Tensorflow provides Iterators to do that.
The iterator is not aware of the number of elements present in the Dataset.
It has a get_next function that is used to create an operation in the tensorflow graph and when run over a session, it will return the values from the iterator.
Once the Dataset is exhausted, it throws an tf.
errors.
OutOfRangeError exception.
Let’s look at various Iterators that TensorFlow provides.
One-shot iterator: This is the most basic form of iterator.
It requires no explicit initialization and iterates over the data only one time and once it gets exhausted, it cannot be re-initialized.
data = np.
arange(10,15)#create the datasetdataset = tf.
data.
Dataset.
from_tensor_slices(data)#create the iteratoriterator = dataset.
make_one_shot_iterator()next_element = iterator.
get_next()with tf.
Session() as sess: val = sess.
run(next_element) print(val)Initializable iterator: This iterator requires you to explicitly initialize the iterator by running iterator.
initialize.
You can define a tf.
placeholder and pass data to it dynamically each time you call the initialize operation.
# define two placeholders to accept min and max valuemin_val = tf.
placeholder(tf.
int32, shape=[])max_val = tf.
placeholder(tf.
int32, shape=[])data = tf.
range(min_val, max_val)dataset = tf.
data.
Dataset.
from_tensor_slices(data)iterator = dataset.
make_initializable_iterator()next_ele = iterator.
get_next()with tf.
Session() as sess: # initialize an iterator with range of values from 10 to 15 sess.
run(iterator.
initializer, feed_dict={min_val:10, max_val:15}) try: while True: val = sess.
run(next_ele) print(val) except tf.
errors.
OutOfRangeError: pass # initialize an iterator with range of values from 1 to 10 sess.
run(iterator.
initializer, feed_dict={min_val:1, max_val:10}) try: while True: val = sess.
run(next_ele) print(val) except tf.
errors.
OutOfRangeError: passReinitializable iterator: This iterator can be initialized from different Dataset objects that have the same structure.
Each dataset can pass through its own transformation pipeline.
def map_fnc(ele): return ele*2min_val = tf.
placeholder(tf.
int32, shape=[])max_val = tf.
placeholder(tf.
int32, shape=[])data = tf.
range(min_val, max_val)#Define separate datasets for training and validationtrain_dataset = tf.
data.
Dataset.
from_tensor_slices(data)val_dataset = tf.
data.
Dataset.
from_tensor_slices(data).
map(map_fnc)#create an iterator iterator=tf.
data.
Iterator.
from_structure(train_dataset.
output_types ,train_dataset.
output_shapes)train_initializer = iterator.
make_initializer(train_dataset)val_initializer = iterator.
make_initializer(val_dataset)next_ele = iterator.
get_next()with tf.
Session() as sess: # initialize an iterator with range of values from 10 to 15 sess.
run(train_initializer, feed_dict={min_val:10, max_val:15}) try: while True: val = sess.
run(next_ele) print(val) except tf.
errors.
OutOfRangeError: pass # initialize an iterator with range of values from 1 to 10 sess.
run(val_initializer, feed_dict={min_val:1, max_val:10}) try: while True: val = sess.
run(next_ele) print(val) except tf.
errors.
OutOfRangeError: passFeedable iterator: Can be used to switch between Iterators for different Datasets.
Useful when you have different Datasets and you want to have more control over which iterator to use over the Dataset.
def map_fnc(ele): return ele*2min_val = tf.
placeholder(tf.
int32, shape=[])max_val = tf.
placeholder(tf.
int32, shape=[])data = tf.
range(min_val, max_val)train_dataset = tf.
data.
Dataset.
from_tensor_slices(data)val_dataset = tf.
data.
Dataset.
from_tensor_slices(data).
map(map_fnc)train_val_iterator = tf.
data.
Iterator.
from_structure(train_dataset.
output_types , train_dataset.
output_shapes)train_initializer = train_val_iterator.
make_initializer(train_dataset)val_initializer = train_val_iterator.
make_initializer(val_dataset)test_dataset = tf.
data.
Dataset.
from_tensor_slices(tf.
range(10,15))test_iterator = test_dataset.
make_one_shot_iterator()handle = tf.
placeholder(tf.
string, shape=[])iterator = tf.
data.
Iterator.
from_string_handle(handle, train_dataset.
output_types, train_dataset.
output_shapes)next_ele = iterator.
get_next()with tf.
