def test_to_sparse_zero():
shape = [2, 3]
data_zero = tf.zeros(shape)
sparse_tensor = tx.to_sparse(data_zero)
dense = tf.sparse.to_dense(sparse_tensor)
num_indices = tf.shape(sparse_tensor.indices)[0]
assert num_indices == 0
assert tx.tensor_equal(dense, data_zero)
def test_gather_sparse():
v = tf.constant([[1, 0, 1], [0, 0, 2], [3, 0, 3]], tf.float32)
sp = tx.to_sparse(v)
indices = np.array([[0, 1], [2, 0]], dtype=np.int64)
gather_sp = tx.gather_sparse(sp, indices)
gather = tf.sparse.to_dense(gather_sp)
expected = tf.constant([[1., 0., 1.], [0., 0., 2.], [3., 0., 3.],
[1., 0, 1.]])
assert tx.tensor_equal(gather, expected)
def test_sparse_dense_mul():
x = tf.random.uniform([2, 4])
s = tx.to_sparse(x)
v = tf.random.uniform([4, 3])
# matmul
mul0 = tf.matmul(x, v)
mul1 = tf.sparse.sparse_dense_matmul(s, v)
assert tx.tensor_all_close(mul0, mul1)
# element-wise
mul2 = tx.sparse_dense_multiply(s, x)
mul3 = tf.multiply(x, x)
assert tx.tensor_all_close(mul2, mul3)
def test_to_sparse():
c = [[1, 0], [2, 3]]
sparse_tensor = tx.to_sparse(c)
dense_shape = tf.shape(c, out_type=tf.int64)
indices = tf.where(tf.not_equal(c, 0))
flat_values = tf.reshape(c, [-1])
flat_indices = tf.where(tf.not_equal(flat_values, 0))
flat_indices = tf.squeeze(flat_indices)
flat_indices = tf.math.mod(flat_indices, dense_shape[1])
values = tf.gather_nd(c, indices)
sp_indices = tx.sparse_indices(sparse_tensor)
assert tx.tensor_equal(sparse_tensor.indices, indices)
assert tx.tensor_equal(sp_indices.values, flat_indices)
assert tx.tensor_equal(sparse_tensor.values, values)
import tensorflow as tf
import tensorx as tx
import numpy as np
from tensorflow.python.framework import tensor_util
v = np.array([[1, 0, 1], [0, 0, 2], [3, 0, 3], [7, 0, 0]], dtype=np.float32)
ids = np.array([[1, 0], [1, 3]], dtype=np.int64)
print(" dense representation \n ", v)
sp = tx.to_sparse(v)
tf.InteractiveSession()
print(sp.eval())
flat_ids = tf.reshape(ids, [-1])
num_elems = tf.shape(flat_ids)[0]
print("gather_ids: ", flat_ids.eval())
# COUNT COLUMNS
sp_cols = tx.sparse_ones(sp.indices, sp.dense_shape, dtype=tf.int64)
col_count = tf.sparse_reduce_sum(sp_cols, axis=-1)
col_count_cs = tf.cumsum(col_count)
start_coord = col_count_cs - col_count
print("#cols ", col_count.eval())
print("cumsum #cols ", col_count_cs.eval())
print("start coord ", start_coord.eval())
current_coors = None
import tensorflow as tf
import tensorx as tx
import numpy as np
from tensorflow.python.ops.sparse_ops import sparse_dense_cwise_mul
import time
ss = tf.InteractiveSession()
v = np.random.uniform(-1, 1, [3, 6])
v = tf.constant(v, dtype=tf.float32)
print("dense tensor \n ", v.eval())
# create a dummy sparse tensor
sp_v = tx.to_sparse(v)
print(tf.shape(sp_v).eval())
# suppose se are reshaping from a concatenated tensor that represents
# a sequence to a batch of sequences of feature vectors
# print(tf.shape(sp_v_reshaped).eval())
# suppose now that we want to use broadcasting with the sparse_tensor
weights = tf.constant([[1., 0.5], [1., -1.], [1., -1.]], dtype=tf.float32)
weights_r = tx.repeat(weights, 3)
print(weights_r.eval())
# weights = tf.expand_dims(weights, -1)
out = tx.sparse_multiply(sp_v, weights_r)
dense_out = tf.sparse_tensor_to_dense(out)