tensorflow/tensorflow/python/eager/backprop_test.py at master · codeplaysoftware/tensorflow · GitHub

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# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

# http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

# ==============================================================================

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import functools

import numpy as np

from tensorflow.python import pywrap_tensorflow

from tensorflow.python.eager import backprop

from tensorflow.python.eager import context

from tensorflow.python.eager import tape

from tensorflow.python.eager import test

from tensorflow.python.framework import constant_op

from tensorflow.python.framework import dtypes

from tensorflow.python.framework import ops

from tensorflow.python.framework import tensor_shape

from tensorflow.python.framework import test_util

from tensorflow.python.ops import array_ops

from tensorflow.python.ops import control_flow_ops

from tensorflow.python.ops import custom_gradient

from tensorflow.python.ops import embedding_ops

from tensorflow.python.ops import gradients

from tensorflow.python.ops import math_ops

from tensorflow.python.ops import nn_grad # pylint: disable=unused-import

from tensorflow.python.ops import nn_ops

from tensorflow.python.ops import random_ops

from tensorflow.python.ops import resource_variable_ops

from tensorflow.python.ops import variables

from tensorflow.python.training import training

class BackpropTest(test.TestCase):

@test_util.run_in_graph_and_eager_modes()

def testAggregateGradients(self):

def fn(x):

ind1 = constant_op.constant(np.array([0, 1]))

ind2 = constant_op.constant(np.array([2, 3]))

ind3 = constant_op.constant(np.array([1, 3]))

# A mixture of IndexedSlices and dense tensor to aggregate.

g1 = embedding_ops.embedding_lookup(x, ind1)

g2 = embedding_ops.embedding_lookup(x, ind2)

g3 = embedding_ops.embedding_lookup(x, ind3)

g4 = math_ops.reduce_sum(x * constant_op.constant(2.0))

return g1 * g2 * g3 * g4

var_np = np.random.rand(4, 2).astype(np.float32)

var = constant_op.constant(var_np)

grad = backprop.gradients_function(fn, [0])(var)[0]

grad = self.evaluate(ops.convert_to_tensor(grad))

with context.graph_mode(), self.test_session():

tf_var = array_ops.constant(var_np, dtypes.float32)

tf_ind1 = array_ops.constant([0, 1])

tf_ind2 = array_ops.constant([2, 3])

tf_ind3 = array_ops.constant([1, 3])

tf_g1 = embedding_ops.embedding_lookup(tf_var, tf_ind1)

tf_g2 = embedding_ops.embedding_lookup(tf_var, tf_ind2)

tf_g3 = embedding_ops.embedding_lookup(tf_var, tf_ind3)

tf_g4 = math_ops.reduce_sum(tf_var * 2.0, reduction_indices=(0, 1))

tf_y = tf_g1 * tf_g2 * tf_g3 * tf_g4

tf_grad = gradients.gradients(tf_y, [tf_var])[0]

tf_dense_grad = math_ops.unsorted_segment_sum(

tf_grad.values, tf_grad.indices, tf_grad.dense_shape[0])

self.assertAllClose(grad, tf_dense_grad.eval())

def testImplicitGradWithResourceVariable(self):

x = resource_variable_ops.ResourceVariable(

initial_value=constant_op.constant(1.0), name='x')

def fn():

tape.watch_variable(x)

b = constant_op.constant(2.0)

c = math_ops.add(x.value(), b)

return math_ops.add(c, constant_op.constant(3.0))

grads_and_vars = backprop.implicit_grad(fn)()

self.assertAllEqual(grads_and_vars[0][0], 1.0)

self.assertAllEqual(id(grads_and_vars[0][1]), id(x))

def testWhereGradient(self):

