Reading data in tensorflow - TypeError ("% s that don't all match".% Prefix)

Question

I am trying to load the following data file (with 225805 lines) in a tensor stream. The data file is as follows:

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 

code reading data

 import tensorflow as 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]) with tf.Session() as sess: coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(coord=coord) for i in range(225805): example, label = sess.run([features, col4]) coord.request_stop() coord.join(threads) 

and this is the error i get

 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 that don't all match." % prefix) TypeError: Tensors in list passed to 'values' of 'Pack' Op have types [int32, int32, float32] that don't all match. 

Answer 1

The tf.pack() operator requires that all the tensors passed to it have the same element type. In your program, the first two tensors are of type tf.int32 , and the third tensor is of type tf.float32 . The simplest solution is to force the first two tensors to be of type tf.float32 using the tf.to_float() operator:

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