def testIndexedSlicesGradientInCondInWhileLoop(self):
with ops.Graph().as_default():
embedding_matrix = tf.get_variable(
"embedding_matrix", [5, 5],
initializer=tf.random_normal_initializer())
def Cond(it, _):
return it < 5
def Body(it, cost):
embedding = embedding_ops.embedding_lookup(embedding_matrix, [0])
cost = tf.cond(tf.equal(it, 3),
lambda: tf.square(cost),
lambda: cost + tf.reduce_sum(embedding))
return it + 1, cost
_, cost = control_flow_ops.While(
Cond, Body, [tf.constant(0), tf.constant(0.0)])
dynamic_grads = tf.gradients(cost, [embedding_matrix])[0]
dynamic_grads = tf.segment_sum(dynamic_grads.values,
dynamic_grads.indices)
embedding = embedding_ops.embedding_lookup(embedding_matrix, [0])
static = tf.square(
tf.reduce_sum(embedding) +
tf.reduce_sum(embedding) +
tf.reduce_sum(embedding)) + tf.reduce_sum(embedding)
static_grads = tf.gradients(static, [embedding_matrix])[0]
static_grads = tf.segment_sum(static_grads.values, static_grads.indices)
with self.test_session() as sess:
sess.run(tf.initialize_all_variables())
self.assertAllEqual(*sess.run([static_grads, dynamic_grads]))
def testIndexedSlicesWithShapeGradientInWhileLoop(self):
with self.test_session() as sess:
num_steps = 9
inputs = tf.placeholder(dtype="float32", shape=[num_steps])
initial_outputs = tf.TensorArray(dtype="float32", size=num_steps)
initial_i = tf.constant(0, dtype="int32")
def Cond(i, _):
return i < num_steps
def Body(i, outputs):
x = tf.gather(inputs, i)
outputs = outputs.write(i, x)
return i + 1, outputs
_, outputs = tf.while_loop(Cond, Body, [initial_i, initial_outputs])
outputs = tf.reduce_sum(outputs.pack())
r = tf.gradients([outputs], [inputs])[0]
grad_wr_inputs = ops.convert_to_tensor(r)
o, grad = sess.run([outputs, grad_wr_inputs],
feed_dict={inputs: [4, 6, 0, 7, 0, 0, 1, 2, 0]})
self.assertEquals(o, 20)
self.assertAllEqual(grad, [1] * num_steps)
def testIndexedSlicesWithDynamicShapeGradientInWhileLoop(self):
for dtype in [dtypes.float32, dtypes.float64]:
with self.test_session() as sess:
inputs = tf.placeholder(dtype=dtype)
initial_outputs = tf.TensorArray(dtype=dtype, dynamic_size=True,
size=1)
initial_i = tf.constant(0, dtype=dtypes.int32)
def Cond(i, _):
return i < tf.size(inputs) # pylint: disable=cell-var-from-loop
def Body(i, outputs):
x = tf.gather(inputs, i) # pylint: disable=cell-var-from-loop
outputs = outputs.write(i, x)
return i + 1, outputs
_, outputs = tf.while_loop(Cond, Body, [initial_i, initial_outputs])
outputs = tf.reduce_sum(outputs.pack())
r = tf.gradients([outputs], [inputs])[0]
grad_wr_inputs = ops.convert_to_tensor(r)
o, grad = sess.run([outputs, grad_wr_inputs],
feed_dict={inputs: [1, 3, 2]})
self.assertEquals(o, 6)
self.assertAllEqual(grad, [1] * 3)
def testIndexedSlicesWithDynamicShapeGradientInWhileLoop(self):
for dtype in [dtypes.float32, dtypes.float64]:
with self.test_session() as sess:
inputs = tf.placeholder(dtype=dtype)
initial_outputs = tf.TensorArray(dtype=dtype,
dynamic_size=True,
size=1)
initial_i = tf.constant(0, dtype=dtypes.int32)
def Cond(i, _):
return i < tf.size(inputs) # pylint: disable=cell-var-from-loop
def Body(i, outputs):
x = tf.gather(inputs, i) # pylint: disable=cell-var-from-loop
outputs = outputs.write(i, x)
return i + 1, outputs
_, outputs = tf.while_loop(Cond, Body,
[initial_i, initial_outputs])
outputs = tf.reduce_sum(outputs.pack())
r = tf.gradients([outputs], [inputs])[0]
grad_wr_inputs = ops.convert_to_tensor(r)
o, grad = sess.run([outputs, grad_wr_inputs],
feed_dict={inputs: [1, 3, 2]})
self.assertEquals(o, 6)
self.assertAllEqual(grad, [1] * 3)
