I am having difficulty searching for documentation, studies, or blogs that can help me in building text sequence (features) classifier. The text sequence that I have contains logs of network.
I am building a GRU model using TensorFlow, with an SVM as the classification function. I am having trouble with the tensor shapes. It says ValueError: Shape must be rank 2 but is rank 3 for 'MatMul' (op: 'MatMul') with input shapes: [?,23,1], [512,2]. Here is a sample of the data I am using for training my neural network.
The goal of my project is to use this GRU-SVM model for intrusion detection on Kyoto University's honeypot system intrusion detection dataset. The dataset has 23 features, and a label (if there is an intrusion in the network or none).
import data
import numpy as np
import os
import tensorflow as tf
BATCH_SIZE = 200
CELLSIZE = 512
NLAYERS = 3
SVMC = 1
learning_rate = 0.01
TRAIN_PATH = '/home/darth/GitHub Projects/gru_svm/dataset/train/6'
def main():
examples, labels, keys = data.input_pipeline(path=TRAIN_PATH, batch_size=BATCH_SIZE, num_epochs=1)
seqlen = examples.shape[1]
x = tf.placeholder(shape=[None, seqlen, 1], dtype=tf.float32)
y = tf.placeholder(shape=[None, 2], dtype=tf.float32)
Hin = tf.placeholder(shape=[None, CELLSIZE*NLAYERS], dtype=tf.float32)
# cell = tf.contrib.rnn.GRUCell(CELLSIZE)
network = []
for index in range(NLAYERS):
network.append(tf.contrib.rnn.GRUCell(CELLSIZE))
mcell = tf.contrib.rnn.MultiRNNCell(network, state_is_tuple=False)
Hr, H = tf.nn.dynamic_rnn(mcell, x, initial_state=Hin, dtype=tf.float32)
Hf = tf.transpose(Hr, [1, 0, 2])
last = tf.gather(Hf, int(Hf.get_shape()[0]) - 1)
weight = tf.Variable(tf.truncated_normal([CELLSIZE, 2], stddev=0.01), tf.float32)
bias = tf.Variable(tf.constant(0.1, shape=[2]))
logits = tf.matmul(last, weight) + bias
regularization_loss = 0.5 * tf.reduce_sum(tf.square(weight))
hinge_loss = tf.reduce_sum(tf.maximum(tf.zeros([BATCH_SIZE, 1]), 1 - y * logits))
loss = regularization_loss + SVMC * hinge_loss
train_step = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
with tf.Session() as sess:
sess.run(init_op)
train_loss = 0
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
try:
for index in range(100):
for j in range(1000):
example_batch, label_batch, key_batch = sess.run([examples, labels, keys])
_, train_loss_ = sess.run([train_step, loss],
feed_dict = { x : example_batch,
y : label_batch,
Hin : np.zeros([BATCH_SIZE, CELLSIZE * NLAYERS])
})
train_loss += train_loss_
print('[{}] loss : {}'.format(index, (train_loss / 1000)))
train_loss = 0
except tf.errors.OutOfRangeError:
print('EOF reached.')
except KeyboardInterrupt:
print('Interrupted by user at {}'.format(index))
finally:
coord.request_stop()
coord.join(threads)
main()
Note: The reason why I built my MultiRNNCell as I did (snippet isolated below) is because I was having an error similar to this post.
network = []
for index in range(NLAYERS):
network.append(tf.contrib.rnn.GRUCell(CELLSIZE))
Thank you in advance for your response!
Update 08/01/2017 The source was improved based on @jdehesa's sugestions:
import data
import numpy as np
import os
import tensorflow as tf
BATCH_SIZE = 200
CELLSIZE = 512
NLAYERS = 3
SVMC = 1
learning_rate = 0.01
TRAIN_PATH = '/home/darth/GitHub Projects/gru_svm/dataset/train/6'
def main():
examples, labels, keys = data.input_pipeline(path=TRAIN_PATH, batch_size=BATCH_SIZE, num_epochs=1)
seqlen = examples.shape[1]
x = tf.placeholder(shape=[None, seqlen, 1], dtype=tf.float32, name='x')
y_input = tf.placeholder(shape=[None], dtype=tf.int32, name='y_input')
y = tf.one_hot(y_input, 2, dtype=tf.float32, name='y')
Hin = tf.placeholder(shape=[None, CELLSIZE*NLAYERS], dtype=tf.float32, name='Hin')
network = []
for index in range(NLAYERS):
network.append(tf.contrib.rnn.GRUCell(CELLSIZE))
mcell = tf.contrib.rnn.MultiRNNCell(network, state_is_tuple=False)
Hr, H = tf.nn.dynamic_rnn(mcell, x, initial_state=Hin, dtype=tf.float32)
Hf = tf.transpose(Hr, [1, 0, 2])
last = tf.gather(Hf, int(Hf.get_shape()[0]) - 1)
weight = tf.Variable(tf.truncated_normal([CELLSIZE, 2], stddev=0.01), tf.float32, name='weights')
bias = tf.Variable(tf.constant(0.1, shape=[2]), name='bias')
logits = tf.matmul(last, weight) + bias
regularization_loss = 0.5 * tf.reduce_sum(tf.square(weight))
hinge_loss = tf.reduce_sum(tf.maximum(tf.zeros([BATCH_SIZE, 1]), 1 - y * logits))
loss = regularization_loss + SVMC * hinge_loss
train_step = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
with tf.Session() as sess:
sess.run(init_op)
train_loss = 0
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
try:
for index in range(100):
example_batch, label_batch, key_batch = sess.run([examples, labels, keys])
_, train_loss_ = sess.run([train_step, loss],
feed_dict = { x : example_batch[..., np.newaxis],
y_input : label_batch,
Hin : np.zeros([BATCH_SIZE, CELLSIZE * NLAYERS])
})
train_loss += train_loss_
print('[{}] loss : {}'.format(index, (train_loss / 1000)))
print('Weights : {}'.format(sess.run(weight)))
print('Biases : {}'.format(sess.run(bias)))
train_loss = 0
except tf.errors.OutOfRangeError:
print('EOF reached.')
except KeyboardInterrupt:
print('Interrupted by user at {}'.format(index))
finally:
coord.request_stop()
coord.join(threads)
main()
My next move is to validate if the results I'm getting are correct.
