CODEWITHSUNDEEP
pythontensorflowreshape
Robin
Robin

Reputation: 1599

How does tf.reshape() work internally ?

I'm trying to understand how tf.reshape works. Let's have an example:

embeddings = tf.placeholder(tf.float32, shape=[N0,N1])
M_2D = tf.placeholder(tf.float32, shape=[N0,None])
M_3D = tf.reshape(M_2D, [-1,N0,1])
weighted_embeddings = tf.multiply(embeddings, M_3D)

Here I have a 2D tensor M_2D whose columns represent coefficients for the N0 embeddings of dimension N1. I want to create a 3D tensor where each column of M_2D is placed in the first dimension of M_3D, and columns are keep in the same order. My final goal is to create a 3D tensor of 2D embeddings, each weighted by the columns of M_2D.

How can I be sure that reshape actually place each column in the new dimension of M_3D. Is it possible that it places the rows instead ? Is there somewhere in tensorflow documentation a clear explanation on the internal working process of tf.reshape, particularly when -1 is provided ?

Upvotes: 1

Views: 3734

Answers (1)

Kaihong Zhang
Kaihong Zhang

Reputation: 419

Tensor before and after tf.reshape have the same flatten order.

In tensorflow runtime, a Tensor is consists of raw data(byte array), shape, and dtype, tf.reshape only change shape, with raw data and dtype not changed. -1 or None in tf.reshape means that this value can be calculated.

For example,

# a tensor with 6 elements, with shape [3,2]
a = tf.constant([[1,2], [3,4], [5,6]]) 
# reshape tensor to [2, 3, 1], 2 is calculated by 6/3/1
b = tf.reshape(a, [-1, 3, 1])

In this example, a and b have the same flatten order, namely [1,2,3,4,5,6], a has the shape [3,2], its value is [[1,2], [3,4], [5,6]], b has the shape [2,3,1], its value is [[[1],[2],[3]],[[4],[5],[6]]].

Upvotes: 5

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