# Note: where is special because only some of its arguments are of

# differentiable dtypes.

def f(x):

return array_ops.where(x < 10, x, x * x)

g = backprop.gradients_function(f)

self.assertAllEqual(g(5.)[0], 1.0)

self.assertAllEqual(g(50.)[0], 100.0)

def testTwoTargets(self):

with backprop.GradientTape() as t:

x = constant_op.constant(3.0)

y = constant_op.constant(2.0)

t.watch([x, y])

xx = 2 * x

yy = 3 * y

dx, dy = t.gradient([xx, yy], [x, y])

self.assertAllEqual(dx, 2.0)

self.assertAllEqual(dy, 3.0)

def testOutputGradUsedInComputation(self):

with backprop.GradientTape() as t:

x = constant_op.constant(3.0)

y = constant_op.constant(2.0)

t.watch([x, y])

loss = x * y

dx, = t.gradient([loss, x], [x], output_gradients=[1.0, 2.0])

self.assertAllEqual(dx, 4.0)

def testDy(self):

def f(x):

return x

grad_fn = backprop.gradients_function(f)

self.assertAllEqual(2., grad_fn(1., dy=2.)[0])

def testGradientInteger(self):

def f(x):

return x + x

int_tensor = constant_op.constant(1)

self.assertEqual(backprop.gradients_function(f)(int_tensor)[0], None)

def testErrors(self):

@custom_gradient.custom_gradient

def f(x):

def grad(_):

raise RuntimeError('x')

return x, grad

# TODO(apassos) raise the right error here

with self.assertRaises(RuntimeError):

backprop.gradients_function(f)(constant_op.constant(1.0))

def testGradientsFunctionInCustomGradient(self):

@custom_gradient.custom_gradient

def f(x):

(y,) = backprop.gradients_function(lambda x: x * x)(x)

def grad(dy):

return [2 * dy]

return y, grad

self.assertAllEqual(f(1.0), 2.0)

def testImplicitGradOverEmbeddingLookup(self):

batch_size = 8

embedding_size = 512

vocab_size = 1000

lrn_rate = 0.1

random_init = random_ops.random_uniform([vocab_size, embedding_size])

x = array_ops.ones((batch_size), dtypes.int64)

embedding = resource_variable_ops.ResourceVariable(

initial_value=random_init, dtype=dtypes.float32, name='embedding')

def f():

tape.watch_variable(embedding)

embedded_x = embedding_ops.embedding_lookup(embedding, x)

return constant_op.constant(1.0, dtypes.float32) - embedded_x

grad = backprop.implicit_grad(f)()[0][0]

opt = training.GradientDescentOptimizer(lrn_rate)

with context.graph_mode(), self.test_session():

tf_x = array_ops.ones((batch_size), dtypes.int64)

# TODO(ashankar,apassos): Change to ResourceVariable.

tf_embedding = variables.Variable(

random_init.numpy(), name='tf_embedding')

tf_embedded_x = embedding_ops.embedding_lookup(tf_embedding, tf_x)

tf_y = 1.0 - tf_embedded_x

tf_grad = gradients.gradients(tf_y, [tf_embedding])[0]

tf_opt = training.GradientDescentOptimizer(0.1)

tf_embedding.initializer.run()

self.assertAllClose(tf_grad.indices.eval(), grad.indices)

self.assertAllClose(tf_grad.values.eval(), grad.values)

tf_opt.apply_gradients([(tf_grad, tf_embedding)]).run()

expected = tf_embedding.eval()

opt.apply_gradients([(grad, embedding)])

self.assertAllClose(expected, embedding.read_value())

def testImplicitGradOrdering(self):

v0 = resource_variable_ops.ResourceVariable(1.0)

v1 = resource_variable_ops.ResourceVariable(2.0)

def f():

x = v1 * v1

y = v0 * v0

return x + y

grads = backprop.implicit_grad(f)()

ordered_variables = [x[1] for x in grads]

self.assertTrue(ordered_variables[0] is v0)

self.assertTrue(ordered_variables[1] is v1)

def testTapeStopRecording(self):

with backprop.GradientTape() as t:

x = constant_op.constant(1.0)

with t.stop_recording():

y = x * x

self.assertEqual(t.gradient(y, x), None)

def testTapeReset(self):

with backprop.GradientTape() as t:

v = resource_variable_ops.ResourceVariable(1.0)

loss = v * v

t.reset()

loss += v * v

self.assertAllEqual(t.gradient(loss, v), 2.0)