Session() as sess: train_val_handle = sess.
run(train_val_iterator.
string_handle()) test_handle = sess.
run(test_iterator.
string_handle()) # training sess.
run(train_initializer, feed_dict={min_val:10, max_val:15}) try: while True: val = sess.
run(next_ele, feed_dict={handle:train_val_handle}) print(val) except tf.
errors.
OutOfRangeError: pass #validation sess.
run(val_initializer, feed_dict={min_val:1, max_val:10}) try: while True: val = sess.
run(next_ele, feed_dict={handle:train_val_handle}) print(val) except tf.
errors.
OutOfRangeError: pass #testing try: while True: val = sess.
run(next_ele, feed_dict={handle:test_handle}) print(val) except tf.
errors.
OutOfRangeError: passWe have learned about various iterators.
Let’s apply the knowledge to a practical dataset.
We will train the famous MNIST dataset using the LeNet-5 Model.
This tutorial will not dive into the details of implementing the LeNet-5 Model as it is beyond the scope of this article.
LeNet-5 ModelLet’s import the MNIST data from the tensorflow library.
The MNIST database contains 60,000 training images and 10,000 testing images.
Each image is of size 28*28*1.
We need to resize it to 32*32*1 for the LeNet-5 Model.
from tensorflow.
examples.
tutorials.
mnist import input_datamnist = input_data.
read_data_sets("MNIST_data/", reshape=False, one_hot = True)X_train, y_train = mnist.
train.
images, mnist.
train.
labelsX_val, y_val = mnist.
validation.
images, mnist.
validation.
labelsX_test, y_test = mnist.
test.
images, mnist.
test.
labelsX_train = np.
pad(X_train, ((0,0), (2,2), (2,2), (0,0)), 'constant')X_val = np.
pad(X_val, ((0,0), (2,2), (2,2), (0,0)), 'constant')X_test = np.
pad(X_test, ((0,0), (2,2), (2,2), (0,0)), 'constant')Ref: https://www.
researchgate.
net/figure/Structure-of-LeNet-5_fig1_312170477Let’s define the forward propagation of the model.
def forward_pass(X): W1 = tf.
get_variable("W1", [5,5,1,6], initializer = tf.
contrib.
layers.
xavier_initializer(seed=0)) # for conv layer2 W2 = tf.
get_variable("W2", [5,5,6,16], initializer = tf.
contrib.
layers.
xavier_initializer(seed=0)) Z1 = tf.
nn.
conv2d(X, W1, strides = [1,1,1,1], padding='VALID') A1 = tf.
nn.
relu(Z1) P1 = tf.
nn.
max_pool(A1, ksize = [1,2,2,1], strides = [1,2,2,1], padding='VALID') Z2 = tf.
nn.
conv2d(P1, W2, strides = [1,1,1,1], padding='VALID') A2= tf.
nn.
relu(Z2) P2= tf.
nn.
max_pool(A2, ksize = [1,2,2,1], strides=[1,2,2,1], padding='VALID') P2 = tf.
contrib.
layers.
flatten(P2) Z3 = tf.
contrib.
layers.
fully_connected(P2, 120) Z4 = tf.
contrib.
layers.
fully_connected(Z3, 84) Z5 = tf.
contrib.
layers.
fully_connected(Z4,10, activation_fn= None) return Z5Let’s define the model operationsdef model(X,Y): logits = forward_pass(X) cost = tf.
reduce_mean( tf.
nn.
softmax_cross_entropy_with_logits_v2(logits=logits, labels=Y)) optimizer = tf.
train.
AdamOptimizer(learning_rate=0.
0009) learner = optimizer.
minimize(cost) correct_predictions = tf.
equal(tf.
argmax(logits,1), tf.
argmax(Y,1)) accuracy = tf.
reduce_mean(tf.
cast(correct_predictions, tf.
float32)) return (learner, accuracy)We have now created the model.
Before deciding on the Iterator to use for our model, let’s see what are the typical requirements of a machine learning model.
Training the data over batches: Dataset can be very huge.
To prevent out of memory errors, we would need to train our dataset in small batches.
Train the model over n passes of the dataset: Typically you want to run your training model over multiple passes of the dataset.
Validate the model at each epoch: You would need to validate your model at each epoch to check your model’s performance.
Finally, test your model on unseen data: After the model is trained, you would like to test your model on unseen data.
