0.5396600365638733,1.9641762971878052,0.8917816281318665,0.3709314167499542,0.02623867802321911,12.4696044921875,0.04200988635420799,0.006792754400521517
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142,0.5455218553543091,1.9504817724227905,0.8910818696022034,0.3760836720466614,0.025572722777724266,12.418275833129883,0.042096901684999466,0.007059136871248484
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143,0.5421590209007263,1.9519047737121582,0.8882477283477783,0.3751889765262604,0.02623867802321911,12.485637664794922,0.040030792355537415,0.006925945635885
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144,0.5448122620582581,1.9468328952789307,0.889616072177887,0.3774411380290985,0.0285029299557209,12.446735382080078,0.03910614550113678,0.00652637192979455
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145,0.5451207756996155,1.9369045495986938,0.8899643421173096,0.3772868812084198,0.02703782543540001,12.560592651367188,0.03579418361186981,0.0063931806944310665
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146,0.5474655032157898,1.9268661737442017,0.8849974870681763,0.3765464425086975,0.02623867802321911,12.596115112304688,0.04286827892065048,0.0073255193419754505
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147,0.5476506352424622,1.93109130859375,0.8859191536903381,0.3773486018180847,0.023042088374495506,12.558056831359863,0.033179011195898056,0.005727224517613649
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148,0.550057053565979,1.9213926792144775,0.8871981501579285,0.383148729801178,0.028902504593133926,12.614895820617676,0.04980237036943436,0.008391049690544605
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149,0.5452133417129517,1.9351493120193481,0.8898617625236511,0.3793848156929016,0.03183271363377571,12.545914649963379,0.0569867305457592,0.00972296204417944
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150,0.5476815104484558,1.9309934377670288,0.8862917423248291,0.3801869750022888,0.02597229555249214,12.716741561889648,0.04245283082127571,0.007192328106611967
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151,0.5476815104484558,1.9219518899917603,0.8872062563896179,0.38317957520484924,0.02770378254354,12.617552757263184,0.03777777776122093,0.006792754400521517
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152,0.5517847537994385,1.9015811681747437,0.8898906707763672,0.38672754168510437,0.024240810424089432,12.619589805603027,0.04237918183207512,0.007591901812702417
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153,0.5500262379646301,1.9154318571090698,0.8858242630958557,0.3834572434425354,0.02943526953458786,12.641131401062012,0.044508256018161774,0.008257858455181122
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154,0.5549933910369873,1.8944422006607056,0.8872418403625488,0.389627605676651,0.0261054877191782,12.670968055725098,0.03582554683089256,0.0061267982237041
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155,0.5589115619659424,1.8859177827835083,0.8901515007019043,0.3915095925331116,0.0261054877191782,12.711993217468262,0.037093110382556915,0.00652637192979455
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156,0.5559806227684021,1.8884798288345337,0.8823529481887817,0.38919568061828613,0.027970165014266968,12.740025520324707,0.042059462517499924,0.007725093048065901
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157,0.5560731887817383,1.8935824632644653,0.886307954788208,0.3908308446407318,0.028902504593133926,12.72891902923584,0.03433162719011307,0.006259989459067583
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158,0.5584487915039062,1.8757405281066895,0.8872426152229309,0.3935149610042572,0.023841235786676407,12.814839363098145,0.03973013535141945,0.007059136871248484
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159,0.5582019686698914,1.8870446681976318,0.8885577917098999,0.3955511748790741,0.02876931242644787,12.754144668579102,0.04811250790953636,0.008657432161271572
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160,0.5601764917373657,1.858019471168518,0.887161910533905,0.39974701404571533,0.025439530611038208,12.773848533630371,0.04048295319080353,0.007591901812702417
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161,0.5626137852668762,1.8602666854858398,0.8863198161125183,0.39737141132354736,0.029968034476041794,12.754046440124512,0.04791666567325592,0.009190197102725506
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162,0.5647425651550293,1.8489397764205933,0.8882902264595032,0.40184494853019714,0.029302077367901802,12.78555965423584,0.05092911049723625,0.009856153279542923
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163,0.5640637874603271,1.8447000980377197,0.8873827457427979,0.39990127086639404,0.0261054877191782,12.787751197814941,0.03735024482011795,0.007059136871248484
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164,0.5602381825447083,1.8688673973083496,0.8844392895698547,0.3992842435836792,0.026505060493946075,12.858635902404785,0.036856744438409805,0.007059136871248484
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165,0.5628297328948975,1.8552261590957642,0.8863311409950256,0.40150555968284607,0.0269046351313591,12.861405372619629,0.04091539606451988,0.007858284749090672
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166,0.5637552738189697,1.8426259756088257,0.8871220946311951,0.40104278922080994,0.0285029299557209,12.849802017211914,0.039034776389598846,0.0073255193419754505
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167,0.5673032402992249,1.8278130292892456,0.8866918683052063,0.4053620398044586,0.030767181888222694,12.965641021728516,0.04281949996948242,0.008657432161271572
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168,0.5707277655601501,1.8260414600372314,0.8871670961380005,0.40801531076431274,0.025705913081765175,12.892997741699219,0.03879598528146744,0.007725093048065901
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169,0.565976619720459,1.8347554206848145,0.8871262669563293,0.4037269055843353,0.02770378254354,12.970552444458008,0.041987404227256775,0.007991475984454155
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170,0.5693086385726929,1.8239221572875977,0.8859087228775024,0.40965044498443604,0.026371870189905167,12.906702995300293,0.02920723147690296,0.005594032816588879
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171,0.5667170286178589,1.8284180164337158,0.8881816864013672,0.4090025722980499,0.02863612212240696,12.944962501525879,0.03335602581501007,0.00652637192979455
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172,0.5709437727928162,1.8230868577957153,0.8877695202827454,0.4077993333339691,0.0245071928948164,12.945072174072266,0.032661572098731995,0.006259989459067583
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173,0.5727948546409607,1.8146964311599731,0.8830251097679138,0.41245797276496887,0.02264251373708248,12.999178886413574,0.025273224338889122,0.0049280766397714615
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174,0.570141613483429,1.814064621925354,0.8848387598991394,0.40891000628471375,0.02770378254354,13.007941246032715,0.030934343114495277,0.00652637192979455
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175,0.5749853253364563,1.8037033081054688,0.8864833116531372,0.4156048595905304,0.027570592239499092,13.03261661529541,0.032890576869249344,0.006925945635885
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176,0.5703267455101013,1.8213082551956177,0.8843298554420471,0.41159412264823914,0.025039957836270332,13.018848419189453,0.034090910106897354,0.006792754400521517
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177,0.5724863409996033,1.8102179765701294,0.8846332430839539,0.4118717908859253,0.025839105248451233,13.093061447143555,0.028747433796525,0.005594032816588879
