How to understand static shape and dynamic shape in TensorFlow?

Sometimes the shape of a tensor depends on a value that is computed at runtime. Let's take the following example, where x is defined as a tf.placeholder() vector with four elements:

x = tf.placeholder(tf.int32, shape=[4])
print x.get_shape()
# ==> '(4,)'

The value of x.get_shape() is the static shape of x, and the (4,) means that it is a vector of length 4. Now let's apply the tf.unique() op to x

y, _ = tf.unique(x)
print y.get_shape()
# ==> '(?,)'

The (?,) means that y is a vector of unknown length. Why is it unknown? tf.unique(x) returns the unique values from x, and the values of x are unknown because it is a tf.placeholder(), so it doesn't have a value until you feed it. Let's see what happens if you feed two different values:

sess = tf.Session()
print sess.run(y, feed_dict={x: [0, 1, 2, 3]}).shape
# ==> '(4,)'
print sess.run(y, feed_dict={x: [0, 0, 0, 0]}).shape
# ==> '(1,)'

Hopefully this makes it clear that a tensor can have a different static and dynamic shape. The dynamic shape is always fully defined—it has no ? dimensions—but the static shape can be less specific. This is what allows TensorFlow to support operations like tf.unique() and tf.dynamic_partition(), which can have variable-sized outputs, and are used in advanced applications.

Finally, the tf.shape() op can be used to get the dynamic shape of a tensor and use it in a TensorFlow computation:

z = tf.shape(y)
print sess.run(z, feed_dict={x: [0, 1, 2, 3]})
# ==> [4]
print sess.run(z, feed_dict={x: [0, 0, 0, 0]})
# ==> [1]

Here's a schematic image showing both:

Tensorflow 2.0 Compatible Answer: Mentioning the Code which mrry has specified in his Answer, in Tensorflow Version 2.x (> 2.0), for the benefit of the Community.

# Installing the Tensorflow Version 2.1
!pip install tensorflow==2.1

# If we don't Disable the Eager Execution, usage of Placeholder results in RunTimeError

tf.compat.v1.disable_eager_execution()

x = tf.compat.v1.placeholder(tf.int32, shape=[4])
print(x.get_shape())

# ==> 4

y, _ = tf.unique(x)
print(y.get_shape())

# ==> (None,)

sess = tf.compat.v1.Session()
print(sess.run(y, feed_dict={x: [0, 1, 2, 3]}).shape)
# ==> '(4,)'
print(sess.run(y, feed_dict={x: [0, 0, 0, 0]}).shape)
# ==> '(1,)'

z = tf.shape(y)
print(sess.run(z, feed_dict={x: [0, 1, 2, 3]}))
# ==> [4]
print(sess.run(z, feed_dict={x: [0, 0, 0, 0]}))
# ==> [1]