def test_sample(self, word_list, sess):
# generate sequence structure and input dictionary
state, feed_dict = rnn.sequence_rnn(self.rnn_cell, word_list, self.word_model, self.first_train)
H = tf.matmul(state, self.W) + self.B
output = tf.nn.softmax(H)
return sess.run(output, feed_dict=feed_dict)
def train_sample(self, word_list, label, sess):
# generate sequence structure and input dictionary
state, feed_dict = rnn.sequence_rnn(self.rnn_cell, word_list, self.word_model, self.first_train)
# init label vector
label_vec = np.zeros((1, self.class_num))
label_vec[0, label] = 1
feed_dict[self.target] = label_vec
# latest layer (softmax)
#with tf.device('/cpu:0'):
H = tf.matmul(state, self.W) + self.B
output = tf.nn.softmax(H)
cost = tf.reduce_sum(-tf.log(output+0.00001)*self.target)
optimizer = tf.train.GradientDescentOptimizer(self.learn_rate).minimize(cost)
if self.first_train:
sess.run(tf.initialize_all_variables())
self.first_train = False
#print sess.run(output, feed_dict=feed_dict)
sess.run(optimizer, feed_dict=feed_dict)