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178,0.5791811943054199,1.7775633335113525,0.8839846849441528,0.4207879602909088,0.024640383198857307,13.067671775817871,0.030653951689600945,0.005993606988340616
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179,0.5767130255699158,1.7834032773971558,0.8888670206069946,0.41801127791404724,0.024107618257403374,13.144515037536621,0.03372243791818619,0.006925945635885
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180,0.5729799866676331,1.790893793106079,0.8876851797103882,0.4196464419364929,0.02863612212240696,13.126501083374023,0.03575989603996277,0.007458710577338934
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181,0.5761885643005371,1.7785006761550903,0.8825170397758484,0.41970813274383545,0.028236547484993935,13.103719711303711,0.03418803587555885,0.006925945635885
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182,0.5740289092063904,1.7901051044464111,0.883081316947937,0.4180421531200409,0.02623867802321911,13.146336555480957,0.038060158491134644,0.008257858455181122
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183,0.5781630873680115,1.7712759971618652,0.8873786330223083,0.4229784309864044,0.026771442964673042,13.140911102294922,0.03639846667647362,0.007591901812702417
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184,0.5782556533813477,1.7702417373657227,0.8815178275108337,0.4214358329772949,0.026638252660632133,13.166864395141602,0.033438485115766525,0.007059136871248484
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185,0.5804769396781921,1.762231707572937,0.8844444155693054,0.4236263334751129,0.026771442964673042,13.120388984680176,0.03320053219795227,0.006659563165158033
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186,0.5814333558082581,1.7503889799118042,0.8858818411827087,0.4241507947444916,0.026771442964673042,13.202676773071289,0.03465045616030693,0.007591901812702417
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187,0.5805386900901794,1.7596943378448486,0.8879162669181824,0.4318637549877167,0.024107618257403374,13.159852981567383,0.0321601927280426,0.007059136871248484
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188,0.583808958530426,1.7473915815353394,0.8855352401733398,0.42723599076271057,0.030767181888222694,13.223766326904297,0.0373944528400898,0.008257858455181122
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189,0.5801067352294922,1.7572182416915894,0.8808368444442749,0.43124672770500183,0.026371870189905167,13.268448829650879,0.035163480788469315,0.007591901812702417
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190,0.5794588327407837,1.764090657234192,0.8827731609344482,0.42702001333236694,0.02184336632490158,13.28363037109375,0.027413588017225266,0.0061267982237041
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191,0.5833770632743835,1.7433326244354248,0.8829051852226257,0.4294264614582062,0.023974427953362465,13.17056941986084,0.037817731499671936,0.008124667219817638
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192,0.5822046995162964,1.7401121854782104,0.8842025399208069,0.43204885721206665,0.023308470845222473,13.270216941833496,0.031121550127863884,0.007059136871248484
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193,0.582389771938324,1.7409472465515137,0.8826382756233215,0.4322648346424103,0.02956845983862877,13.305438995361328,0.03871748223900795,0.008524240925908089
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194,0.5880356431007385,1.7228251695632935,0.8870276808738708,0.43457871675491333,0.02597229555249214,13.304712295532227,0.035108957439661026,0.007725093048065901
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195,0.5907197594642639,1.7185474634170532,0.887032151222229,0.4372628331184387,0.02623867802321911,13.304615020751953,0.033787790685892105,0.007591901812702417
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196,0.5877580046653748,1.712127923965454,0.8861203193664551,0.43667665123939514,0.026505060493946075,13.373809814453125,0.032073311507701874,0.007458710577338934
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197,0.5893005728721619,1.7202327251434326,0.885047435760498,0.4375405013561249,0.024906765669584274,13.317015647888184,0.035863716155290604,0.007991475984454155
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198,0.5906580686569214,1.7163420915603638,0.8778059482574463,0.4379415810108185,0.027970165014266968,13.338918685913086,0.040361445397138596,0.008923814631998539
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199,0.5922006368637085,1.7096997499465942,0.8826839923858643,0.4403479993343353,0.027970165014266968,13.373757362365723,0.03206996992230415,0.0073255193419754505
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200,0.5860611200332642,1.7133421897888184,0.8829767107963562,0.4367074966430664,0.02623867802321911,13.329508781433105,0.04228571429848671,0.009856153279542923
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201,0.5920463800430298,1.7080191373825073,0.8845868110656738,0.4405331313610077,0.026771442964673042,13.364344596862793,0.031812723726034164,0.007059136871248484
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202,0.5882824659347534,1.7190485000610352,0.8837282061576843,0.43497979640960693,0.024107618257403374,13.407631874084473,0.035118021070957184,0.008124667219817638
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203,0.5942986011505127,1.7019438743591309,0.8894762992858887,0.44642582535743713,0.023042088374495506,13.423816680908203,0.025553662329912186,0.005993606988340616
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204,0.5913367867469788,1.7097444534301758,0.8833067417144775,0.4437108635902405,0.025439530611038208,13.391646385192871,0.03433242440223694,0.008391049690544605
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205,0.5924474596977234,1.6975864171981812,0.8828210830688477,0.44488322734832764,0.02597229555249214,13.446434020996094,0.033664457499980927,0.008124667219817638
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388,0.6719526052474976,1.3232167959213257,0.8840323090553284,0.5540986657142639,0.02437400072813034,15.175251007080078,0.028143275529146194,0.010255726985633373
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389,0.6681886911392212,1.3429473638534546,0.8871647119522095,0.5520932674407959,0.024640383198857307,15.14013671875,0.026729559525847435,0.009057005867362022
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390,0.6672322750091553,1.3427270650863647,0.8820606470108032,0.5530805587768555,0.02264251373708248,15.173784255981445,0.02987361140549183,0.010388918220996857
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391,0.6668003797531128,1.3483669757843018,0.8832740783691406,0.5516613721847534,0.02770378254354,15.073701858520508,0.0320587083697319,0.011054874397814274
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392,0.6656588315963745,1.3510165214538574,0.8816626667976379,0.5516613721847534,0.028902504593133926,15.058490753173828,0.035452794283628464,0.0122535964474082
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393,0.6647024154663086,1.3485132455825806,0.8816465735435486,0.5504273176193237,0.023841235786676407,15.043341636657715,0.02779952995479107,0.009456579573452473
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394,0.6677259206771851,1.3334041833877563,0.8836899995803833,0.5529571175575256,0.026505060493946075,15.032604217529297,0.03200000151991844,0.011188065633177757
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395,0.6645481586456299,1.3454192876815796,0.8818504810333252,0.5510443449020386,0.022109748795628548,15.131749153137207,0.03278084844350815,0.011854022741317749