Let’s see the pros and cons of each iterator.
One-shot iterator: The Dataset can’t be reinitialized once exhausted.
To train for more epochs, you would need to repeat the Dataset before feeding to the iterator.
This will require huge memory if the size of the data is large.
It also doesn’t provide any option to validate the model.
epochs = 10 batch_size = 64 iterations = len(y_train) * epochstf.
reset_default_graph()dataset = tf.
data.
Dataset.
from_tensor_slices((X_train, y_train))# need to repeat the dataset for epoch number of times, as all the data needs# to be fed to the dataset at oncedataset = dataset.
repeat(epochs).
batch(batch_size)iterator = dataset.
make_one_shot_iterator()X_batch , Y_batch = iterator.
get_next()(learner, accuracy) = model(X_batch, Y_batch)with tf.
Session() as sess: sess.
run(tf.
global_variables_initializer()) total_accuracy = 0 try: while True: temp_accuracy, _ = sess.
run([accuracy, learner]) total_accuracy += temp_accuracy except tf.
errors.
OutOfRangeError: pass print('Avg training accuracy is {}'.
format((total_accuracy * batch_size) / iterations ))Initializable iterator: You can dynamically change the Dataset between training and validation Datasets.
However, in this case both the Datasets needs to go through the same transformation pipeline.
epochs = 10 batch_size = 64tf.
reset_default_graph()X_data = tf.
placeholder(tf.
float32, [None, 32,32,1])Y_data = tf.
placeholder(tf.
float32, [None, 10])dataset = tf.
data.
Dataset.
from_tensor_slices((X_data, Y_data))dataset = dataset.
batch(batch_size)iterator = dataset.
make_initializable_iterator()X_batch , Y_batch = iterator.
get_next()(learner, accuracy) = model(X_batch, Y_batch)with tf.
Session() as sess: sess.
run(tf.
global_variables_initializer()) for epoch in range(epochs): # train the model sess.
run(iterator.
initializer, feed_dict={X_data:X_train, Y_data:y_train}) total_train_accuracy = 0 no_train_examples = len(y_train) try: while True: temp_train_accuracy, _ = sess.
run([accuracy, learner]) total_train_accuracy += temp_train_accuracy*batch_size except tf.
errors.
OutOfRangeError: pass # validate the model sess.
run(iterator.
initializer, feed_dict={X_data:X_val, Y_data:y_val}) total_val_accuracy = 0 no_val_examples = len(y_val) try: while True: temp_val_accuracy = sess.
run(accuracy) total_val_accuracy += temp_val_accuracy*batch_size except tf.
errors.
OutOfRangeError: pass print('Epoch {}'.
format(str(epoch+1))) print("—————————") print('Training accuracy is {}'.
format(total_train_accuracy/no_train_examples)) print('Validation accuracy is {}'.
format(total_val_accuracy/no_val_examples))Re-initializable iterator: This iterator overcomes the problem of initializable iterator by using two separate Datasets.
Each dataset can go through its own preprocessing pipeline.
The iterator can be created using the tf.
Iterator.
from_structure method.
def map_fnc(X, Y): return X, Yepochs = 10 batch_size = 64tf.
reset_default_graph()X_data = tf.
placeholder(tf.
float32, [None, 32,32,1])Y_data = tf.
placeholder(tf.
float32, [None, 10])train_dataset = tf.
data.
Dataset.
from_tensor_slices((X_data, Y_data)).
batch(batch_size).
map(map_fnc)val_dataset = tf.
data.
Dataset.
from_tensor_slices((X_data, Y_data)).
batch(batch_size)iterator = tf.
data.
Iterator.
from_structure(train_dataset.
output_types, train_dataset.
output_shapes)X_batch , Y_batch = iterator.
get_next()(learner, accuracy) = model(X_batch, Y_batch)train_initializer = iterator.
make_initializer(train_dataset)val_initializer = iterator.
make_initializer(val_dataset)with tf.
Session() as sess: sess.
run(tf.
global_variables_initializer()) for epoch in range(epochs): # train the model sess.
run(train_initializer, feed_dict={X_data:X_train, Y_data:y_train}) total_train_accuracy = 0 no_train_examples = len(y_train) try: while True: temp_train_accuracy, _ = sess.
run([accuracy, learner]) total_train_accuracy += temp_train_accuracy*batch_size except tf.
errors.
