craymichael - 5 months ago 109

Python Question

**EDIT**: I'm using TensorFlow version 0.10.0rc0

I'm currently trying to use

`tf.contrib.learn.read_batch_examples`

`LinearClassifier`

`read_batch_examples`

`tf.decode_csv`

`parse_fn`

`input_fn`

`ValueError: Tensor("centered_bias_weight:0", shape=(1,), dtype=float32_ref) must be from the same graph as Tensor("linear/linear/BiasAdd:0", shape=(?, 1), dtype=float32).`

I'm confused because neither of those Tensors appear to be from the

`read_batch_examples`

`Estimator.fit`

`Estimator.partial_fit`

`def input_fn(examples_dict):`

continuous_cols = {k: tf.cast(examples_dict[k], dtype=tf.float32)

for k in CONTINUOUS_FEATURES}

categorical_cols = {

k: tf.SparseTensor(

indices=[[i, 0] for i in xrange(examples_dict[k].get_shape()[0])],

values=examples_dict[k],

shape=[int(examples_dict[k].get_shape()[0]), 1])

for k in CATEGORICAL_FEATURES}

feature_cols = dict(continuous_cols)

feature_cols.update(categorical_cols)

label = tf.contrib.layers.one_hot_encoding(labels=examples_dict[LABEL],

num_classes=2,

on_value=1,

off_value=0)

return feature_cols, label

filenames = [...]

csv_headers = [...] # features and label headers

batch_size = 50

min_after_dequeue = int(num_examples * min_fraction_of_examples_in_queue)

queue_capacity = min_after_dequeue + 3 * batch_size

examples = tf.contrib.learn.read_batch_examples(

filenames,

batch_size=batch_size,

reader=tf.TextLineReader,

randomize_input=True,

queue_capacity=queue_capacity,

num_threads=1,

read_batch_size=1,

parse_fn=lambda x: tf.decode_csv(x, [tf.constant([''], dtype=tf.string) for _ in xrange(csv_headers)]))

examples_dict = {}

for i, header in enumerate(csv_headers):

examples_dict[header] = examples[:, i]

categorical_cols = []

for header in CATEGORICAL_FEATURES:

categorical_cols.append(tf.contrib.layers.sparse_column_with_keys(

header,

keys # Keys for that particular feature, source not shown here

))

continuous_cols = []

for header in CONTINUOUS_FEATURES:

continuous_cols.append(tf.contrib.layers.real_valued_column(header))

feature_columns = categorical_cols + continuous_cols

model = tf.contrib.learn.LinearClassifier(

model_dir=model_dir,

feature_columns=feature_columns,

optimizer=optimizer,

n_classes=num_classes)

# Above code is ok up to this point

model.fit(input_fn=lambda: input_fn(examples_dict),

steps=200) # This line causes the error ****

Any alternatives for batching would be appreciated as well!

Answer

I was able to figure out my mistake through the help of the great TensorFlow team! `read_batch_examples`

has to be called within `input_fn`

, otherwise the op has to be run beforehand as it'll be from a different graph. If someone else has this problem and I wasn't clear enough, just leave a comment.