A tensorflow version of dssm using textcnn as feature extractor.
training data using document's title as query, document's content as hit doc in dssm.
training data size 1300w+
paper: https://www.microsoft.com/en-us/research/publication/learning-semantic-representations-using-convolutional-neural-networks-for-web-search/

loss function = - p(Q,D+)

loss function = max{1 - p(Q,D+) + p(Q,D-),0}

In my practice:
little learning_rate, such as 0.001, easier to converge, when using 0.01 not coverge any more, loss jump largely.
small conv output,such as 64, easier to training
active function tanh get better,relu usally got nan loss

sentence_len = 1000
embedding_size = 128
batch_size = 500
vocab,vocab_idx = load_vocab()
vocab_size = len(vocab) # 4469
print 'vocab_size',vocab_size
#filter_sizes = [3,4,5]
filter_sizes = [1,2,3,4,5]
num_filters = 64
hidden_sizes = [embedding_size]
NEG = 4
learning_rate = 0.001
activeFn = tf.nn.tanh
model_path = "./model"
summary_path = "./train_summeray"