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Lint Python files with Yapf (#1872)

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This commit is contained in:
Philipp Moritz
2018-04-11 10:11:35 -07:00
committed by Robert Nishihara
parent a3ddde398c
commit 74162d1492
97 changed files with 3927 additions and 3139 deletions
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test/tensorflow_test.py
+27 -27
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@@ -23,7 +23,6 @@ def make_linear_network(w_name=None, b_name=None):
class LossActor(object):
def __init__(self, use_loss=True):
# Uses a separate graph for each network.
with tf.Graph().as_default():
@@ -32,10 +31,8 @@ class LossActor(object):
loss, init, _, _ = make_linear_network()
sess = tf.Session()
# Additional code for setting and getting the weights.
weights = ray.experimental.TensorFlowVariables(loss if use_loss
else None,
sess,
input_variables=var)
weights = ray.experimental.TensorFlowVariables(
loss if use_loss else None, sess, input_variables=var)
# Return all of the data needed to use the network.
self.values = [weights, init, sess]
sess.run(init)
@@ -49,7 +46,6 @@ class LossActor(object):
class NetActor(object):
def __init__(self):
# Uses a separate graph for each network.
with tf.Graph().as_default():
@@ -71,7 +67,6 @@ class NetActor(object):
class TrainActor(object):
def __init__(self):
# Almost the same as above, but now returns the placeholders and
# gradient.
@@ -82,16 +77,17 @@ class TrainActor(object):
optimizer = tf.train.GradientDescentOptimizer(0.9)
grads = optimizer.compute_gradients(loss)
train = optimizer.apply_gradients(grads)
self.values = [loss, variables, init, sess, grads, train,
[x_data, y_data]]
self.values = [
loss, variables, init, sess, grads, train, [x_data, y_data]
]
sess.run(init)
def training_step(self, weights):
_, variables, _, sess, grads, _, placeholders = self.values
variables.set_weights(weights)
return sess.run([grad[0] for grad in grads],
feed_dict=dict(zip(placeholders,
[[1] * 100, [2] * 100])))
return sess.run(
[grad[0] for grad in grads],
feed_dict=dict(zip(placeholders, [[1] * 100, [2] * 100])))
def get_weights(self):
return self.values[1].get_weights()
@@ -216,8 +212,8 @@ class TensorFlowTest(unittest.TestCase):
net2 = ray.remote(NetActor).remote()
weights2 = ray.get(net2.get_weights.remote())
new_weights2 = ray.get(net2.set_and_get_weights.remote(
net2.get_weights.remote()))
new_weights2 = ray.get(
net2.set_and_get_weights.remote(net2.get_weights.remote()))
self.assertEqual(weights2, new_weights2)
def testVariablesControlDependencies(self):
@@ -247,22 +243,26 @@ class TensorFlowTest(unittest.TestCase):
net_values = TrainActor().values
loss, variables, _, sess, grads, train, placeholders = net_values
before_acc = sess.run(loss, feed_dict=dict(zip(placeholders,
[[2] * 100,
[4] * 100])))
before_acc = sess.run(
loss, feed_dict=dict(zip(placeholders, [[2] * 100, [4] * 100])))
for _ in range(3):
gradients_list = ray.get(
[net.training_step.remote(variables.get_weights())
for _ in range(2)])
mean_grads = [sum([gradients[i] for gradients in gradients_list]) /
len(gradients_list) for i
in range(len(gradients_list[0]))]
feed_dict = {grad[0]: mean_grad for (grad, mean_grad)
in zip(grads, mean_grads)}
gradients_list = ray.get([
net.training_step.remote(variables.get_weights())
for _ in range(2)
])
mean_grads = [
sum([gradients[i]
for gradients in gradients_list]) / len(gradients_list)
for i in range(len(gradients_list[0]))
]
feed_dict = {
grad[0]: mean_grad
for (grad, mean_grad) in zip(grads, mean_grads)
}
sess.run(train, feed_dict=feed_dict)
after_acc = sess.run(loss, feed_dict=dict(zip(placeholders,
[[2] * 100, [4] * 100])))
after_acc = sess.run(
loss, feed_dict=dict(zip(placeholders, [[2] * 100, [4] * 100])))
self.assertTrue(before_acc < after_acc)