tf.placeholder()
Inserts a placeholder for a tensor that will be always fed.
A placeholder is simply a variable that we will assign data to
at a later date. It allows us to create our operations and build
our computation graph, without needing the data. In TensorFlow
terminology, we then feed data into the graph through these
placeholders.
tf.compat.v1.placeholder(
dtype, shape=None, name=None
)
Important: This tensor will produce an error if evaluated.
Its value must be fed using the feed_dict optional argument
to Session.run(), Tensor.eval(), or Operation.run().
Example:
x = tf.compat.v1.placeholder(tf.float32, shape=(1024, 1024))
y = tf.matmul(x, x)
with tf.compat.v1.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.