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acrnn1.py
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acrnn1.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 19 14:54:52 2018
@author: hexuanji
"""
import tensorflow as tf
from attention import attention
epsilon = 1e-3
def leaky_relu(x, leakiness=0.0):
return tf.where(tf.less(x, 0.0), leakiness * x, x, name='leaky_relu')
def batch_norm_wrapper(inputs, is_training, decay = 0.999):
scale = tf.Variable(tf.ones([inputs.get_shape()[-1]]))
beta = tf.Variable(tf.zeros([inputs.get_shape()[-1]]))
pop_mean = tf.Variable(tf.zeros([inputs.get_shape()[-1]]), trainable=False)
pop_var = tf.Variable(tf.ones([inputs.get_shape()[-1]]), trainable=False)
if is_training is not None:
batch_mean, batch_var = tf.nn.moments(inputs,[0])
train_mean = tf.assign(pop_mean,
pop_mean * decay + batch_mean * (1 - decay))
train_var = tf.assign(pop_var,
pop_var * decay + batch_var * (1 - decay))
with tf.control_dependencies([train_mean, train_var]):
return tf.nn.batch_normalization(inputs,
batch_mean, batch_var, beta, scale, epsilon)
else:
return tf.nn.batch_normalization(inputs,
pop_mean, pop_var, beta, scale, epsilon)
def acrnn(inputs, num_classes=4,
is_training=True,
L1=128,
L2=256,
cell_units=128,
num_linear=768,
p=10,
time_step=150,
F1=64,
dropout_keep_prob=1):
layer1_filter = tf.get_variable('layer1_filter', shape=[5, 3, 3, L1], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.1))
layer1_bias = tf.get_variable('layer1_bias', shape=[L1], dtype=tf.float32,
initializer=tf.constant_initializer(0.1))
layer1_stride = [1, 1, 1, 1]
layer2_filter = tf.get_variable('layer2_filter', shape=[5, 3, L1, L2], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.1))
layer2_bias = tf.get_variable('layer2_bias', shape=[L2], dtype=tf.float32,
initializer=tf.constant_initializer(0.1))
layer2_stride = [1, 1, 1, 1]
layer3_filter = tf.get_variable('layer3_filter', shape=[5, 3, L2, L2], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.1))
layer3_bias = tf.get_variable('layer3_bias', shape=[L2], dtype=tf.float32,
initializer=tf.constant_initializer(0.1))
layer3_stride = [1, 1, 1, 1]
layer4_filter = tf.get_variable('layer4_filter', shape=[5, 3, L2, L2], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.1))
layer4_bias = tf.get_variable('layer4_bias', shape=[L2], dtype=tf.float32,
initializer=tf.constant_initializer(0.1))
layer4_stride = [1, 1, 1, 1]
layer5_filter = tf.get_variable('layer5_filter', shape=[5, 3, L2, L2], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.1))
layer5_bias = tf.get_variable('layer5_bias', shape=[L2], dtype=tf.float32,
initializer=tf.constant_initializer(0.1))
layer5_stride = [1, 1, 1, 1]
layer6_filter = tf.get_variable('layer6_filter', shape=[5, 3, L2, L2], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.1))
layer6_bias = tf.get_variable('layer6_bias', shape=[L2], dtype=tf.float32,
initializer=tf.constant_initializer(0.1))
layer6_stride = [1, 1, 1, 1]
linear1_weight = tf.get_variable('linear1_weight', shape=[p*L2,num_linear], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.1))
linear1_bias = tf.get_variable('linear1_bias', shape=[num_linear], dtype=tf.float32,
initializer=tf.constant_initializer(0.1))