@test_util.assert_no_new_tensors

def testGradientNone(self):

def loss(x, l):

return math_ops.reduce_mean(

nn_ops.softmax_cross_entropy_with_logits(logits=x, labels=l),

constant_op.constant([0]))

logits = constant_op.constant([[0.0, 0.0]])

labels = constant_op.constant([[1.0, 0.0]])

# softmax_cross_entropy_with_logits returns two outputs and in this case the

# gradient wrt the second is None.

g, = backprop.gradients_function(loss, [0])(logits, labels)

self.assertAllEqual(g.numpy(), [[-0.5, 0.5]])

@test_util.run_in_graph_and_eager_modes()

def testGradientWithinTapeBlock(self):

v1 = resource_variable_ops.ResourceVariable(1.)

self.evaluate(v1.initializer)

with backprop.GradientTape() as t:

loss = 2 * v1

grad = t.gradient(loss, v1)

self.assertAllEqual(self.evaluate(grad), 2.0)

with backprop.GradientTape(persistent=True) as t:

loss = 2 * v1

grad = t.gradient(loss, v1)

self.assertAllEqual(self.evaluate(grad), 2.0)

@test_util.run_in_graph_and_eager_modes()

def testNestedSelfContexts(self):

v1 = resource_variable_ops.ResourceVariable(1.)

self.evaluate(v1.initializer)

with backprop.GradientTape() as t:

with self.assertRaises(ValueError):

with t:

pass

@test_util.assert_no_new_tensors

def testSecondGrad(self):

def first(x):

l = constant_op.constant([[0.0]])

x = nn_ops.softmax_cross_entropy_with_logits(labels=l, logits=x)

x = math_ops.reduce_sum(x, constant_op.constant([0]))

return x

def second(x):

grad = backprop.gradients_function(first, [0])(x)[0]

return math_ops.reduce_sum(grad, constant_op.constant([0]))

f = constant_op.constant([[0.1]])

grad = backprop.gradients_function(second, [0])(f)[0]

self.assertAllEqual([[0.0]], grad)

@test_util.assert_no_new_tensors

def testMakeVJP(self):

def f(x):

return x * x

wrapped_fn = backprop.make_vjp(f, persistent=False)

result, vjp = wrapped_fn(constant_op.constant(3.0))

self.assertAllEqual(result, 9.0)

self.assertAllEqual(vjp(2.0)[0], 12.0)

def testPersistentMakeVJP(self):

def f(x):

return x * x

wrapped_fn = backprop.make_vjp(f, persistent=True)

_, vjp = wrapped_fn(constant_op.constant(3.0))

vjp_result1 = vjp(2.0)[0]

vjp_result2 = vjp(2.0)[0]

self.assertAllEqual(vjp_result1, vjp_result2, 12.0)

@test_util.assert_no_new_tensors

def testGradGrad(self):

def sq(x):

return x * x

def grad(x):

value = backprop.gradients_function(sq, [0])(x)[0]

return value

gradgrad = backprop.gradients_function(grad, [0])

self.assertAllEqual(gradgrad(constant_op.constant(3.0))[0], 2.0)

@test_util.assert_no_new_tensors

def testGradGradExp(self):

def grad(x):

value = backprop.gradients_function(math_ops.exp, [0])(x)[0]

return value

gradgrad = backprop.gradients_function(grad, [0])

self.assertAllEqual(gradgrad(constant_op.constant(0.0))[0], 1.0)

