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Chapter 2
3. Placeholders: Placeholders are used to feed values into a TensorFlow graph.
They are used along with feed_dict to feed data. They are normally used
to feed new training examples while training a neural network. We assign
values to a placeholder while running the graph in session. They allow us to
create our operations and build the computational graph without requiring
any data. An important detail to note is that placeholders do not contain any
data and thus there is no need to initialize them.
Examples of operations
Let's see some examples of different operations available in TensorFlow 1.x.
Constants
First, let's look at some constants that we will encounter:
• We can declare a scalar constant:
t_1 = tf.constant(4)
Example: A constant vector of shape [1,3]:
t_2 = tf.constant([4, 3, 2])
• To create a tensor with all elements as zero, we use tf.zeros(). This
statement creates a matrix of zeros of shape [M,N] with dtype (int32,
float32, and so on):
tf.zeros([M,N],tf.dtype)
Example: zero_t = tf.zeros([2,3],tf.int32) ==>[[0 0 0], [0 0 0]]
• Get the shape of a tensor:
Example: print(tf.zeros([2,3],tf.int32).shape) ==> (2, 3)
• We can also create tensor variables of the same shape as an existing NumPy
array or tensor constant using:
tf.zeros_like(t_2) # Create a zero matrix of same shape as t_2
tf.ones_like(t_2) # Creates a ones matrix of same shape as t_2
• We can create a tensor with all elements set to one; next, we create a ones
matrix of shape [M,N]:
tf.ones([M,N],tf.dtype)
Example: ones_t = tf.ones([2,3],tf.int32) ==>[[0 0 0], [0 0 0]]
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