TF Ops (Part 2)

Common TF ops

Here, we discuss some important TF ops applied to tensors. These include the following:

• tf.transpose(): This is for taking the transpose of a tensor.

• tf.matmul(): This is for matrix (tensor) multiplication.

• tf.reshape(): This is to change the shape of the tensor.

• tf.reduce_mean(): This is for reducing a tensor using its mean (equivalent to np.mean() in NumPy).

• tf.reduce_sum(): This is for reducing a tensor using its sum (equivalent to np.sum() in NumPy).

• tf.reduce_max(): This is for reducing a tensor using its maximum value (equivalent to np.max() in NumPy).

• tf.squeeze(): This is to remove dimensions of size 1 from a tensor.

• tf.slice(): This is to extract a slice of the specified size from an input tensor.

• tf.tile(): This is for tiling a tensor a number of times along each axis as specified by some multiple.