@test_util.assert_no_new_tensors

def testStopGradient(self):

grad = backprop.gradients_function(

lambda x: array_ops.stop_gradient(math_ops.argmax(x)))

self.assertAllEqual(grad([0.0])[0], None)

@test_util.assert_no_new_tensors

def testArgmax(self):

def argmax(x):

i = math_ops.argmax(x)

return array_ops.stop_gradient(i)

grad = backprop.gradients_function(argmax)

self.assertAllEqual(grad([0.0])[0], None)

@test_util.assert_no_new_tensors

def testGPU(self):

if not context.context().num_gpus():

self.skipTest('No GPUs found')

def fn(x):

with context.device('/gpu:0'):

b = constant_op.constant(2.0)

c = math_ops.add(x.gpu(), b)

# TODO(apassos): remove cpu below by making TensorVSPace aware

# of devices.

return math_ops.add(c, constant_op.constant(3.0)).cpu()

grad = backprop.gradients_function(fn, [0])(constant_op.constant(1.0))[0]

self.assertAllEqual(grad, 1.0)

@test_util.assert_no_new_tensors

def testGPUImplicitGrad(self):

if not context.context().num_gpus():

self.skipTest('No GPU found')

with context.device('gpu:0'):

v = resource_variable_ops.ResourceVariable(

constant_op.constant(1.0), name='v')

def f():

with context.device('gpu:0'):

tape.watch_variable(v)

return v.read_value()

self.assertEqual(

backprop.implicit_grad(f)()[0][0].cpu().numpy(), 1.0)

@test_util.assert_no_new_tensors

def testCPU(self):

def fn(x):

b = constant_op.constant(2.0)

c = math_ops.add(x, b)

return math_ops.add(c, constant_op.constant(3.0))

grad = backprop.gradients_function(fn, [0])(constant_op.constant(1.0))[0]

self.assertAllEqual(grad, 1.0)

@test_util.assert_no_new_tensors

def testTensorCopyGPU2CPU2GPU(self):

if not context.context().num_gpus():

self.skipTest('No GPUs found')

def f(a, b):

return a.cpu() + b.cpu()

with context.device('/gpu:0'):

a = constant_op.constant(1.0)

b = constant_op.constant(2.0)

grad = backprop.gradients_function(f, [0])(a, b)[0]

self.assertAllEqual(grad, 1.0)

@test_util.assert_no_new_tensors

def testEmptyParams(self):

def fn(a, b):

return a * b

x = constant_op.constant(1.0)

y = constant_op.constant(2.0)

dx, dy = backprop.gradients_function(fn)(x, y)

self.assertAllEqual(dx, y.numpy())

self.assertAllEqual(dy, x.numpy())

@test_util.assert_no_new_tensors

def testUnconnectedNone(self):

v = resource_variable_ops.ResourceVariable(

1.0, name='testUnconnectedNone')

def f():

v.read_value()

return constant_op.constant(1.0)

self.assertEqual(backprop.implicit_grad(f)()[0][0], None)

@test_util.assert_no_new_tensors

@test_util.run_in_graph_and_eager_modes()

def testGradientTapeRepeatedSource(self):

with backprop.GradientTape(persistent=False) as g:

x = constant_op.constant(3.0)

g.watch(x)

y = 2 * x

grad = g.gradient(target=y, sources=[x, x])

self.assertEqual(self.evaluate(grad), [2.0, 2.0])

@test_util.assert_no_new_tensors

@test_util.run_in_graph_and_eager_modes()

def testPersistentGradientTapeRepeatedSource(self):

with backprop.GradientTape(persistent=True) as g:

x = constant_op.constant(3.0)

y = constant_op.constant(5.0)

g.watch(x)

g.watch(y)

z = x * x + x * y

grad = g.gradient(target=z, sources=[x, x])

self.assertEqual(self.evaluate(grad), [11.0, 11.0])

grad = g.gradient(target=z, sources=[y, x])

self.assertEqual(self.evaluate(grad), [3.0, 11.0])

