I would like to center my set of rows with several means and get several sets of centered rows.
My data has shape of (4, 3) i.e. four 3D vectors:
data = tf.get_variable("myvar1", shape=[4, 3], dtype=tf.float64)
I have two centers (two 3D vectors):
mu = tf.get_variable("mu", initializer=tf.constant(np.arange(2*3).reshape(2, 3), dtype=tf.float64))
I would like to center data once per each mu. In numpy I would write loop:
data = np.arange(4 * 3).reshape(4, 3)
mu = np.arange(2*3).reshape(2, 3)
centered_data = np.empty((2, 4, 3))
for i_data in range(len(data)):
for i_mu in range(len(mu)):
centered = data[i_data] - mu[i_mu]
centered_data[i_mu, i_data, :] = centered
How to do the same in tensorflow?
Bulk method for numpy would also be appreciated!
Apparently I can insert singular dimension to provoke broadcasting:
data = tf.get_variable("myvar1", shape=[4, 3], dtype=tf.float64)
mu = tf.get_variable("mu", initializer=tf.constant(np.arange(2*3).reshape(2, 3), dtype=tf.float64))
centered_data = data - tf.expand_dims(mu, axis=1)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
ans_value, centered_data_value, mu_value = sess.run([centered_data, data, mu], {data: np.arange(4 * 3).reshape(4, 3)})
print("centered_data_value: ", centered_data_value)
print("mu: ", mu_value)
print("ans: ", ans_value)
The same is in numpy:
mu = np.reshape(mu, (2, 1, 3))
centered_data = data - mu
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