Can not collect filtered string features information about deeprec

[candyzone]

Thanks for your issue and we will check it.

[candyzone]

it works in my testcase.

import tensorflow as tf
import os

checkpoint_directory = '/tmp/1'
with tf.device("/cpu:0"):
columns = tf.feature_column.categorical_column_with_embedding("col_emb", dtype=tf.string,
ev_option = tf.EmbeddingVariableOption(filter_option=tf.CounterFilter(filter_freq=3)))
W = tf.feature_column.embedding_column(columns,
dimension=3,
initializer=tf.ones_initializer(tf.float32))
ids={}
ids["col_emb"] = tf.SparseTensor(indices=[[0,0],[1,0],[2,0],[3,0],[4,0],[5,0]], values=['1','1','1','2','2','3'], dense_shape=[6, 1])
emb = tf.feature_column.input_layer(ids, [W])
fun = tf.multiply(emb, 2.0, name='multiply')
loss = tf.reduce_sum(fun, name='reduce_sum')
gs = tf.train.get_or_create_global_step()
opt = tf.train.AdagradOptimizer(0.1)
g_v = opt.compute_gradients(loss)
train_op = opt.apply_gradients(g_v, gs)
saver = tf.train.Saver()
init = tf.global_variables_initializer()
model_path = os.path.join(checkpoint_directory,
"model1.ckpt")
with tf.Session() as sess:
sess.run([init])
sess.run([train_op])
save_path = saver.save(sess, model_path)
for name, shape in tf.train.list_variables(model_path):
print(name, shape)

[dixingxing0]

We discovered that our string column wasn't a SparseTensor. After making a minor adjustment, it functioned as expected. Thanks @candyzone .