import sys

import tensorflow as tf

from google.protobuf import text_format

from inputs import FeatureColumns, InputFn

VOCAB_FILE='/home/alan/Workspace/other/fastText/data/ag_news.train.vocab'

VOCAB_SIZE=95810

INPUT_FILE='/home/alan/Workspace/other/fastText/data/ag_news.train.tfrecords-1-of-1'

def test_parse_spec():

fc = FeatureColumns(

True,

False,

VOCAB_FILE,

VOCAB_SIZE,

10,

10,

1000,

10)

parse_spec = tf.feature_column.make_parse_example_spec(fc)

print parse_spec

reader = tf.python_io.tf_record_iterator(INPUT_FILE)

sess = tf.Session()

for record in reader:

example = tf.parse_single_example(

record,

parse_spec)

print sess.run(example)

break

def test_reading_inputs():

parse_spec = {

"text": tf.VarLenFeature(tf.string),

"label": tf.FixedLenFeature(shape=(1,), dtype=tf.int64,

default_value=None)

}

sess = tf.Session()

reader = tf.python_io.tf_record_iterator(INPUT_FILE)

ESZ = 4

HSZ = 100

NC = 4

n = 0

text_lookup_table = tf.contrib.lookup.index_table_from_file(

VOCAB_FILE, 10, VOCAB_SIZE)

text_embedding_w = tf.Variable(tf.random_uniform(

[VOCAB_SIZE, ESZ], -1.0, 1.0))

sess.run([tf.tables_initializer()])

for record in reader:

example = tf.parse_single_example(

record,

parse_spec)

text = example["text"]

labels = tf.subtract(example["label"], 1)

text_ids = text_lookup_table.lookup(text)

dense = tf.sparse_tensor_to_dense(text_ids)

print dense.shape

text_embedding = tf.reduce_mean(tf.nn.embedding_lookup(

text_embedding_w, dense), axis=-2)

print text_embedding.shape

text_embedding = tf.expand_dims(text_embedding, -2)

print text_embedding.shape

text_embedding_2 = tf.contrib.layers.bow_encoder(

dense, VOCAB_SIZE, ESZ)

print text_embedding_2.shape

num_classes = 2

logits = tf.contrib.layers.fully_connected(

inputs=text_embedding, num_outputs=4,

activation_fn=None)

sess.run([tf.global_variables_initializer()])

loss = tf.nn.sparse_softmax_cross_entropy_with_logits(

labels=labels, logits=logits)

x = sess.run([text_embedding, text_embedding_2, logits, labels, loss])

print(len(x), list(str(x[i]) for i in range(len(x))))

if n > 2:

break

n += 1

if __name__ == '__main__':

print "Test Parse Spec:"

test_parse_spec()

print "Test Input Fn"

test_reading_inputs()