OutOfRangeError: pass # validate the model sess.
run(val_initializer, feed_dict={X_data:X_val, Y_data:y_val}) total_val_accuracy = 0 no_val_examples = len(y_val) try: while True: temp_val_accuracy = sess.
run(accuracy) total_val_accuracy += temp_val_accuracy*batch_size except tf.
errors.
OutOfRangeError: pass print('Epoch {}'.
format(str(epoch+1))) print("—————————") print('Training accuracy is {}'.
format(total_train_accuracy/no_train_examples)) print('Validation accuracy is {}'.
format(total_val_accuracy/no_val_examples))Feedable iterator: This iterator provides the option of switching between various iterators.
You can create a re-initializable iterator for training and validation purposes.
For inference/testing where you require one pass of the dataset, you can use the one shot iterator.
epochs = 10 batch_size = 64tf.
reset_default_graph()X_data = tf.
placeholder(tf.
float32, [None, 32,32,1])Y_data = tf.
placeholder(tf.
float32, [None, 10])train_dataset = tf.
data.
Dataset.
from_tensor_slices((X_data, Y_data)).
batch(batch_size)val_dataset = tf.
data.
Dataset.
from_tensor_slices((X_data, Y_data)).
batch(batch_size)test_dataset = tf.
data.
Dataset.
from_tensor_slices((X_test, y_test.
astype(np.
float32)).
batch(batch_size)handle = tf.
placeholder(tf.
string, shape=[])iterator = tf.
data.
Iterator.
from_string_handle(handle, train_dataset.
output_types, train_dataset.
output_shapes)X_batch , Y_batch = iterator.
get_next()(learner, accuracy) = model(X_batch, Y_batch)train_val_iterator = tf.
data.
Iterator.
from_structure(train_dataset.
output_types, train_dataset.
output_shapes)train_iterator = train_val_iterator.
make_initializer(train_dataset)val_iterator = train_val_iterator.
make_initializer(val_dataset)test_iterator = test_dataset.
make_one_shot_iterator()with tf.
Session() as sess: sess.
run(tf.
global_variables_initializer()) train_val_string_handle = sess.
run(train_val_iterator.
string_handle()) test_string_handle = sess.
run(test_iterator.
string_handle()) for epoch in range(epochs): # train the model sess.
run(train_iterator, feed_dict={X_data:X_train, Y_data:y_train}) total_train_accuracy = 0 no_train_examples = len(y_train) try: while True: temp_train_accuracy, _ = sess.
run([accuracy, learner], feed_dict={handle:train_val_string_handle}) total_train_accuracy += temp_train_accuracy*batch_size except tf.
errors.
OutOfRangeError: pass # validate the model sess.
run(val_iterator, feed_dict={X_data:X_val, Y_data:y_val}) total_val_accuracy = 0 no_val_examples = len(y_val) try: while True: temp_val_accuracy, _ = sess.
run([accuracy, learner], feed_dict={handle:train_val_string_handle}) total_val_accuracy += temp_val_accuracy*batch_size except tf.
errors.
OutOfRangeError: pass print('Epoch {}'.
format(str(epoch+1))) print("—————————") print('Training accuracy is {}'.
format(total_train_accuracy/no_train_examples)) print('Validation accuracy is {}'.
format(total_val_accuracy/no_val_examples)) print("Testing the model ——–") total_test_accuracy = 0 no_test_examples = len(y_test) try: while True: temp_test_accuracy, _ = sess.
run([accuracy, learner], feed_dict={handle:test_string_handle}) total_test_accuracy += temp_test_accuracy*batch_size except tf.
errors.
OutOfRangeError: pass print('Testing accuracy is {}'.
format(total_test_accuracy/no_test_examples))Thanks for reading the blog.
The code examples used in this blog can be found in this jupyter notebook.
Do leave your comments below if you have any questions or if you have any suggestions for improving this blog.
Referenceshttps://www.
tensorflow.
org/api_docs/python/tf/data/Iterator#from_string_handlehttps://www.
tensorflow.
org/guide/datasetshttps://docs.
google.
com/presentation/d/16kHNtQslt-yuJ3w8GIx-eEH6t_AvFeQOchqGRFpAD7U/edit#slide=id.
g254d08e080_0_141https://github.
com/tensorflow/tensorflow/issues/2919.