@test_util.assert_no_new_tensors

@test_util.run_in_graph_and_eager_modes()

def testGradientTapeStructure(self):

with backprop.GradientTape(persistent=True) as g:

# Using different constant values because constant tensors are

# cached, leading to a different gradient then what one might expect.

x1 = constant_op.constant(3.0)

x2 = constant_op.constant(3.1)

x3 = constant_op.constant(3.2)

g.watch(x1)

g.watch(x2)

g.watch(x3)

y = x1 + 2 * x2 + 3 * x3

self.assertEqual(self.evaluate(g.gradient(y, x1)), [1.0])

self.assertEqual(self.evaluate(g.gradient(y, (x1,))), (1.0,))

self.assertEqual(self.evaluate(g.gradient(y, (x1, x2))), (1.0, 2.0))

self.assertEqual(self.evaluate(g.gradient(y, [(x1, x2), (x2, x3)])),

[(1.0, 2.0), (2.0, 3.0)])

self.assertEqual(self.evaluate(g.gradient(y, (x1, x2, [x1, x3]))),

(1.0, 2.0, [1.0, 3.0]))

self.assertEqual(self.evaluate(g.gradient(y, [x1, {'x2': x2, 'x3': x3}])),

[1.0, {'x2': 2.0, 'x3': 3.0}])

@test_util.assert_no_new_tensors

@test_util.run_in_graph_and_eager_modes()

def testGradientTape(self):

with backprop.GradientTape() as g:

x = constant_op.constant(3.0)

g.watch(x)

y = x * x

with backprop.GradientTape() as gg:

gg.watch(y)

z = 2 * y

inner_grad = gg.gradient(z, [y])[0]

self.assertEqual(self.evaluate(inner_grad), 2.0)

y += inner_grad

grad = g.gradient(y, [x])[0]

self.assertEqual(self.evaluate(grad), 6.0)

@test_util.run_in_graph_and_eager_modes()

def testGradientTapeWithCond(self):

x = constant_op.constant(3.0)

def true_fn():

return x

def false_fn():

return x * x

with backprop.GradientTape() as g:

g.watch(x)

y = control_flow_ops.cond(x < x, true_fn, false_fn)

if not context.executing_eagerly():

with self.assertRaisesRegexp(NotImplementedError, 'tf.gradients'):

dy = g.gradient(y, [x])[0]

else:

dy = g.gradient(y, [x])[0]

self.assertEqual(self.evaluate(dy), 6.0)

@test_util.run_in_graph_and_eager_modes()

def testGradientTapeWithWhileLoop(self):

i = constant_op.constant(1)

x = constant_op.constant(2.)

def cond(i, _):

return i < 3

def body(i, x):

return i + 1, x * 2

with backprop.GradientTape() as g:

g.watch([x])

_, y = control_flow_ops.while_loop(cond, body, [i, x])

if not context.executing_eagerly():

with self.assertRaisesRegexp(NotImplementedError, 'tf.gradients'):

dy = g.gradient(y, [x])[0]

else:

dy = g.gradient(y, [x])[0]

self.assertEqual(self.evaluate(dy), 4.0)

@test_util.assert_no_new_tensors

def testGradientTapeGradientCalledMultipleTimes(self):

with backprop.GradientTape() as g:

x = constant_op.constant(3.0)

g.watch(x)

y = x * x

z = y * y

g.gradient(z, [x])

with self.assertRaisesRegexp(

RuntimeError, 'GradientTape.gradient can only be called once'):

g.gradient(y, [x])

