def test_onehot():
x = tf.constant(np.array([[1], [3]], dtype='float32'))
onehot_t = modeling.onehot(x, 10)
init()
onehot = sess.run(onehot_t)
assert onehot.shape == (2, 10)
assert onehot[0][1] == 1
assert onehot[1][3] == 1
assert np.sum(onehot[0]) == 1
assert np.sum(onehot) == 2
def cost(y, logits, regularize=False, l2_weight=0.01):
batch_size = logits.get_shape().as_list()[0]
with tf.name_scope('cost'):
with tf.name_scope('xentropy'):
xentropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
logits, tf.cast(modeling.onehot(y, NUM_CLASSES), 'float')))
if regularize:
l2 = modeling.l2_penalty(l2_weight)
tot_cost = xentropy
if regularize:
tot_cost = tf.add(xentropy, l2)
tf.scalar_summary('cost', tot_cost)
return tot_cost