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my_rnn.py
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my_rnn.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Mar 16 18:41:38 2020
@author: xugang
"""
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import LSTM, Bidirectional
class BiRNN_SS(keras.Model):
def __init__(self, num_layers, units, rate, \
ss8_output, ss3_output):
super(BiRNN_SS, self).__init__()
self.num_layers = num_layers
self.birnn_layers = [
Bidirectional(LSTM(units, dropout=rate, return_sequences=True), merge_mode='concat')
for _ in range(self.num_layers)]
self.dropout = keras.layers.Dropout(rate)
self.ss8_layer = keras.layers.Dense(ss8_output)
self.ss3_layer = keras.layers.Dense(ss3_output)
def call(self, x, x_mask, training):
# x.shape (batch_size, max_seq_length, embeded_size)
# x_mask.shape (batch_size, timesteps).
x_mask = tf.math.logical_not(tf.math.equal(x_mask, 1))
for i in range(self.num_layers):
x = self.birnn_layers[i](x, mask=x_mask, training=training)
x = tf.nn.relu(x)
x = self.dropout(x, training=training)
# predictions.shape: (batch_size, input_seq_len, output_size)
ss8_predictions = self.ss8_layer(x)
ss3_predictions = self.ss3_layer(x)
return ss8_predictions, ss3_predictions
class BiRNN_PP(keras.Model):
def __init__(self, num_layers, units, rate, \
phipsi_output):
super(BiRNN_PP, self).__init__()
self.num_layers = num_layers
self.birnn_layers = [
Bidirectional(LSTM(units, dropout=rate, return_sequences=True), merge_mode='concat')
for _ in range(self.num_layers)]
self.dropout = keras.layers.Dropout(rate)
self.phipsi_layer = keras.layers.Dense(phipsi_output)
def call(self, x, x_mask, training):
# x.shape (batch_size, max_seq_length, embeded_size)
# x_mask.shape (batch_size, timesteps).
x_mask = tf.math.logical_not(tf.math.equal(x_mask, 1))
for i in range(self.num_layers):
x = self.birnn_layers[i](x, mask=x_mask, training=training)
x = tf.nn.relu(x)
x = self.dropout(x, training=training)
# predictions.shape: (batch_size, input_seq_len, output_size)
phipsi_predictions = self.phipsi_layer(x)
return phipsi_predictions
class BiRNN_C2(keras.Model):
def __init__(self, num_layers, units, rate, \
ss8_output, ss3_output, phipsi_output):
super(BiRNN_C2, self).__init__()
self.num_layers = num_layers
self.birnn_layers = [
Bidirectional(LSTM(units, dropout=rate, return_sequences=True), merge_mode='concat')
for _ in range(self.num_layers)]
self.dropout = keras.layers.Dropout(rate)
self.ss8_layer = keras.layers.Dense(ss8_output)
self.ss3_layer = keras.layers.Dense(ss3_output)
self.phipsi_layer = keras.layers.Dense(phipsi_output)
def call(self, x, x_mask, training):
# x.shape (batch_size, max_seq_length, embeded_size)
# x_mask.shape (batch_size, timesteps).
x_mask = tf.math.logical_not(tf.math.equal(x_mask, 1))
for i in range(self.num_layers):
x = self.birnn_layers[i](x, mask=x_mask, training=training)
x = tf.nn.relu(x)
x = self.dropout(x, training=training)
# predictions.shape: (batch_size, input_seq_len, output_size)
ss8_predictions = self.ss8_layer(x)
ss3_predictions = self.ss3_layer(x)
phipsi_predictions = self.phipsi_layer(x)
return ss8_predictions, ss3_predictions, phipsi_predictions
class BiRNN_C3(keras.Model):
def __init__(self, num_layers, units, rate, \
ss8_output, ss3_output, phipsi_output, csf_output):
super(BiRNN_C3, self).__init__()
self.num_layers = num_layers
self.birnn_layers = [
Bidirectional(LSTM(units, dropout=rate, return_sequences=True), merge_mode='concat')
for _ in range(self.num_layers)]
self.dropout = keras.layers.Dropout(rate)
self.ss8_layer = keras.layers.Dense(ss8_output)
self.ss3_layer = keras.layers.Dense(ss3_output)
self.phipsi_layer = keras.layers.Dense(phipsi_output)
self.csf_layer = keras.layers.Dense(csf_output)
def call(self, x, x_mask, training):
