There are three main methods of getting data into a TensorFlow PRogram:

Feeding

TensorFlow's feed mechanism lets you inject data into any Tensor in acomputation graph. A python computation can thus feed data directly into thegraph.

Supply feed data through the feed_dict argument to a run() or eval() callthat initiates computation.

with tf.session():  input = tf.placeholder(tf.float32)  classifier = ...  print(classifier.eval(feed_dict={input: my_python_preprocessing_fn()}))
While you can replace any Tensor with feed data, including variables andconstants, the best practice is to use aplaceholder op node. Aplaceholder exists solely to serve as the target of feeds. It is notinitialized and contains no data. A placeholder generates an error ifit is executed without a feed, so you won't forget to feed it.
An example using placeholder and feeding to train on MNIST data can be foundintensorflow/examples/tutorials/mnist/fully_connected_feed.py,and is described in the MNIST tutorial.
x = tf.placeholder(tf.float32, shape=(1024, 1024))y = tf.matmul(x, x)with tf.Session() as sess:  print(sess.run(y))  # ERROR: will fail because x was not fed.  rand_array = np.random.rand(1024, 1024)  print(sess.run(y, feed_dict={x: rand_array}))  # Will succeed.
tf.placeholder(dtype, shape=None, name=None)
Args:
dtype: The type of elements in the tensor to be fed.shape: The shape of the tensor to be fed (optional). If the shape is not    specified, you can feed a tensor of any shape.name: A name for the Operation (optional).
Returns:
A Tensor that may be used as a handle for feeding a value, but not  evaluated directly.
                 for step in xrange(num_batch):                    batch_imgs, batch_labels = self.nextBatch(train_imgs, train_labels, step, BATCH_SIZE)                    # Fit training using batch data                    # print("IMG_PL = ", self.img_pl.get_shape())                    _, single_cost = sess.run([optimizer, cost], feed_dict={self.img_pl: batch_imgs, self.label_pl: batch_labels, self.keep_prob: dropout})