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Dorchies David authoredb3c92b47
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import sys
import os
import numpy as np
import math
from operator import itemgetter, attrgetter, methodcaller
import tensorflow as tf
from tensorflow.contrib import rnn
import random
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_recall_fscore_support
from sklearn.metrics import f1_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.utils import shuffle
from sklearn.metrics import confusion_matrix
def Bgru(x, nunits, nlayer, timesteps, nclasses, dropout):
#Processing input tensor array of (?,16)
x = tf.unstack(x,timesteps,1)
b_cell = None
f_cell = None
b_cell = rnn.GRUCell(nunits)
f_cell = rnn.GRUCell(nunits)
f_cell = tf.nn.rnn_cell.DropoutWrapper(f_cell, output_keep_prob=dropout)
b_cell = tf.nn.rnn_cell.DropoutWrapper(b_cell, output_keep_prob=dropout)
outputs,_,_=rnn.static_bidirectional_rnn(f_cell, b_cell, x, dtype=tf.float32)
outputs = tf.stack(outputs, axis=1)
return outputs
#def getPrediction(x_rnn, x_rnn_b, x_cnn, nunits, nlayer, n_classes, choice):
def getPrediction(x, nunits, nlayer, n_classes, dropout, is_training):
n_timetamps = 34
features = Bgru( x, nunits, nlayer, n_timetamps, n_classes, dropout )
# Trainable parameters
print "output ",features.get_shape()
attention_size = int(nunits)
print "units ",nunits
print "att size",attention_size
W_omega = tf.Variable(tf.random_normal([nunits*2, attention_size], stddev=0.1))
print "womega ",W_omega.get_shape()
b_omega = tf.Variable(tf.random_normal([attention_size], stddev=0.1))
u_omega = tf.Variable(tf.random_normal([attention_size], stddev=0.1))
# Applying fully connected layer with non-linear activation to each of the B*T timestamps;
# the shape of `v` is (B,T,D)*(D,A)=(B,T,A), where A=attention_size
v = tf.tanh(tf.tensordot(features, W_omega, axes=1) + b_omega)
# For each of the timestamps its vector of size A from `v` is reduced with `u` vector
vu = tf.tensordot(v, u_omega, axes=1) # (B,T) shape
alphas = tf.nn.softmax(vu) # (B,T) shape also
# Output of (Bi-)RNN is reduced with attention vector; the result has (B,D) shape
features = tf.reduce_sum(features * tf.expand_dims(alphas, -1), 1)
print "output", features.get_shape()
features = tf.reshape(features, [-1, nunits*2])
print "output ",features.get_shape()
prediction = tf.layers.dense( features, n_classes, activation=None, name='prediction')
print "prediction ",prediction.get_shape()
return prediction
def getBatch(X, Y, i, batch_size):
start_id = i*batch_size
end_id = min( (i+1) * batch_size, X.shape[0])
batch_x = X[start_id:end_id]
batch_y = Y[start_id:end_id]
return batch_x, batch_y
def getLabelFormat(Y):
vals = np.unique( np.array(Y) )
sorted(vals)
hash_val = {}
for el in vals:
hash_val[el] = len(hash_val.keys())
new_Y = []
for el in Y:
t = np.zeros(len(vals))
t[hash_val[el]] = 1.0
new_Y.append(t)
return np.array(new_Y)
def getRNNFormat(X):
#print X.shape
new_X = []
for row in X:
new_X.append( np.split(row, 34) )
return np.array(new_X)
#main
#Model parameters
batchsz = 64
hm_epochs = 400
n_levels_lstm = 1
drop_val = 0.6
#dropout = 0.2
#Data INformation
n_timestamps = 34
n_dims = 16
patch_window = 25
n_channels = 5
itr = int( sys.argv[1] )
p_split = 100*float( sys.argv[2] )
nunits = int(sys.argv[3])
path_in_ts = sys.argv[4]
path_in_gt = sys.argv[5]
g_path = './dataset/TS/%dx%d/'%( patch_window, patch_window )
train_x = np.load( path_in_ts+"train_x%d_%d.npy"%( itr, p_split ) )
train_y = np.load( path_in_gt+"train_y%d_%d.npy"%( itr, p_split ) )
n_classes = np.bincount( train_y ).shape[0]-1
train_y = getLabelFormat( train_y )
train_x = getRNNFormat( train_x )
directory = g_path+'Bgru_Attention_output0.6_%d_%d_%d/'%( p_split, batchsz, nunits)
if not os.path.exists(directory):
os.makedirs(directory)
path_out_model = directory+'modelTT%d/'%itr
if not os.path.exists(path_out_model):
os.makedirs(path_out_model)
#log File
flog = open( directory+"log.txt","a" )
x = tf.placeholder("float",[None,n_timestamps,n_dims],name="x_rnn")
y = tf.placeholder("float",[None,n_classes],name="y")
learning_rate = tf.placeholder(tf.float32, shape=(), name="learning_rate")
is_training_ph = tf.placeholder(tf.bool, shape=(), name="is_training")
dropout = tf.placeholder(tf.float32, shape=(), name="drop_rate")
sess = tf.InteractiveSession()
prediction = getPrediction(x, nunits, n_levels_lstm, n_classes, dropout, is_training_ph)
cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction) )
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
correct = tf.equal(tf.argmax(prediction,1),tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct,tf.float64))
tf.global_variables_initializer().run()
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
iterations = train_x.shape[0] / batchsz
if train_x.shape[0] % batchsz != 0:
iterations+=1
best_loss = sys.float_info.max
for e in range(hm_epochs):
lossi = 0
accS = 0
train_x, train_y = shuffle(train_x, train_y, random_state=0)
for ibatch in range(iterations):
#for ibatch in range(10):
#BATCH_X BATCH_Y: i-th batches of train_indices_x and train_y
batch_x, batch_y = getBatch(train_x, train_y, ibatch, batchsz)
acc,_,loss = sess.run([accuracy,optimizer,cost],feed_dict={x:batch_x,
#x_rnn_b:batch_rnn_x_b,
#x_cnn:batch_cnn_x,
y:batch_y,
is_training_ph:True,
dropout:drop_val,
learning_rate:0.0002})
lossi+=loss
accS+=acc
del batch_x
del batch_y
c_loss = float(lossi/iterations)
c_acc = float(accS/iterations)
print "Epoch:",e,"Train loss:",c_loss,"| Accuracy:",c_acc
flog.write("Epoch %d Train loss:%f | Accuracy: %f\n"%( e, c_loss, c_acc ))
if c_loss < best_loss:
best_loss = c_loss
save_path = saver.save( sess, path_out_model+'model', global_step = itr )
flog.close()