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396,0.6679418683052063,1.3340646028518677,0.8836682438850403,0.5535433292388916,0.0269046351313591,15.21681022644043,0.03572757542133331,0.012786361388862133
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397,0.6652577519416809,1.355985403060913,0.8813912272453308,0.548853874206543,0.023974427953362465,15.111682891845703,0.027293404564261436,0.009589770808815956
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398,0.6664301156997681,1.3443900346755981,0.883171796798706,0.5539135336875916,0.026505060493946075,15.129378318786621,0.028914757072925568,0.010255726985633373
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399,0.6718292236328125,1.3300294876098633,0.880777895450592,0.5561348795890808,0.024773575365543365,15.142078399658203,0.03658996522426605,0.012919552624225616
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400,0.6688365936279297,1.3345757722854614,0.8814259767532349,0.5591275095939636,0.025839105248451233,15.061202049255371,0.030220843851566315,0.010388918220996857
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401,0.6680035591125488,1.3443490266799927,0.8824078440666199,0.5544688701629639,0.027970165014266968,15.158656120300293,0.03127354755997658,0.011054874397814274
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402,0.669885516166687,1.3225524425506592,0.8874465227127075,0.5568135976791382,0.030500799417495728,15.19572639465332,0.028193525969982147,0.010788491927087307
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403,0.6736494898796082,1.3183526992797852,0.8829625844955444,0.5572146773338318,0.024773575365543365,15.23160457611084,0.027316352352499962,0.009856153279542923
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404,0.6711196303367615,1.3234702348709106,0.8821116089820862,0.5572764277458191,0.02623867802321911,15.263787269592285,0.026394052430987358,0.009456579573452473
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405,0.6665843725204468,1.3373602628707886,0.8803410530090332,0.5574615001678467,0.026638252660632133,15.251965522766113,0.033515483140945435,0.0122535964474082
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406,0.6689599752426147,1.3324828147888184,0.8817598819732666,0.555857241153717,0.025839105248451233,15.254908561706543,0.029086891561746597,0.01052210945636034
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407,0.6708419322967529,1.331113576889038,0.8804464936256409,0.5548391342163086,0.02437400072813034,15.218559265136719,0.02890792302787304,0.010788491927087307
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408,0.667016327381134,1.3329501152038574,0.8825289607048035,0.5551167726516724,0.02277570590376854,15.30823040008545,0.024105185642838478,0.008790623396635056
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409,0.6667386293411255,1.3428945541381836,0.8820964694023132,0.5555795431137085,0.026771442964673042,15.310749053955078,0.027007298544049263,0.009856153279542923
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410,0.6708419322967529,1.3284109830856323,0.884909987449646,0.5595902800559998,0.025705913081765175,15.271698951721191,0.030585601925849915,0.01092168316245079
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411,0.6706568598747253,1.3258869647979736,0.8830150365829468,0.5591275095939636,0.022509323433041573,15.329648971557617,0.024074073880910873,0.008657432161271572
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412,0.6728164553642273,1.3220443725585938,0.8853025436401367,0.5610403418540955,0.025839105248451233,15.306375503540039,0.028860028833150864,0.010655300691723824
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413,0.6668928861618042,1.3291908502578735,0.8811192512512207,0.5547465682029724,0.022509323433041573,15.388779640197754,0.024205202236771584,0.008923814631998539
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414,0.6718292236328125,1.318732500076294,0.8841966390609741,0.5587573051452637,0.02117741107940674,15.225462913513184,0.024463681504130363,0.008657432161271572
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415,0.672600507736206,1.3107600212097168,0.884510338306427,0.5595285892486572,0.025439530611038208,15.230507850646973,0.025354214012622833,0.009057005867362022
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416,0.6678184866905212,1.3346354961395264,0.8798011541366577,0.5568753480911255,0.023175280541181564,15.384774208068848,0.024677716195583344,0.008923814631998539
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417,0.6711504459381104,1.3289040327072144,0.879194974899292,0.559312641620636,0.024107618257403374,15.296202659606934,0.025757575407624245,0.009057005867362022
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418,0.6723228096961975,1.3197834491729736,0.8793028593063354,0.5603307485580444,0.022109748795628548,15.384011268615723,0.022058824077248573,0.007991475984454155
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419,0.6752229332923889,1.3065757751464844,0.8844304084777832,0.5616881847381592,0.0245071928948164,15.334729194641113,0.026428570970892906,0.009856153279542923
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420,0.673556923866272,1.3200078010559082,0.8848767280578613,0.5624903440475464,0.022908898070454597,15.302691459655762,0.02778806909918785,0.009989344514906406
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421,0.6741430759429932,1.3237558603286743,0.8849021792411804,0.5638169646263123,0.019579116255044937,15.320037841796875,0.02165137603878975,0.007858284749090672
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422,0.6763644218444824,1.3052361011505127,0.8870647549629211,0.5651127696037292,0.02703782543540001,15.34117603302002,0.02997075952589512,0.01092168316245079
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423,0.6776602268218994,1.3001573085784912,0.8854216933250427,0.5655138492584229,0.02517314814031124,15.429993629455566,0.031049249693751335,0.011587640270590782
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424,0.6735877394676208,1.3089853525161743,0.8848690390586853,0.5629222989082336,0.02197655849158764,15.403242111206055,0.025339042767882347,0.009456579573452473
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425,0.6744207739830017,1.303492546081543,0.8822564482688904,0.5640637874603271,0.027570592239499092,15.38291072845459,0.029273580759763718,0.010788491927087307
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426,0.6721377372741699,1.3211054801940918,0.880723774433136,0.5631382465362549,0.024773575365543365,15.439311981201172,0.03128332644701004,0.011720831505954266
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427,0.6790176630020142,1.293905258178711,0.8825172185897827,0.5654829740524292,0.02011188119649887,15.407571792602539,0.02056555263698101,0.007458710577338934
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428,0.6748526692390442,1.295568585395813,0.8819300532341003,0.5639095306396484,0.01931273378431797,15.317055702209473,0.02155332639813423,0.007725093048065901
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429,0.6756548285484314,1.3084707260131836,0.8819872736930847,0.564680814743042,0.021710176020860672,15.392277717590332,0.024875622242689133,0.00932338833808899
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430,0.6715207099914551,1.3145455121994019,0.8834236860275269,0.5629839897155762,0.024240810424089432,15.324234008789062,0.026526162400841713,0.00972296204417944
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431,0.6738037467002869,1.3097641468048096,0.881783127784729,0.5644957423210144,0.024773575365543365,15.394522666931152,0.027170076966285706,0.010255726985633373
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432,0.6768580675125122,1.2988286018371582,0.8841656446456909,0.569185197353363,0.02184336632490158,15.384305000305176,0.022892819717526436,0.008790623396635056