fully1_weight = tf.get_variable('fully1_weight', shape=[2*cell_units,F1], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.1))
fully1_bias = tf.get_variable('fully1_bias', shape=[F1], dtype=tf.float32,
initializer=tf.constant_initializer(0.1))
fully2_weight = tf.get_variable('fully2_weight', shape=[F1,num_classes], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.1))
fully2_bias = tf.get_variable('fully2_bias', shape=[num_classes], dtype=tf.float32,
initializer=tf.constant_initializer(0.1))
layer1 = tf.nn.conv2d(inputs, layer1_filter, layer1_stride, padding='SAME')
layer1 = tf.nn.bias_add(layer1,layer1_bias)
layer1 = leaky_relu(layer1, 0.01)
layer1 = tf.nn.max_pool(layer1,ksize=[1, 2, 4, 1], strides=[1, 2, 4, 1], padding='VALID', name='max_pool')
layer1 = tf.contrib.layers.dropout(layer1, keep_prob=dropout_keep_prob, is_training=is_training)
layer2 = tf.nn.conv2d(layer1, layer2_filter, layer2_stride, padding='SAME')
layer2 = tf.nn.bias_add(layer2,layer2_bias)
layer2 = leaky_relu(layer2, 0.01)
layer2 = tf.contrib.layers.dropout(layer2, keep_prob=dropout_keep_prob, is_training=is_training)
layer3 = tf.nn.conv2d(layer2, layer3_filter, layer3_stride, padding='SAME')
layer3 = tf.nn.bias_add(layer3,layer3_bias)
layer3 = leaky_relu(layer3, 0.01)
layer3 = tf.contrib.layers.dropout(layer3, keep_prob=dropout_keep_prob, is_training=is_training)
layer4 = tf.nn.conv2d(layer3, layer4_filter, layer4_stride, padding='SAME')
layer4 = tf.nn.bias_add(layer4,layer4_bias)
layer4 = leaky_relu(layer4, 0.01)
layer4 = tf.contrib.layers.dropout(layer4, keep_prob=dropout_keep_prob, is_training=is_training)
layer5 = tf.nn.conv2d(layer4, layer5_filter, layer5_stride, padding='SAME')
layer5 = tf.nn.bias_add(layer5,layer5_bias)
layer5 = leaky_relu(layer5, 0.01)
layer5 = tf.contrib.layers.dropout(layer5, keep_prob=dropout_keep_prob, is_training=is_training)
layer6 = tf.nn.conv2d(layer5, layer6_filter, layer6_stride, padding='SAME')
layer6 = tf.nn.bias_add(layer6,layer6_bias)
layer6 = leaky_relu(layer6, 0.01)
layer6 = tf.contrib.layers.dropout(layer6, keep_prob=dropout_keep_prob, is_training=is_training)
layer6 = tf.reshape(layer6,[-1,time_step,L2*p])
layer6 = tf.reshape(layer6, [-1,p*L2])
linear1 = tf.matmul(layer6,linear1_weight) + linear1_bias
linear1 = batch_norm_wrapper(linear1,is_training)
linear1 = leaky_relu(linear1, 0.01)
#linear1 = batch_norm_wrapper(linear1,is_training)
linear1 = tf.reshape(linear1, [-1, time_step, num_linear])
# Define lstm cells with tensorflow
# Forward direction cell
gru_fw_cell1 = tf.contrib.rnn.BasicLSTMCell(cell_units, forget_bias=1.0)
# Backward direction cell
gru_bw_cell1 = tf.contrib.rnn.BasicLSTMCell(cell_units, forget_bias=1.0)
# Now we feed `layer_3` into the LSTM BRNN cell and obtain the LSTM BRNN output.
outputs1, output_states1 = tf.nn.bidirectional_dynamic_rnn(cell_fw=gru_fw_cell1,
cell_bw=gru_bw_cell1,
inputs= linear1,
dtype=tf.float32,
time_major=False,
scope='LSTM1')
# Attention layer
gru, alphas = attention(outputs1, 1, return_alphas=True)
fully1 = tf.matmul(gru,fully1_weight) + fully1_bias
fully1 = leaky_relu(fully1, 0.01)
fully1 = tf.nn.dropout(fully1, dropout_keep_prob)
Ylogits = tf.matmul(fully1, fully2_weight) + fully2_bias
#Ylogits = tf.nn.softmax(Ylogits)
return Ylogits