Create model.py

model.py

@@ -0,0 +1,73 @@
+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