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433,0.68034428358078,1.2796796560287476,0.8881680369377136,0.5701724886894226,0.02703782543540001,15.456602096557617,0.03315696492791176,0.012519978918135166
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434,0.6713664531707764,1.318310022354126,0.883273720741272,0.5640329718589783,0.023042088374495506,15.318225860595703,0.02608376182615757,0.009456579573452473
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435,0.6769814491271973,1.3000831604003906,0.8819786906242371,0.5687841176986694,0.024107618257403374,15.36328125,0.029637761414051056,0.010788491927087307
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436,0.6781229972839355,1.295853614807129,0.8841683864593506,0.5656681060791016,0.022908898070454597,15.39720630645752,0.027162259444594383,0.01012253575026989
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437,0.6791719198226929,1.2803832292556763,0.8835996389389038,0.5698022246360779,0.026505060493946075,15.408196449279785,0.029263006523251534,0.010788491927087307
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438,0.6767038106918335,1.3026790618896484,0.8831337094306946,0.5686298608779907,0.022376131266355515,15.356972694396973,0.02379182167351246,0.008524240925908089
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439,0.6800357699394226,1.2822908163070679,0.8840919137001038,0.5711288452148438,0.024107618257403374,15.34129810333252,0.02654867246747017,0.009589770808815956
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440,0.6764878034591675,1.3075710535049438,0.8808825612068176,0.5653595924377441,0.02277570590376854,15.432689666748047,0.03374908119440079,0.0122535964474082
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441,0.6780304312705994,1.2965459823608398,0.8848252892494202,0.570264995098114,0.02357485331594944,15.435650825500488,0.024596182629466057,0.008923814631998539
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442,0.6836146116256714,1.2699233293533325,0.8885815143585205,0.5747693777084351,0.027570592239499092,15.48069953918457,0.03558844327926636,0.01345231756567955
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443,0.6789868474006653,1.2881088256835938,0.883030354976654,0.5724863409996033,0.02344166301190853,15.395249366760254,0.02698706090450287,0.00972296204417944
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444,0.6800357699394226,1.2887217998504639,0.8834303021430969,0.5714373588562012,0.026505060493946075,15.56297492980957,0.030034130439162254,0.011720831505954266
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445,0.6804985404014587,1.2849736213684082,0.8828154802322388,0.5715299248695374,0.0261054877191782,15.475839614868164,0.03288334980607033,0.01265317015349865
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446,0.6779995560646057,1.2900556325912476,0.8807769417762756,0.5679819583892822,0.022509323433041573,15.524502754211426,0.029147176072001457,0.010788491927087307
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447,0.680560290813446,1.2741906642913818,0.8828533291816711,0.5731342434883118,0.022376131266355515,15.586129188537598,0.03295932710170746,0.012519978918135166
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448,0.6751611828804016,1.3016000986099243,0.8830138444900513,0.5698330998420715,0.023042088374495506,15.458155632019043,0.025877349078655243,0.00972296204417944
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449,0.6800975203514099,1.278232216835022,0.8888995051383972,0.5731650590896606,0.023042088374495506,15.417872428894043,0.030271030962467194,0.011454449035227299
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450,0.6784623265266418,1.2950562238693237,0.8827908635139465,0.5706969499588013,0.02104421891272068,15.665538787841797,0.023943662643432617,0.009057005867362022
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451,0.6829667091369629,1.2810605764389038,0.8823249936103821,0.5727640390396118,0.023974427953362465,15.526734352111816,0.026879766955971718,0.009856153279542923
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452,0.6804677248001099,1.2831255197525024,0.8845148086547852,0.5739672183990479,0.02024507150053978,15.557622909545898,0.027718549594283104,0.010388918220996857
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453,0.6853423118591309,1.2641315460205078,0.8876217603683472,0.5763119459152222,0.02344166301190853,15.507885932922363,0.029442327097058296,0.01132125686854124
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454,0.6821954250335693,1.2764559984207153,0.8838685154914856,0.5757566690444946,0.021710176020860672,15.532593727111816,0.029287226498126984,0.011054874397814274
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455,0.6842625141143799,1.2756507396697998,0.8842873573303223,0.5757566690444946,0.027304209768772125,15.546850204467773,0.03378613665699959,0.012919552624225616
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456,0.6765803694725037,1.2999184131622314,0.8818047046661377,0.5692160725593567,0.025439530611038208,15.613167762756348,0.02839900553226471,0.010655300691723824
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457,0.6792336702346802,1.27949857711792,0.8840559124946594,0.5718693137168884,0.03023441694676876,15.587337493896484,0.03599153086543083,0.013585508801043034
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458,0.6756239533424377,1.29777991771698,0.8793943524360657,0.5680128335952759,0.02197655849158764,15.51997184753418,0.027249909937381744,0.01012253575026989
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459,0.6826581954956055,1.2777398824691772,0.8811712265014648,0.5737821459770203,0.027304209768772125,15.51381778717041,0.03238161653280258,0.012386787682771683
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460,0.6815475225448608,1.278915524482727,0.8839501738548279,0.5755098462104797,0.02517314814031124,15.576892852783203,0.026306558400392532,0.009989344514906406
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461,0.6829975843429565,1.263382077217102,0.8850753307342529,0.5799833536148071,0.024240810424089432,15.552083015441895,0.030024725943803787,0.01132125686854124
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462,0.6797581315040588,1.2840021848678589,0.8833214640617371,0.5727022886276245,0.0245071928948164,15.596945762634277,0.028866713866591454,0.010788491927087307
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463,0.6792027950286865,1.2845584154129028,0.8823557496070862,0.5722395181655884,0.027437400072813034,15.609827041625977,0.031111111864447594,0.012120405212044716
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464,0.6823804974555969,1.2659330368041992,0.8846390247344971,0.5779779553413391,0.023042088374495506,15.628459930419922,0.02975778467953205,0.011454449035227299
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465,0.682349681854248,1.2660725116729736,0.881977379322052,0.5796130895614624,0.02357485331594944,15.613870620727539,0.02938053011894226,0.011054874397814274
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466,0.6863295435905457,1.257373332977295,0.8853891491889954,0.5784407258033752,0.0269046351313591,15.603287696838379,0.032809771597385406,0.012519978918135166
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467,0.6828433275222778,1.2705256938934326,0.8826392889022827,0.5761268734931946,0.024640383198857307,15.652741432189941,0.032718412578105927,0.012519978918135166
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468,0.6806527972221375,1.2820827960968018,0.8829236030578613,0.5784099102020264,0.026371870189905167,15.670934677124023,0.029015544801950455,0.011188065633177757
