def test_sum_node_create_and_eval_keras():
n_trials = 100
for i in range(n_trials):
n_children = numpy.random.randint(1, 100)
print('n children', n_children)
children = [Node() for c in range(n_children)]
weights = numpy.random.rand(n_children)
weights = weights / weights.sum()
#
# create sum node and adding children to it
sum_node = SumNode()
for child, w in zip(children, weights):
sum_node.add_child(child, w)
child.log_vals = K.placeholder(ndim=2)
assert len(sum_node.children) == n_children
assert len(sum_node.weights) == n_children
assert len(sum_node.log_weights) == n_children
print(sum_node)
#
# evaluating for fake probabilities
n_instances = numpy.random.randint(1, 100)
print('n instances', n_instances)
probs = numpy.random.rand(n_instances, n_children) # .astype(theano.config.floatX)
log_probs = numpy.log(probs)
log_vals = []
for d in range(n_instances):
for c, child in enumerate(children):
child.set_val(probs[d, c])
sum_node.eval()
print('sum node eval')
print(sum_node.log_val)
log_vals.append(sum_node.log_val)
#
# now theano
sum_node.build_k()
eval_sum_node_f = K.function(inputs=[c.log_vals for c in children],
outputs=[sum_node.log_vals])
keras_log_vals = eval_sum_node_f([log_probs[:, c].reshape(log_probs.shape[0], 1)
for c in range(n_children)])[0]
print(keras_log_vals)
assert_array_almost_equal(numpy.array(log_vals).reshape(log_probs.shape[0], 1),
keras_log_vals,
decimal=4)
def test_sum_node_create_and_eval():
# create child nodes
child1 = Node()
val1 = 1.
child1.set_val(val1)
child2 = Node()
val2 = 1.
child2.set_val(val2)
# create sum node and adding children to it
sum_node = SumNode()
weight1 = 0.8
weight2 = 0.2
sum_node.add_child(child1, weight1)
sum_node.add_child(child2, weight2)
assert len(sum_node.children) == 2
assert len(sum_node.weights) == 2
assert len(sum_node.log_weights) == 2
log_weights = [log(weight1), log(weight2)]
assert log_weights == sum_node.log_weights
print(sum_node)
# evaluating
sum_node.eval()
print(sum_node.log_val)
assert_almost_equal(sum_node.log_val,
log(val1 * weight1 + val2 * weight2),
places=15)
# changing values 1,0
val1 = 1.
child1.set_val(val1)
val2 = 0.
child2.set_val(val2)
# evaluating
sum_node.eval()
print(sum_node.log_val)
assert_almost_equal(sum_node.log_val,
log(val1 * weight1 + val2 * weight2),
places=15)
# changing values 0,0 -> LOG_ZERO
val1 = 0.
child1.set_val(val1)
val2 = 0.
child2.set_val(val2)
# evaluating
sum_node.eval()
print(sum_node.log_val)
assert_almost_equal(sum_node.log_val,
LOG_ZERO,
places=15)
def test_sum_node_backprop():
# create child nodes
child1 = Node()
val1 = 1.
child1.set_val(val1)
child2 = Node()
val2 = 1.
child2.set_val(val2)
# create sum node and adding children to it
sum_node1 = SumNode()
weight11 = 0.8
weight12 = 0.2
sum_node1.add_child(child1, weight11)
sum_node1.add_child(child2, weight12)
# adding a coparent
sum_node2 = SumNode()
weight21 = 0.6
weight22 = 0.4
sum_node2.add_child(child1, weight21)
sum_node2.add_child(child2, weight22)
# evaluating
sum_node1.eval()
sum_node2.eval()
# setting the log derivatives to the parents
sum_node_der1 = 1.0
sum_node1.log_der = log(sum_node_der1)
sum_node1.backprop()
sum_node_der2 = 1.0
sum_node2.log_der = log(sum_node_der2)
sum_node2.backprop()
# checking for correctness
log_der1 = log(weight11 * sum_node_der1 +
weight21 * sum_node_der2)
log_der2 = log(weight12 * sum_node_der1 +
weight22 * sum_node_der2)
print('log ders 1:{lgd1} 2:{lgd2}'.format(lgd1=log_der1,
lgd2=log_der2))
assert_almost_equal(log_der1, child1.log_der, 15)
assert_almost_equal(log_der2, child2.log_der, 15)
# resetting
child1.log_der = LOG_ZERO
child2.log_der = LOG_ZERO
# now changing the initial der values
sum_node_der1 = 0.5
sum_node1.log_der = log(sum_node_der1)
sum_node1.backprop()
sum_node_der2 = 0.0
sum_node2.log_der = LOG_ZERO
sum_node2.backprop()
# checking for correctness
log_der1 = log(weight11 * sum_node_der1 +
weight21 * sum_node_der2)
log_der2 = log(weight12 * sum_node_der1 +
weight22 * sum_node_der2)
print('log ders 1:{lgd1} 2:{lgd2}'.format(lgd1=log_der1,
lgd2=log_der2))
assert_almost_equal(log_der1, child1.log_der, 15)
assert_almost_equal(log_der2, child2.log_der, 15)