Feeding Dataset Iterator to Tensorflow - tensorflow

Question

Can i get a full example somewhere where they feed tf.data.Dataset iterator to a model? I'm trying to feed this data into a model without the help of tf.Estimators.
def preprocess_image(image):
image = tf.image.decode_jpeg(image, channels=1)
image = tf.image.resize_images(image, [224, 224])
image = tf.image.random_flip_left_right(image)
image /= 255.0
image = tf.cast(image, tf.float32)
image = tf.train.shuffle_batch([image],batch_size=16, num_threads=10, capacity=100000, min_after_dequeue=15)
return image
def load_and_preprocess_image(path):
image = tf.read_file(path)
return preprocess_image(image)
train_data_dx = tf.data.Dataset.from_tensor_slices(xray_data_train['full_path'].values)
train_data_dx = train_data_dx.map(load_and_preprocess_image, num_parallel_calls=8)
train_data_dy = xray_data_train['Finding_strings']
print(train_data_dx.output_shapes)
print(train_data_dx.output_types)
test_data_dx = tf.data.Dataset.from_tensor_slices(xray_data_test['full_path'].values)
test_data_dx = test_data_dx.map(load_and_preprocess_image, num_parallel_calls=8)
test_data_dy = xray_data_test['Finding_strings']

Answer 1

Here's a full example.
Note
Iterator must be initialized at the beginning
We can set number of epochs to perform by using repeat() method of number of epochs and batch() method for batch size. Note that I use first repeat() and then batch().
At each iteration we're using tf.Session() interface to access the next batch.
We use try-except since when repetition of data ends it raises tf.error.OutOfRangeError.
import tensorflow as tf
from sklearn.datasets import make_blobs
# generate dummy data for illustration
x_train, y_train = make_blobs(n_samples=25,
n_features=2,
centers=[[1, 1], [-1, -1]],
cluster_std=0.5)
n_epochs = 2
batch_size = 10
with tf.name_scope('inputs'):
x = tf.placeholder(tf.float32, shape=[None, 2])
y = tf.placeholder(tf.int32, shape=[None])
with tf.name_scope('logits'):
logits = tf.layers.dense(x,
units=2,
name='logits')
with tf.name_scope('loss'):
xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)
loss_tensor = tf.reduce_mean(xentropy)
with tf.name_scope('optimizer'):
train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss_tensor)
# create dataset `from_tensor_slices` and create iterator
dataset = tf.data.Dataset.from_tensor_slices({'x':x_train, 'y':y_train})
dataset = dataset.repeat(n_epochs).batch(10)
iterator = dataset.make_initializable_iterator()
with tf.Session() as sess:
sess.run([tf.global_variables_initializer(),
iterator.initializer]) # <-- must be initialized!
next_batch = iterator.get_next()
while True:
try:
batch = sess.run(next_batch) # <-- extract next batch
loss_val, _ = sess.run([loss_tensor, train_op],
feed_dict={x:batch['x'], y:batch['y']})
print(loss_val)
except tf.errors.OutOfRangeError:
break