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469,0.6846635341644287,1.2682994604110718,0.8850221633911133,0.5782556533813477,0.0253063403069973,15.638886451721191,0.029463347047567368,0.011188065633177757
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470,0.685218870639801,1.266710638999939,0.8832165002822876,0.5791195034980774,0.02956845983862877,15.626413345336914,0.03188908100128174,0.0122535964474082
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471,0.6789559721946716,1.2770353555679321,0.8827762603759766,0.5752630233764648,0.024640383198857307,15.641463279724121,0.02450486831367016,0.00972296204417944
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472,0.680560290813446,1.2725456953048706,0.8805040121078491,0.5756023526191711,0.023974427953362465,15.54421615600586,0.030422333627939224,0.01132125686854124
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473,0.6904019713401794,1.248769760131836,0.8834103941917419,0.5837163925170898,0.024906765669584274,15.665316581726074,0.031651828438043594,0.012786361388862133
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474,0.6857742071151733,1.2504584789276123,0.8846297860145569,0.5831302404403687,0.026638252660632133,15.61228084564209,0.029311520978808403,0.011454449035227299
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475,0.6858359575271606,1.2545549869537354,0.8806201815605164,0.5782864689826965,0.027437400072813034,15.596033096313477,0.03480444848537445,0.012919552624225616
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476,0.6866689324378967,1.2515605688095093,0.8878271579742432,0.5818961262702942,0.022109748795628548,15.68541145324707,0.02385128103196621,0.009057005867362022
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477,0.6829975843429565,1.2713279724121094,0.8820290565490723,0.5782864689826965,0.0245071928948164,15.596580505371094,0.030077729374170303,0.011854022741317749
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478,0.6875019073486328,1.2445377111434937,0.8843173384666443,0.5808780193328857,0.02197655849158764,15.608220100402832,0.03026038035750389,0.011454449035227299
|
||||
479,0.6863604187965393,1.2577707767486572,0.8845593333244324,0.580600380897522,0.024640383198857307,15.67897891998291,0.02923773042857647,0.011188065633177757
|
||||
480,0.6871317028999329,1.2502896785736084,0.8846459984779358,0.5832228064537048,0.02104421891272068,15.637908935546875,0.02727900631725788,0.01052210945636034
|
||||
481,0.6871317028999329,1.2557921409606934,0.8847221732139587,0.5801067352294922,0.022908898070454597,15.653802871704102,0.02449520118534565,0.009856153279542923
|
||||
482,0.6861135959625244,1.2525684833526611,0.885220468044281,0.5815259218215942,0.021443793550133705,15.511479377746582,0.025195173919200897,0.009456579573452473
|
||||
483,0.685928463935852,1.2476720809936523,0.8867862224578857,0.5797365307807922,0.023841235786676407,15.60422134399414,0.025641025975346565,0.00972296204417944
|
||||
484,0.6912041306495667,1.2444134950637817,0.884115993976593,0.5860919952392578,0.020644646137952805,15.647104263305664,0.022791024297475815,0.008657432161271572
|
||||
485,0.6871933937072754,1.2472432851791382,0.8845306634902954,0.5823280811309814,0.023308470845222473,15.679403305053711,0.027988942340016365,0.010788491927087307
|
||||
486,0.6868231892585754,1.2486929893493652,0.8801140785217285,0.580723762512207,0.024773575365543365,15.690462112426758,0.029907183721661568,0.011587640270590782
|
||||
487,0.6867614984512329,1.24271821975708,0.8813993334770203,0.5837472677230835,0.023308470845222473,15.701974868774414,0.028670120984315872,0.011054874397814274
|
||||
488,0.6853731274604797,1.2553348541259766,0.8803150057792664,0.5793354511260986,0.022908898070454597,15.78759765625,0.028401585295796394,0.011454449035227299
|
||||
489,0.6885817646980286,1.2426351308822632,0.8853147029876709,0.5858760476112366,0.023841235786676407,15.694445610046387,0.03143812716007233,0.012519978918135166
|
||||
490,0.6866380572319031,1.2594478130340576,0.8839532732963562,0.5814025402069092,0.025039957836270332,15.690524101257324,0.028629856184124947,0.011188065633177757
|
||||
491,0.6887051463127136,1.250142216682434,0.8840315937995911,0.5839632153511047,0.02277570590376854,15.764365196228027,0.027217742055654526,0.010788491927087307
|
||||
492,0.6900317668914795,1.2379319667816162,0.8842149376869202,0.586894154548645,0.02277570590376854,15.769707679748535,0.02131202071905136,0.008524240925908089
|
||||
493,0.6919445991516113,1.23922598361969,0.8878971934318542,0.5862154364585876,0.02597229555249214,15.70924186706543,0.03021148033440113,0.011987213976681232
|
||||
494,0.6882423758506775,1.2532153129577637,0.8841437697410583,0.580600380897522,0.02357485331594944,15.702035903930664,0.028542304411530495,0.011188065633177757
|
||||
495,0.6893530488014221,1.2369344234466553,0.8848087191581726,0.5865239500999451,0.024107618257403374,15.827147483825684,0.026838965713977814,0.010788491927087307
|
||||
496,0.688180685043335,1.2399886846542358,0.8844113349914551,0.5863696932792664,0.023708045482635498,15.680917739868164,0.028882093727588654,0.01132125686854124
|
||||
497,0.6899392008781433,1.240660548210144,0.8811273574829102,0.584518551826477,0.026371870189905167,15.827164649963379,0.02998696267604828,0.0122535964474082
|
||||
498,0.6896923780441284,1.2430635690689087,0.8848340511322021,0.5857217907905579,0.021310601383447647,15.643759727478027,0.02571134641766548,0.009989344514906406
|
||||
499,0.6924999356269836,1.227786898612976,0.8868100047111511,0.5878505706787109,0.0245071928948164,15.766032218933105,0.025303643196821213,0.009989344514906406
|
||||
500,0.689599871635437,1.254508376121521,0.8851861953735352,0.5851355791091919,0.02024507150053978,15.780624389648438,0.02225913666188717,0.008923814631998539
|
||||
501,0.6882732510566711,1.2436668872833252,0.8817734122276306,0.5853824019432068,0.022109748795628548,15.643333435058594,0.02482621558010578,0.009989344514906406
|
||||
502,0.6912041306495667,1.2225037813186646,0.8826060891151428,0.5884675979614258,0.02197655849158764,15.728686332702637,0.023217247799038887,0.00932338833808899
|
||||
503,0.6895381212234497,1.2369574308395386,0.8846009969711304,0.5848579406738281,0.02184336632490158,15.78727912902832,0.02242744155228138,0.009057005867362022
|
||||
504,0.6896923780441284,1.236598014831543,0.8838709592819214,0.5875111818313599,0.02197655849158764,15.721540451049805,0.02557980827987194,0.009989344514906406
|
||||
505,0.6949372291564941,1.2077374458312988,0.8857077360153198,0.5946071147918701,0.02437400072813034,15.750764846801758,0.03257549926638603,0.012786361388862133
|
||||
506,0.6908956170082092,1.2320822477340698,0.8841482996940613,0.5862771272659302,0.0253063403069973,15.87469482421875,0.02779616229236126,0.011188065633177757
|
||||
507,0.6930860877037048,1.2261123657226562,0.8851701021194458,0.5907506346702576,0.024906765669584274,15.72480583190918,0.02967257797718048,0.011587640270590782
|
||||
508,0.6932095289230347,1.2226026058197021,0.8861822485923767,0.5892388820648193,0.025705913081765175,15.760244369506836,0.030981067568063736,0.011987213976681232
|
||||
509,0.6949372291564941,1.2181510925292969,0.8859137892723083,0.5910282731056213,0.025039957836270332,15.747661590576172,0.02960745245218277,0.011854022741317749
|
||||
510,0.6944435834884644,1.2189587354660034,0.8842795491218567,0.5905655026435852,0.0245071928948164,15.886993408203125,0.02287798374891281,0.009190197102725506
|
||||
511,0.6909881830215454,1.2265442609786987,0.8860976099967957,0.5909048914909363,0.023708045482635498,15.838539123535156,0.025623735040426254,0.01012253575026989
|
||||
BIN
RNN/trained_models/gamma-lstm-weights.hdf5
Normal file
BIN
RNN/trained_models/gamma-lstm-weights.hdf5
Normal file
Binary file not shown.