@test_util.assert_no_new_tensors

@test_util.run_in_graph_and_eager_modes()

def testPersistentTape(self):

with backprop.GradientTape(persistent=True) as g:

x = constant_op.constant(3.0)

g.watch(x)

y = x * x

z = y * y

dz_dx = g.gradient(z, [x])[0]

self.assertEqual(self.evaluate(dz_dx), 4 * 3 * 3 * 3)

dy_dx = g.gradient(y, [x])[0]

self.assertEqual(self.evaluate(dy_dx), 2 * 3)

del g

@test_util.assert_no_new_tensors

@test_util.run_in_graph_and_eager_modes()

def testHigherOrderGradient(self):

with backprop.GradientTape(persistent=True) as g:

x = constant_op.constant(3.0)

g.watch(x)

y = x ** 3 # y := x^3

dy_dx = g.gradient(y, x) # dy/dx := 3x^2

d2y_dx2 = g.gradient(dy_dx, x) # d2y/dx2 := 6x

d3y_dx3 = g.gradient(d2y_dx2, x) # d3y/dx3 := 6

x = 3

self.assertEqual(self.evaluate(y), x ** 3)

self.assertEqual(self.evaluate(dy_dx), 3 * x ** 2)

self.assertEqual(self.evaluate(d2y_dx2), 6 * x)

self.assertEqual(self.evaluate(d3y_dx3), 6)

del g

@test_util.assert_no_new_tensors

@test_util.run_in_graph_and_eager_modes()

def testPersistentNestedTape(self):

with backprop.GradientTape(persistent=True) as g:

x = constant_op.constant(3.0)

g.watch(x)

y = x * x

with backprop.GradientTape(persistent=True) as gg:

gg.watch(y)

z = 2 * y

for _ in range(2):

inner_grad = gg.gradient(z, [y])[0]

self.assertEqual(self.evaluate(inner_grad), 2.0)

y += inner_grad

del gg

grad = g.gradient(y, [x])[0]

self.assertEqual(self.evaluate(grad), 6.0)

grad = g.gradient(z, [x])[0]

self.assertEqual(self.evaluate(grad), 12.0)

del g

@test_util.assert_no_new_tensors

@test_util.run_in_graph_and_eager_modes()

def testGradientTapeVariable(self):

v = resource_variable_ops.ResourceVariable(1.0, name='v')

self.evaluate(v.initializer)

with backprop.GradientTape() as g:

y = v * v

grad = g.gradient(y, [v])[0]

self.assertAllEqual(self.evaluate(grad), 2.0)

@test_util.assert_no_new_tensors

@test_util.run_in_graph_and_eager_modes()

def testNestedGradients(self):

x = constant_op.constant(3.0)

with backprop.GradientTape() as g:

g.watch(x)

y = x * x

z = y * y

dz_dx, dz_dy = g.gradient(z, [x, y])

self.assertEqual(self.evaluate(dz_dx), 108.0)

self.assertEqual(self.evaluate(dz_dy), 18.0)

@test_util.assert_no_new_tensors

def testEmptyParamsForValueAndGradFunction(self):

def fn(a, b):

return a * b

val_and_grads_fn = backprop.val_and_grad_function(fn)

x = 2.0

y = 3.0

val, (dx, dy) = val_and_grads_fn(x, y)

self.assertAllClose(val, x * y)

self.assertAllEqual(dx, y)

self.assertAllEqual(dy, x)

@test_util.assert_no_new_tensors

def testNonEmptyParamsForValueAndGradFunction(self):

def fn(a, b):

return a * b

val_and_grad_fn = backprop.val_and_grad_function(fn, params=[1])

x = 2.0

y = 3.0

val, grads = val_and_grad_fn(x, y)

self.assertAllClose(val, x * y)

self.assertEqual(1, len(grads))

self.assertAllEqual(grads[0], x)

@test_util.assert_no_new_tensors

def testTensorCopyCPU2GPU2CPU(self):

if not context.context().num_gpus():

self.skipTest('No GPUs found')

