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import os |
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import gc |
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import numpy as np |
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import pandas as pd |
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import matplotlib.pyplot as plt |
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import seaborn as sns |
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from collections import Counter |
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from prettytable import PrettyTable |
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from IPython.display import Image |
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from sklearn.preprocessing import LabelEncoder |
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from keras.models import Model |
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from keras.regularizers import l2 |
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from keras.constraints import max_norm |
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from keras.utils import to_categorical |
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from keras.preprocessing.text import Tokenizer |
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from keras.utils import pad_sequences |
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from keras.callbacks import EarlyStopping |
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from keras.layers import Input, Dense, Dropout, Flatten, Activation |
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from keras.layers import Conv1D, Add, MaxPooling1D, BatchNormalization |
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from keras.layers import Embedding, Bidirectional, LSTM, CuDNNLSTM, GlobalMaxPooling1D |
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import tensorflow as tf |
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def residual_block(data, filters, d_rate): |
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""" |
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_data: input |
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_filters: convolution filters |
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_d_rate: dilation rate |
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""" |
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shortcut = data |
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bn1 = BatchNormalization()(data) |
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act1 = Activation('relu')(bn1) |
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conv1 = Conv1D(filters, 1, dilation_rate=d_rate, padding='same', kernel_regularizer=l2(0.001))(act1) |
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bn2 = BatchNormalization()(conv1) |
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act2 = Activation('relu')(bn2) |
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conv2 = Conv1D(filters, 3, padding='same', kernel_regularizer=l2(0.001))(act2) |
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x = Add()([conv2, shortcut]) |
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return x |
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def get_model(): |
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x_input = Input(shape=(100, 21)) |
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conv = Conv1D(128, 1, padding='same')(x_input) |
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res1 = residual_block(conv, 128, 2) |
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res2 = residual_block(res1, 128, 3) |
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x = MaxPooling1D(3)(res2) |
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x = Dropout(0.5)(x) |
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x = Flatten()(x) |
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x_output = Dense(1000, activation='softmax', kernel_regularizer=l2(0.0001))(x) |
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model2 = Model(inputs=x_input, outputs=x_output) |
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model2.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) |
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return model2 |
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