423
RNN/trained_models/gamma_lstm.log
Normal file
423
RNN/trained_models/gamma_lstm.log
Normal file
@ -0,0 +1,423 @@
|
||||
epoch,accuracy,loss,precision,recall,val_accuracy,val_loss,val_precision,val_recall
|
||||
0,0.044179804623126984,6.215623378753662,0.0,0.0,0.04128929227590561,6.525483131408691,0.0,0.0
|
||||
1,0.049918241798877716,5.920177459716797,0.0,0.0,0.04728289693593979,6.602599620819092,0.0,0.0
|
||||
2,0.05858760327100754,5.741495132446289,0.7368420958518982,0.00043192546581849456,0.04475226253271103,6.6925506591796875,0.0,0.0
|
||||
3,0.07123684883117676,5.538107872009277,0.97826087474823,0.00138833187520504,0.046616941690444946,6.796008110046387,0.0,0.0
|
||||
4,0.0901181623339653,5.326170921325684,0.8947368264198303,0.004720328375697136,0.04568460211157799,6.864126682281494,0.0,0.0
|
||||
5,0.1119612529873848,5.1200079917907715,0.9161290526390076,0.008761916309595108,0.040756527334451675,6.992399215698242,0.0,0.0
|
||||
6,0.1278807818889618,4.92478609085083,0.9153713583946228,0.01635146327316761,0.04049014300107956,7.130080223083496,0.0,0.0
|
||||
7,0.14685465395450592,4.732901096343994,0.894911527633667,0.024959120899438858,0.04195524752140045,7.23391580581665,0.014285714365541935,0.00013319126446731389
|
||||
8,0.1641625314950943,4.558708667755127,0.8944765329360962,0.03347422182559967,0.038225892931222916,7.348636627197266,0.016129031777381897,0.00013319126446731389
|
||||
9,0.17946502566337585,4.3979315757751465,0.8881028890609741,0.04260636121034622,0.033963773399591446,7.45266056060791,0.0,0.0
|
||||
10,0.1956622302532196,4.223810195922852,0.8829953074455261,0.052386388182640076,0.034096963703632355,7.640355110168457,0.03773584961891174,0.0005327650578692555
|
||||
11,0.21250732243061066,4.071560859680176,0.8820124864578247,0.06111745163798332,0.03729355335235596,7.722485065460205,0.09615384787321091,0.001331912586465478
|
||||
12,0.23052479326725006,3.9347004890441895,0.8885003924369812,0.0705581083893776,0.035961639136075974,7.909381866455078,0.03030303120613098,0.0005327650578692555
|
||||
13,0.2474624365568161,3.788947820663452,0.8762160539627075,0.08058495074510574,0.03303143382072449,8.03416633605957,0.08965517580509186,0.00173148640897125
|
||||
14,0.2598957121372223,3.685380458831787,0.8806374073028564,0.0903649777173996,0.03223228454589844,8.166817665100098,0.07065217196941376,0.00173148640897125
|
||||
15,0.28176966309547424,3.556426525115967,0.8897936940193176,0.10113225877285004,0.035961639136075974,8.2846097946167,0.03703703731298447,0.0007991475868038833
|
||||
16,0.29565298557281494,3.4513485431671143,0.8825381398200989,0.11242403090000153,0.03116675466299057,8.439950942993164,0.0625,0.001331912586465478
|
||||
17,0.3128065764904022,3.3439784049987793,0.89199298620224,0.1251041293144226,0.029968034476041794,8.554068565368652,0.056872036308050156,0.0015982951736077666
|
||||
18,0.324129194021225,3.250164747238159,0.8870079517364502,0.13417455554008484,0.033297814428806305,8.699393272399902,0.06614785641431808,0.0022642514668405056
|
||||
19,0.3396785259246826,3.163926839828491,0.8892918229103088,0.14373862743377686,0.02876931242644787,8.747879981994629,0.06844106316566467,0.002397442702203989
|
||||
20,0.34819361567497253,3.093963384628296,0.8880199193954468,0.15413568913936615,0.029968034476041794,8.892354011535645,0.0625,0.0029302078764885664
|
||||
21,0.36118224263191223,3.0195746421813965,0.8958263993263245,0.1655508577823639,0.0285029299557209,9.000614166259766,0.05624999850988388,0.002397442702203989
|
||||
22,0.3742017149925232,2.935436725616455,0.8926143646240234,0.17823095619678497,0.03223228454589844,9.13429069519043,0.06575342267751694,0.0031965903472155333
|
||||
23,0.3839200437068939,2.8791615962982178,0.8897695541381836,0.18702372908592224,0.030767181888222694,9.235234260559082,0.07238605618476868,0.0035961640533059835
|
||||
24,0.3966309726238251,2.8087072372436523,0.8930029273033142,0.19569309055805206,0.029168887063860893,9.330933570861816,0.0416666679084301,0.0022642514668405056
|
||||
25,0.4057631194591522,2.741058349609375,0.896346390247345,0.20436245203018188,0.028236547484993935,9.388174057006836,0.04415584355592728,0.0022642514668405056
|
||||
26,0.4139080047607422,2.684173583984375,0.896016538143158,0.21374140679836273,0.028369739651679993,9.490544319152832,0.07088607549667358,0.003729355288669467
|
||||
27,0.42214542627334595,2.6382946968078613,0.8947760462760925,0.22247245907783508,0.02703782543540001,9.563433647155762,0.06997742503881454,0.004128929227590561
|
||||
28,0.4328818619251251,2.579094648361206,0.8960577845573425,0.23351742327213287,0.031433138996362686,9.628026962280273,0.07079645991325378,0.004262120462954044
|
||||
29,0.4407799243927002,2.5282981395721436,0.8990995287895203,0.24027396738529205,0.03303143382072449,9.722415924072266,0.09210526198148727,0.00652637192979455
|
||||
30,0.4508376121520996,2.4787583351135254,0.8953956365585327,0.24718476831912994,0.032365478575229645,9.825923919677734,0.07933579385280609,0.005727224517613649
|
||||
31,0.4624378979206085,2.423647880554199,0.8986515402793884,0.2590627074241638,0.0269046351313591,9.947509765625,0.0664505660533905,0.005460841581225395
|
||||
32,0.4662635326385498,2.403594493865967,0.8967328667640686,0.26335111260414124,0.031566329300403595,9.99622631072998,0.05347593501210213,0.0039957379922270775
|
||||
33,0.47530311346054077,2.34285831451416,0.8935049176216125,0.2720513343811035,0.030633989721536636,10.071548461914062,0.050215207040309906,0.004661694169044495
|
||||
34,0.48449695110321045,2.302877902984619,0.8968417048454285,0.28297287225723267,0.02770378254354,10.141798973083496,0.06896551698446274,0.0061267982237041
|
||||
35,0.48986518383026123,2.2606725692749023,0.9018037915229797,0.28843364119529724,0.030767181888222694,10.193535804748535,0.06314244121313095,0.005727224517613649
|
||||
36,0.4948631823062897,2.2281806468963623,0.894219160079956,0.2973189651966095,0.028369739651679993,10.256683349609375,0.07057057321071625,0.006259989459067583
|
||||
37,0.5059389472007751,2.186138153076172,0.9010799527168274,0.3063277006149292,0.03103356435894966,10.322997093200684,0.04940119758248329,0.004395311698317528
|
||||
38,0.5125104188919067,2.1492385864257812,0.8943003416061401,0.3127140402793884,0.02783697471022606,10.534867286682129,0.05025125667452812,0.005327650345861912
|
||||
39,0.5206552743911743,2.1025490760803223,0.9020215272903442,0.32351216673851013,0.03196590393781662,10.432486534118652,0.06367041170597076,0.006792754400521517
|
||||
40,0.524480938911438,2.072115898132324,0.896875262260437,0.33029958605766296,0.030633989721536636,10.539868354797363,0.06699752062559128,0.007192328106611967
|
||||
41,0.5312374830245972,2.0392653942108154,0.9004128575325012,0.33640822768211365,0.03303143382072449,10.627106666564941,0.06589595228433609,0.007591901812702417
|
||||
