python - reading data in tensorflow - TypeError("%s that don't all match." % prefix) -

i trying load following data file (with 225805 rows) in tensor flow. data file looks this:

1,1,0.05,-1.05 1,1,0.1,-1.1 1,1,0.15,-1.15 1,1,0.2,-1.2 1,1,0.25,-1.25 1,1,0.3,-1.3 1,1,0.35,-1.35 

the code reads data is

import tensorflow tf  # read in data filename_queue = tf.train.string_input_producer(["~/input.data"]) reader = tf.textlinereader() key, value = reader.read(filename_queue)  record_defaults = [tf.constant([], dtype=tf.int32),    # column 1                    tf.constant([], dtype=tf.int32),    # column 2                    tf.constant([], dtype=tf.float32),  # column 3                    tf.constant([], dtype=tf.float32)]  # column 4  col1, col2, col3, col4 = tf.decode_csv(value, record_defaults=record_defaults) features = tf.pack([col1, col2, col3])  tf.session() sess:   coord = tf.train.coordinator()   threads = tf.train.start_queue_runners(coord=coord)    in range(225805):     example, label = sess.run([features, col4])    coord.request_stop()   coord.join(threads) 

and error getting

traceback (most recent call last):   file "dummy.py", line 16, in <module>     features = tf.pack([col1, col2, col3])   file "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/array_ops.py", line 487, in pack     return gen_array_ops._pack(values, axis=axis, name=name)   file "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 1462, in _pack     result = _op_def_lib.apply_op("pack", values=values, axis=axis, name=name)   file "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 437, in apply_op     raise typeerror("%s don't match." % prefix) typeerror: tensors in list passed 'values' of 'pack' op have types [int32, int32, float32] don't match. 

the tf.pack() operator requires of tensors passed have same element type. in program, first 2 tensors have type tf.int32, while third tensor has type tf.float32. simplest solution cast first 2 tensors have type tf.float32, using tf.to_float() operator:

features = tf.pack([tf.to_float(col1), tf.to_float(col2), col3])