# x.shape (batch_size, max_seq_length, embeded_size)
# x_mask.shape (batch_size, timesteps).
x_mask = tf.math.logical_not(tf.math.equal(x_mask, 1))
for i in range(self.num_layers):
x = self.birnn_layers[i](x, mask=x_mask, training=training)
x = tf.nn.relu(x)
x = self.dropout(x, training=training)
# predictions.shape: (batch_size, input_seq_len, output_size)
ss8_predictions = self.ss8_layer(x)
ss3_predictions = self.ss3_layer(x)
phipsi_predictions = self.phipsi_layer(x)
csf_predictions = self.csf_layer(x)
return ss8_predictions, ss3_predictions, phipsi_predictions, csf_predictions
class BiRNN_C4(keras.Model):
def __init__(self, num_layers, units, rate,
ss8_output, ss3_output, phipsi_output, csf_output, asa_output):
super(BiRNN_C4, self).__init__()
self.num_layers = num_layers
self.birnn_layers = [
Bidirectional(LSTM(units, dropout=rate, return_sequences=True), merge_mode='concat')
for _ in range(self.num_layers)]
self.dropout = keras.layers.Dropout(rate)
self.ss8_layer = keras.layers.Dense(ss8_output)
self.ss3_layer = keras.layers.Dense(ss3_output)
self.phipsi_layer = keras.layers.Dense(phipsi_output)
self.csf_layer = keras.layers.Dense(csf_output)
self.asa_layer = keras.layers.Dense(asa_output)
def call(self, x, x_mask, training):
# x.shape (batch_size, max_seq_length, embeded_size)
# x_mask.shape (batch_size, timesteps).
x_mask = tf.math.logical_not(tf.math.equal(x_mask, 1))
for i in range(self.num_layers):
x = self.birnn_layers[i](x, mask=x_mask, training=training)
x = tf.nn.relu(x)
x = self.dropout(x, training=training)
# predictions.shape: (batch_size, input_seq_len, output_size)
ss8_predictions = self.ss8_layer(x)
ss3_predictions = self.ss3_layer(x)
phipsi_predictions = self.phipsi_layer(x)
csf_predictions = self.csf_layer(x)
asa_predictions = self.asa_layer(x)
return ss8_predictions, ss3_predictions, phipsi_predictions, csf_predictions, asa_predictions
class BiRNN_C5(keras.Model):
def __init__(self, num_layers, units, rate,
ss8_output, ss3_output, phipsi_output, csf_output, asa_output, rota_output):
super(BiRNN_C5, self).__init__()
self.num_layers = num_layers
self.birnn_layers = [
Bidirectional(LSTM(units, dropout=rate, return_sequences=True), merge_mode='concat')
for _ in range(self.num_layers)]
self.dropout = keras.layers.Dropout(rate)
self.ss8_layer = keras.layers.Dense(ss8_output)
self.ss3_layer = keras.layers.Dense(ss3_output)
self.phipsi_layer = keras.layers.Dense(phipsi_output)
self.csf_layer = keras.layers.Dense(csf_output)
self.asa_layer = keras.layers.Dense(asa_output)
self.rota_layer = keras.layers.Dense(rota_output)
def call(self, x, x_mask, training):
# x.shape (batch_size, max_seq_length, embeded_size)
# x_mask.shape (batch_size, timesteps).
x_mask = tf.math.logical_not(tf.math.equal(x_mask, 1))
for i in range(self.num_layers):
x = self.birnn_layers[i](x, mask=x_mask, training=training)
x = tf.nn.relu(x)
x = self.dropout(x, training=training)
# predictions.shape: (batch_size, input_seq_len, output_size)
ss8_predictions = self.ss8_layer(x)
ss3_predictions = self.ss3_layer(x)
phipsi_predictions = self.phipsi_layer(x)
csf_predictions = self.csf_layer(x)
asa_predictions = self.asa_layer(x)
rota_predictions = self.rota_layer(x)
return ss8_predictions, ss3_predictions, phipsi_predictions, csf_predictions, asa_predictions, rota_predictions