# forward: a (cpu->gpu) -> add (gpu) -> c (gpu->cpu) -> add (cpu) -> e (cpu)

# back: e (cpu) -> add (cpu) -> c (cpu->gpu) -> add (gpu) -> grad (gpu->cpu)

def f(a, b):

with context.device('/gpu:0'):

c = math_ops.add(a.gpu(0), b.gpu(0))

return math_ops.add(c.cpu(), constant_op.constant(3.0))

with context.device('/cpu:0'):

a = constant_op.constant(1.0)

b = constant_op.constant(2.0)

grad = backprop.gradients_function(f, [0])(a, b)[0]

self.assertAllEqual(grad, 1.0)

def testGetAttrType(self):

typ = backprop.op_attr_type('Add', 'T')

self.assertEqual(typ, pywrap_tensorflow.TF_ATTR_TYPE)

def testGetAttrList(self):

typ = backprop.op_attr_type('MaxPool', 'ksize')

self.assertEqual(typ, [pywrap_tensorflow.TF_ATTR_INT])

def testMakeAttrType(self):

self.assertEqual(dtypes.float32,

backprop.make_attr(pywrap_tensorflow.TF_ATTR_TYPE, 1))

def testMakeAttrTypeList(self):

self.assertEqual([dtypes.float32],

backprop.make_attr([pywrap_tensorflow.TF_ATTR_TYPE], [1]))

def testMulType(self):

def mul(x):

return math_ops._mul_dispatch(x, x) # pylint: disable=protected-access

self.assertAllEqual(

backprop.gradients_function(mul)(3.0)[0].numpy(),

6.0)

def testMakeAttrShape(self):

for s in ([], None, [1, 2, 3], [None, None], [1, None, 3]):

expected = tensor_shape.TensorShape(s).as_proto()

actual = backprop.make_attr(pywrap_tensorflow.TF_ATTR_SHAPE, s)

self.assertEqual(

expected,

actual,

msg=('For shape %r, expected %r != %r actual' % (s, expected,

actual)))

def testMakeAttrShapeList(self):

shape_list = [[], None, [1, 2, 3], [None, None], [1, None, 3]]

self.assertEqual(

[tensor_shape.TensorShape(s).as_proto() for s in shape_list],

backprop.make_attr([pywrap_tensorflow.TF_ATTR_SHAPE], shape_list))

def testArgsGradientFunction(self):

def f(*args):

return args[0] * args[0]

grad = backprop.gradients_function(f)

self.assertAllEqual(grad(1.0)[0], 2.0)

def testPartial(self):

def f(x, y):

return x * y

part = functools.partial(f, constant_op.constant(2.0))

self.assertAllEqual(

backprop.gradients_function(part)(constant_op.constant(1.0))[0],

2.0)

def testReturnSameThing(self):

def f(x):

return x, 2 * x

self.assertAllEqual(backprop.gradients_function(f)(1.0)[0], 3.0)

@test_util.assert_no_new_tensors

def testExceptionSafety(self):

def f(unused_x):

raise ValueError()

try:

backprop.gradients_function(f)(1.0)

except ValueError:

pass

def real_f(x):

return x * x

self.assertAllEqual(backprop.gradients_function(real_f)(1.0)[0], 2.0)

@test_util.assert_no_new_tensors

def testMultiValueConvertToTensor(self):

x = resource_variable_ops.ResourceVariable(

initial_value=array_ops.constant([1.0]), name='x')

def fn():

tape.watch_variable(x)

a = math_ops.add(x.value(), 1.0)