42,0.5341683626174927,2.01973295211792,0.8989775776863098,0.3418073058128357,0.027304209768772125,10.717484474182129,0.05569007247686386,0.0061267982237041
|
||||
43,0.5399993658065796,1.9795559644699097,0.9002400040626526,0.3471755087375641,0.030500799417495728,10.767905235290527,0.06907545030117035,0.008657432161271572
|
||||
44,0.5447196960449219,1.9645510911941528,0.8995612859725952,0.3542405962944031,0.026771442964673042,10.793705940246582,0.054143644869327545,0.00652637192979455
|
||||
45,0.5545614361763,1.927313208580017,0.9014158844947815,0.3692654073238373,0.02876931242644787,10.866731643676758,0.06256306916475296,0.008257858455181122
|
||||
46,0.556227445602417,1.9039641618728638,0.8971729874610901,0.36716750264167786,0.02876931242644787,10.925694465637207,0.07202505320310593,0.009190197102725506
|
||||
47,0.5616573691368103,1.8822921514511108,0.8998004794120789,0.3756825923919678,0.02876931242644787,11.013936042785645,0.052527256309986115,0.007059136871248484
|
||||
48,0.5692777633666992,1.8465776443481445,0.9002607464790344,0.3834572434425354,0.02876931242644787,10.918274879455566,0.04959513992071152,0.00652637192979455
|
||||
49,0.5725480318069458,1.8282252550125122,0.9022663831710815,0.38934993743896484,0.02783697471022606,11.038796424865723,0.07169117778539658,0.010388918220996857
|
||||
50,0.5785332918167114,1.7933269739151,0.9021662473678589,0.3957362771034241,0.029035694897174835,11.16333293914795,0.050513699650764465,0.007858284749090672
|
||||
51,0.5812174081802368,1.7816730737686157,0.8998066782951355,0.4020300507545471,0.02703782543540001,11.185982704162598,0.04987102374434471,0.007725093048065901
|
||||
52,0.5866164565086365,1.754209041595459,0.8975144624710083,0.40662696957588196,0.03103356435894966,11.252925872802734,0.05906148999929428,0.00972296204417944
|
||||
53,0.5947613716125488,1.715588092803955,0.906348466873169,0.41711658239364624,0.03036760725080967,11.338024139404297,0.055601660162210464,0.008923814631998539
|
||||
54,0.5988337993621826,1.7050288915634155,0.9004661440849304,0.42313268780708313,0.02783697471022606,11.286218643188477,0.04987531155347824,0.007991475984454155
|
||||
55,0.5972912311553955,1.6938203573226929,0.8981159329414368,0.4220837354660034,0.027437400072813034,11.391554832458496,0.04240554943680763,0.0073255193419754505
|
||||
56,0.6028445363044739,1.6784948110580444,0.9032028913497925,0.431524395942688,0.024773575365543365,11.482613563537598,0.04264706000685692,0.007725093048065901
|
||||
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102,0.7081418037414551,1.173882007598877,0.8996544480323792,0.5944219827651978,0.027304209768772125,13.029313087463379,0.046207498759031296,0.014118273742496967
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133,0.7357233166694641,1.0443423986434937,0.903075635433197,0.6404529213905334,0.021443793550133705,13.764057159423828,0.0294736847281456,0.011188065633177757
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381,0.8279702663421631,0.6550795435905457,0.91835618019104,0.7756147384643555,0.02623867802321911,16.035493850708008,0.03211937099695206,0.01691528968513012
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382,0.8252861499786377,0.6629602313041687,0.9197481274604797,0.7750594019889832,0.023974427953362465,16.030113220214844,0.02857142873108387,0.015050612390041351
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388,0.8265202045440674,0.6666960120201111,0.9170145392417908,0.7793477773666382,0.024107618257403374,16.023324966430664,0.02933065965771675,0.015583377331495285
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389,0.828217089176178,0.6497079133987427,0.918266236782074,0.7778052091598511,0.022376131266355515,16.078550338745117,0.027033984661102295,0.013985082507133484
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396,0.828001081943512,0.6469826102256775,0.9185999631881714,0.7781445980072021,0.028902504593133926,16.06393814086914,0.030811622738838196,0.016382524743676186
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397,0.8288032412528992,0.6517045497894287,0.919562816619873,0.778761625289917,0.022109748795628548,16.122154235839844,0.02536783367395401,0.013319126330316067
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405,0.8260574340820312,0.664846658706665,0.9164173603057861,0.774966835975647,0.025439530611038208,16.121021270751953,0.02908385917544365,0.01598295196890831
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408,0.8273532390594482,0.6567899584770203,0.9172120690345764,0.7782988548278809,0.024640383198857307,16.01740074157715,0.02875318005681038,0.015050612390041351
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410,0.8299139142036438,0.6414439082145691,0.9159913063049316,0.7811063528060913,0.025705913081765175,16.185644149780273,0.029301276430487633,0.015583377331495285
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411,0.830129861831665,0.645475447177887,0.9171627759933472,0.7795020341873169,0.024906765669584274,16.22943878173828,0.02956393174827099,0.01598295196890831
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412,0.8292043209075928,0.6419966220855713,0.9168751835823059,0.779964804649353,0.02517314814031124,16.242097854614258,0.029802152886986732,0.015849759802222252
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413,0.8286489844322205,0.641472578048706,0.9187912940979004,0.7804893255233765,0.025039957836270332,16.13872718811035,0.02718062698841095,0.014651038683950901
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414,0.8267362117767334,0.6506471633911133,0.9171057939529419,0.7785764932632446,0.026505060493946075,16.166034698486328,0.02991347387433052,0.01611614227294922
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416,0.8279085755348206,0.6486541628837585,0.9183577299118042,0.7804893255233765,0.024240810424089432,16.136459350585938,0.027813218533992767,0.014517847448587418
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417,0.8288958072662354,0.6414381265640259,0.9192797541618347,0.7796562910079956,0.023974427953362465,16.208248138427734,0.02790234237909317,0.014917421154677868
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418,0.8288032412528992,0.6416102647781372,0.9201772212982178,0.7817233800888062,0.02623867802321911,16.26786231994629,0.031320471316576004,0.016648907214403152
|
||||
419,0.8288649320602417,0.6421637535095215,0.9157834053039551,0.7810137867927551,0.023308470845222473,16.137784957885742,0.02688172087073326,0.013985082507133484
|
||||
420,0.8303766846656799,0.6369450092315674,0.9153620600700378,0.7811063528060913,0.02091102860867977,16.334096908569336,0.024289531633257866,0.013319126330316067
|
||||
421,0.8267053365707397,0.6583683490753174,0.9175845980644226,0.7786998748779297,0.022109748795628548,16.290664672851562,0.026633786037564278,0.014384656213223934
|
||||
BIN
RNN/trained_models/omicron-lstm-weights.hdf5
Normal file
BIN
RNN/trained_models/omicron-lstm-weights.hdf5
Normal file
Binary file not shown.
