How to do sparse input text classification(dnn) using tensorflow

You can get complete example code from

https://github.com/chenghuige/hasky/tree/master/applications

   

Including

  1. How to parse libsvm dataset file to tfrecords
  2. Reading tfrecords and do dnn/logistic regresssion classifciation/regresssion
  3. Train + evaluate
  4. See train process (loss and metric track) in tensorboard
  5. Show how to use melt.train_flow to handle all other things(optimizer, learning rate, model saving, log …)

   

The main realated code:

melt.tfrecords/libsvm_decode #parsing libsvm file

melt.models.mlp

   

def forward(inputs,

num_outputs,

input_dim=None,

hiddens=[200],

activation_fn=tf.nn.relu,

weights_initializer=initializers.xavier_initializer(),

weights_regularizer=None,

biases_initializer=init_ops.zeros_initializer(),

biases_regularizer=None,

reuse=None,

scope=None

):

   

text-classfication/model.py shows how to use this

   

You must specify num_outputs and input_dim for sparse input dataset

For example 10 classes classficiation problem then num_outputs=10

If you do regresssion then num_outputs=1

input_dim should be the same as your dataset num input features

   

   

You may change hiddens, the default is [200], means only 1 hidden layer size 200,

You can use more hiddens like [200, 100, 100] means 3 hidden layers with size 200,100,100

You may also set hiddens [] empty , means you only do logistic regression

   

What's the diff between melt.layers.fully_connected and tf.contrib.layers.fully_connected?

Well similary but we will also deal with sparse input, the main difference in here

We use melt.matmul

def matmul(X, w):

  

  

if isinstance(X, tf.Tensor):

  

return tf.matmul(X,w)

  

else:

  

#X[0] index, X[1] value

  

return tf.nn.embedding_lookup_sparse(w, X[0], X[1], combiner='sum')

   

来自 <https://github.com/chenghuige/tensorflow-example/blob/master/util/melt/ops/ops.py>

   

   

   

   

Tensorboard show:

   

   

   

   

posted @ 2017-02-26 13:19  阁子  阅读(...)  评论(... 编辑 收藏