# Make sure convert_to_tensor works correctly with list of TensorNodes.

b = array_ops.stack([a, a], axis=0)

return math_ops.reduce_mean(b)

grad = backprop.implicit_grad(fn)()[0][0]

self.assertAllEqual([1.0], grad)

def testOutput(self):

def multiout(x):

return x + 2, x * x

x = constant_op.constant([0.0, 1.0, 2.0])

grad = backprop.gradients_function(multiout)(x)[0]

self.assertAllEqual([1.0, 3.0, 5.0], grad)

def testMultiValuePreservesIfNotDiffedAgainst(self):

def tfe_conv2d(timage, tkernel, conv2dstrides):

return nn_ops.conv2d(timage, tkernel, conv2dstrides, 'SAME')

i = constant_op.constant([[[[1.0]]]])

k = constant_op.constant([[[[2.0]]]])

s = [1, 1, 1, 1]

grad = backprop.gradients_function(tfe_conv2d, params=(0,))(i, k, s)[0]

self.assertAllEqual([[[[2.0]]]], grad)

def testSameObjectForMultipleArguments(self):

def f(x, y):

return math_ops.multiply(x, y)

g = backprop.gradients_function(f)

def np_g(x, y):

dx, dy = g(x, y)

return [dx.numpy(), dy.numpy()]

x = constant_op.constant(1.)

self.assertAllEqual([1., 1.], np_g(x, x))

x = 1.

self.assertAllEqual([1., 1.], np_g(x, x))

x = constant_op.constant([[1.]])

self.assertAllEqual([[[1.]], [[1.]]], np_g(x, x))

x = [[1.]]

self.assertAllEqual([[[1.]], [[1.]]], np_g(x, x))

v = resource_variable_ops.ResourceVariable(

initial_value=1., name='testSameObjectForMultipleArguments.Variable')

self.assertAllEqual([1., 1.], np_g(v, v))

@test_util.assert_no_new_tensors

def testImplicitGradientsCustomGradientAndCachedVariableValue(self):

@custom_gradient.custom_gradient

def my_square(x):

result = math_ops.square(x)

def grad(dr):

return 2 * dr * x + 1

return result, grad

x = resource_variable_ops.ResourceVariable(

initial_value=3., name='X.' + self.id())

def f():

return my_square(x)

g = backprop.implicit_grad(f)

grads_and_vars = g()

self.assertEqual(1, len(grads_and_vars))

grad, var = grads_and_vars[0]

self.assertAllEqual(7, grad)

self.assertAllEqual(x, var)

@test_util.assert_no_new_tensors

def testCustomGradient(self):

@custom_gradient.custom_gradient

def my_mul(x, y):

result = x*y

def grad(dr):

return [dr*y, dr*x]

return result, grad

lr = 0.25

x = resource_variable_ops.ResourceVariable(2., name='x')

def loss(x):

return my_mul(2., x.read_value())

loss_grads_fn = backprop.implicit_val_and_grad(loss)

losses = []

for _ in range(5):

loss, grads_and_vars = loss_grads_fn(x)

losses.append(loss.numpy())

for (grad, var) in grads_and_vars:

var.assign_sub(lr*grad)

self.assertAllEqual(losses, [4.0, 3., 2., 1., 0.])

@test_util.assert_no_new_tensors

def testCustomGradientIdentity(self):

@custom_gradient.custom_gradient

def my_identity(x):

def grad(dresult):

return [2 * dresult]

return x, grad

self.assertAllEqual(backprop.gradients_function(my_identity)(1.0)[0], 2.0)

def testDifferentiatingFunctionThatReturnsNone(self):

def fn(x, y):

result = x*y # pylint: disable=unused-variable

x = constant_op.constant(1)

y = constant_op.constant(2)

loss_grads_fn = backprop.implicit_val_and_grad(fn)

with self.assertRaisesRegexp(

ValueError, 'Cannot differentiate a function that returns None; '

'did you forget to return a value from fn?'):

loss_grads_fn(x, y)

val_and_grads_fn = backprop.val_and_grad_function(fn)

with self.assertRaisesRegexp(

ValueError, 'Cannot differentiate a function that returns None; '

'did you forget to return a value from fn?'):

val_and_grads_fn(x, y)

if __name__ == '__main__':

test.main()

tensorflow/tensorflow/python/eager/backprop_test.py at master · codeplaysoftware/tensorflow · GitHub

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