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 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)
