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