53
RNN/trained_models/omicron_lstm.log
Normal file
53
RNN/trained_models/omicron_lstm.log
Normal file
@ -0,0 +1,53 @@
|
||||
epoch,accuracy,loss,precision,recall
|
||||
0,0.7614441514015198,0.8092082142829895,0.8728103041648865,0.6518684029579163
|
||||
1,0.7671184539794922,0.7907419800758362,0.8782083988189697,0.6588951349258423
|
||||
2,0.7755563855171204,0.7747973203659058,0.8812822699546814,0.6676270961761475
|
||||
3,0.779995858669281,0.7407286763191223,0.8845013976097107,0.6756828427314758
|
||||
4,0.7850233912467957,0.7300142645835876,0.886836588382721,0.6803869009017944
|
||||
5,0.7795842885971069,0.7503414154052734,0.8848004341125488,0.6758592128753662
|
||||
6,0.7788198590278625,0.7509223818778992,0.8803386092185974,0.6757122278213501
|
||||
7,0.7890512347221375,0.718399167060852,0.8869243264198303,0.6902067065238953
|
||||
8,0.7979596257209778,0.6898294687271118,0.8934946656227112,0.7034369111061096
|
||||
9,0.7928733229637146,0.6943138241767883,0.8882777690887451,0.6979978084564209
|
||||
10,0.7944315671920776,0.6911575794219971,0.8885570168495178,0.6997324824333191
|
||||
11,0.8051333427429199,0.6637961268424988,0.8919206857681274,0.7130802869796753
|
||||
12,0.801340639591217,0.6680640578269958,0.8919448256492615,0.70937579870224
|
||||
13,0.802457869052887,0.6730897426605225,0.8919108510017395,0.7096110582351685
|
||||
14,0.7929909229278564,0.7038369178771973,0.8853718042373657,0.7014670968055725
|
||||
15,0.7998706102371216,0.6797693967819214,0.8896541595458984,0.7063769698143005
|
||||
16,0.8064857721328735,0.6524642109870911,0.8916356563568115,0.7189604043960571
|
||||
17,0.801634669303894,0.6665034294128418,0.8875302076339722,0.7127274870872498
|
||||
18,0.8097198009490967,0.6495258808135986,0.894097089767456,0.7223120331764221
|
||||
19,0.8071619868278503,0.6461054682731628,0.8926549553871155,0.7231940627098083
|
||||
20,0.8145120739936829,0.6293599009513855,0.8963528871536255,0.7312498092651367
|
||||
21,0.8266839385032654,0.5887758135795593,0.9055382609367371,0.744627058506012
|
||||
22,0.8124834895133972,0.6240927577018738,0.893752932548523,0.7281039357185364
|
||||
23,0.8164525032043457,0.6143172979354858,0.8947745561599731,0.7345132827758789
|
||||
24,0.8197748064994812,0.6053867936134338,0.8968208432197571,0.7398053407669067
|
||||
25,0.8155117034912109,0.615023136138916,0.8943008184432983,0.7363067269325256
|
||||
26,0.8240672945976257,0.5888757705688477,0.8999822735786438,0.7452444434165955
|
||||
27,0.8297121524810791,0.5684961676597595,0.9025516510009766,0.752918004989624
|
||||
28,0.8239790797233582,0.588928759098053,0.899597704410553,0.7494487166404724
|
||||
29,0.8223032355308533,0.5940691232681274,0.8965772986412048,0.7462734580039978
|
||||
30,0.8363860845565796,0.5579916834831238,0.9069669842720032,0.7624143958091736
|
||||
31,0.8227442502975464,0.6085800528526306,0.8956509232521057,0.7459501028060913
|
||||
32,0.8282127380371094,0.5747796893119812,0.899915874004364,0.755005419254303
|
||||
33,0.8385029435157776,0.5406728386878967,0.9084644317626953,0.7674124836921692
|
||||
34,0.8382089138031006,0.5410904288291931,0.9048228859901428,0.768353283405304
|
||||
35,0.8184223771095276,0.609982430934906,0.8922887444496155,0.7450386881828308
|
||||
36,0.8366212844848633,0.5514355301856995,0.9045984148979187,0.7669126391410828
|
||||
37,0.8435598015785217,0.5238895416259766,0.9065799713134766,0.7749096155166626
|
||||
38,0.8374739289283752,0.5577998757362366,0.9009860157966614,0.768353283405304
|
||||
39,0.8336518406867981,0.5553674101829529,0.8994195461273193,0.7653250098228455
|
||||
40,0.8411489725112915,0.5325552225112915,0.9057586193084717,0.7773498296737671
|
||||
41,0.8474701046943665,0.510323166847229,0.9075850248336792,0.784494161605835
|
||||
42,0.8425601720809937,0.5326884984970093,0.9025572538375854,0.777202844619751
|
||||
43,0.8458530306816101,0.5184728503227234,0.9071467518806458,0.7807015180587769
|
||||
44,0.8346808552742004,0.557343602180481,0.8976917862892151,0.7695292830467224
|
||||
45,0.8469408750534058,0.5145681500434875,0.9105383157730103,0.785199761390686
|
||||
46,0.8492341041564941,0.5103574991226196,0.9106293320655823,0.787875235080719
|
||||
47,0.8546438217163086,0.48868727684020996,0.9114149808883667,0.7955487370491028
|
||||
48,0.8562314510345459,0.4850415587425232,0.9128401875495911,0.7978125810623169
|
||||
49,0.8366506695747375,0.5457478165626526,0.9022435545921326,0.7744391560554504
|
||||
50,0.8526445627212524,0.49833357334136963,0.9100509285926819,0.7930203080177307
|
||||
51,0.853585422039032,0.4867699444293976,0.9094598889350891,0.7970775961875916
|
||||
27
RNN/transformer.py
Normal file
27
RNN/transformer.py
Normal file
@ -0,0 +1,27 @@
|
||||
import gpt_2_simple as gpt2
|
||||
|
||||
import os
|
||||
#import requests
|
||||
|
||||
model_name = "124M"
|
||||
if not os.path.isdir(os.path.join("models", model_name)):
|
||||
print(f"Downloading {model_name} model...")
|
||||
gpt2.download_gpt2(model_name=model_name) # model is saved into current directory under /models/124M/
|
||||
|
||||
|
||||
file_name = "metallica_hendrix.txt"
|
||||
#if not os.path.isfile(file_name):
|
||||
# url = "https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt"
|
||||
# data = requests.get(url)
|
||||
#
|
||||
# with open(file_name, 'w') as f:
|
||||
# f.write(data.text)
|
||||
|
||||
|
||||
sess = gpt2.start_tf_sess()
|
||||
gpt2.finetune(sess,
|
||||
file_name,
|
||||
model_name=model_name,
|
||||
steps=1000) # steps is max number of training steps
|
||||
|
||||
gpt2.generate(sess)
|
||||
Loading…
Reference in New Issue
Block a user