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batching huge data in tensorflow

I am trying to perform binary classification using the code/tutorial from
https://github.com/eisenjulian/nlp_estimator_tutorial/blob/master/nlp_estimators.py

print("Loading data...")
(x_train_variable, y_train), (x_test_variable, y_test) = imdb.load_data(num_words=vocab_size)
print(len(y_train), "train sequences")
print(len(y_test), "test sequences")

print("Pad sequences (samples x time)")
x_train = sequence.pad_sequences(x_train_variable,
maxlen=sentence_size,
padding='post',
value=0)
x_test = sequence.pad_sequences(x_test_variable,
maxlen=sentence_size,
padding='post',
value=0)
print("x_train shape:", x_train.shape)
print("x_test shape:", x_test.shape)

def train_input_fn():
dataset = tf.data.Dataset.from_tensor_slices((x_train, x_len_train, y_train))
dataset = dataset.shuffle(buffer_size=len(x_train_variable))
dataset = dataset.batch(100)
dataset = dataset.map(parser)
dataset = dataset.repeat()
iterator = dataset.make_one_shot_iterator()
return iterator.get_next()

def eval_input_fn():
dataset = tf.data.Dataset.from_tensor_slices((x_test, x_len_test, y_test))
dataset = dataset.batch(100)
dataset = dataset.map(parser)
iterator = dataset.make_one_shot_iterator()
return iterator.get_next()



def cnn_model_fn(features, labels, mode, params):
input_layer = tf.contrib.layers.embed_sequence(
features['x'], vocab_size, embedding_size,
initializer=params['embedding_initializer'])

training = mode == tf.estimator.ModeKeys.TRAIN
dropout_emb = tf.layers.dropout(inputs=input_layer,
rate=0.2,
training=training)

conv = tf.layers.conv1d(
inputs=dropout_emb,
filters=32,
kernel_size=3,
padding="same",
activation=tf.nn.relu)

# Global Max Pooling
pool = tf.reduce_max(input_tensor=conv, axis=1)

hidden = tf.layers.dense(inputs=pool, units=250, activation=tf.nn.relu)

dropout_hidden = tf.layers.dropout(inputs=hidden,
rate=0.2,
training=training)

logits = tf.layers.dense(inputs=dropout_hidden, units=1)

# This will be None when predicting
if labels is not None:
labels = tf.reshape(labels, [-1, 1])


optimizer = tf.train.AdamOptimizer()

def _train_op_fn(loss):
return optimizer.minimize(
loss=loss,
global_step=tf.train.get_global_step())

return head.create_estimator_spec(
features=features,
labels=labels,
mode=mode,
logits=logits,
train_op_fn=_train_op_fn)

cnn_classifier = tf.estimator.Estimator(model_fn=cnn_model_fn,
model_dir=os.path.join(model_dir, 'cnn'),
params=params)
train_and_evaluate(cnn_classifier)


The example here loads data from IMDB movie reviews. I have my own dataset in the form of text which is approx 2GB huge. Now in this example the line
(x_train_variable, y_train), (x_test_variable, y_test) = imdb.load_data(num_words=vocab_size) tries to load whole dataset in memory. If I try to do the same I run out of memory. How can I restructure this logic to read data in batches from my disk?

(x_train_variable, y_train), (x_test_variable, y_test) = imdb.load_data(num_words=vocab_size)

1 Answer
1

You want to change the dataset = tf.data.Dataset.from_tensor_slices((x_train, x_len_train, y_train)) line. There are lots of ways of creating a dataset - from_tensor_slices is the easiest, but won't work on its own if you can't load the entire dataset to memory.

dataset = tf.data.Dataset.from_tensor_slices((x_train, x_len_train, y_train))

from_tensor_slices

The best way depends on how you have the data stored, or how you want to store it/manipulate it. The simplest in my opinion with very little down-side (unless running on multiple GPUs) is to have the original dataset just give indices to data, and write a normal numpy function for loading the ith example.

i

dataset = tf.data.Dataset.from_tensor_slices(tf.range(epoch_size))

def tf_map_fn(i):
def np_map_fn(i):
return load_ith_example(i)

inp1, inp2 = tf.py_func(np_map_fn, (i,), Tout=(tf.float32, tf.float32), stateful=False)
# other preprocessing/data augmentation goes here.

# unbatched sizes
inp1.set_shape(shape1)
inp2.set_shape(shape2)
return inp1, inp2

dataset = dataset.repeat().shuffle(epoch_size).map(tf_map_fn, 8)

dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(1) # start loading data as GPU trains on previous batch

inp1, inp2 = dataset.make_one_shot_iterator().get_next()


Here I assume your outputs are float32 tensors (Tout=...). set_shape calls aren't strictly necessary, but if you know the shape it'll do better error checks.

float32

Tout=...

set_shape

So long as your preprocessing doesn't take longer than your network to run, this should run just as fast as any other method on a single GPU machine.

The other obvious way is to convert your data to tfrecords, but that'll take up more space on disk and is more of a pain to manage if you ask me.

tfrecords

load_ith_example

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