adj_mx = tf.SparseTensor(indices=indices, values=values, dense_shape=dense_shape)

adj_mx_serialized = tf.io.serialize_sparse(adj_mx).numpy().tobytes()

I did this and saved as blob to sql.

I know what I did is probably a mistake, but I didn’t notice it at the time. And I really would love to be able to de-serialize it without needing to serialize it again, correctly this time. I have done this for half a million matrices, so I want to know if it is remotely possible to reverse this serialization.

According to TensorFlow, the serialize_sparse function returns:

“A 3-vector (1-D Tensor), with each column representing the serialized SparseTensor’s indices, values, and shape (respectively).”

    #...
    sparse_adj = nx.adjacency_matrix(G, weight='weight')
    coo = sparse_adj.tocoo()
    indices = np.mat([coo.row, coo.col]).transpose()
    values = coo.data
    dense_shape = (aa_limit, aa_limit)
    adj_mx = tf.SparseTensor(indices=indices, values=values, dense_shape=dense_shape)

This was the construction.
I know how many indices we have (i mean i know len(coo.row) which is equal to len(coo.col)). The values are floats.

Do I have any chance?