本文整理汇总了Python中tensorflow.python.ops.gradient_checker.compute_gradient方法的典型用法代码示例。如果您正苦于以下问题:Python gradient_checker.compute_gradient方法的具体用法?Python gradient_checker.compute_gradient怎么用?Python gradient_checker.compute_gradient使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.python.ops.gradient_checker
的用法示例。
在下文中一共展示了gradient_checker.compute_gradient方法的5个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _warp_test
# 需要导入模块: from tensorflow.python.ops import gradient_checker [as 别名]
# 或者: from tensorflow.python.ops.gradient_checker import compute_gradient [as 别名]
def _warp_test(self, first, second, flow, debug=False):
with self.test_session(use_gpu=True) as sess:
num_batch, height, width, channels = second.shape
#second_ = tf.placeholder(tf.float32, shape=second.shape, name='im')
flow_ = tf.placeholder(tf.float32, shape=flow.shape, name='flow')
inv_warped_second = ops.backward_warp(second, flow_)
pred = sess.run(inv_warped_second, feed_dict={flow_: flow})
if debug:
print('-- result channels')
for c in range(channels):
print(np.reshape(pred[0, :, :, c], [height, width]))
self.assertAllClose(first, pred)
jacob_t, jacob_n = gradient_checker.compute_gradient(flow_, flow.shape,
inv_warped_second, pred.shape)
self.assertAllClose(jacob_t, jacob_n, 1e-3, 1e-3)
示例2: _test_correlation
# 需要导入模块: from tensorflow.python.ops import gradient_checker [as 别名]
# 或者: from tensorflow.python.ops.gradient_checker import compute_gradient [as 别名]
def _test_correlation(self, in0, in1, out=None, **kwargs):
with self.test_session(use_gpu=True) as sess:
in0_op = tf.constant(in0, tf.float32)
in1_op = tf.constant(in1, tf.float32)
result_op = ops.correlation(in0_op, in1_op, **kwargs)
result = sess.run(result_op)
if out is not None:
self.assertAllClose(out, result)
jacob_t, jacob_n = gradient_checker.compute_gradient([in0_op, in1_op],
[in0.shape, in1.shape],
result_op, result.shape)
#print("--------------- n")
#print(jacob_n)
#print("--------------- t")
#print(jacob_t)
self.assertAllClose(jacob_t, jacob_n, 1e-3, 1e-3)
示例3: test_high_dim_filter_grad
# 需要导入模块: from tensorflow.python.ops import gradient_checker [as 别名]
# 或者: from tensorflow.python.ops.gradient_checker import compute_gradient [as 别名]
def test_high_dim_filter_grad(self):
x_shape = [5, 10, 10]
# Test inputs: unaries and RGB values
unary_np = np.random.randn(*x_shape).astype(np.float32)
rgb_np = np.random.randint(low=0, high=256, size=x_shape).astype(np.float32)
with self.test_session():
unary_tf = constant_op.constant(unary_np)
rgb_tf = constant_op.constant(rgb_np)
y_tf = custom_module.high_dim_filter(unary_tf, rgb_tf,
bilateral=True,
theta_alpha=1000.,
theta_beta=1000.,
theta_gamma=1000.)
out = gradient_checker.compute_gradient([unary_tf, rgb_tf], [x_shape, x_shape],
y_tf, x_shape)
# We only need to compare gradients w.r.t. unaries
computed = out[0][0].flatten()
estimated = out[0][1].flatten()
mask = (computed != 0)
computed = computed[mask]
estimated = estimated[mask]
difference = computed - estimated
measure1 = np.mean(difference) / np.mean(computed)
measure2 = np.max(difference) / np.max(computed)
print('Gradient check: measure1 = {:.6f}, measure2 = {:.6f}'.format(measure1, measure2))
self.assertLess(measure1, 1e-3, 'Errors found in the gradient computation.')
self.assertLess(measure2, 2e-2, 'Errors found in the gradient computation.')
print('Gradient check: success!')
示例4: test_grad
# 需要导入模块: from tensorflow.python.ops import gradient_checker [as 别名]
# 或者: from tensorflow.python.ops.gradient_checker import compute_gradient [as 别名]
def test_grad(self):
with self.test_session(use_gpu=True) as sess:
flow_shape = [1, 10, 10, 2]
warped_shape = [1, 10, 10, 1]
flow_ = tf.placeholder(tf.float32, shape=flow_shape, name='flow')
warped_ = ops.forward_warp(flow_)
jacob_t, jacob_n = gradient_checker.compute_gradient(flow_, flow_shape,
warped_, warped_shape)
self.assertAllClose(jacob_t, jacob_n, 1e-3, 1e-3)
示例5: _computeGradient
# 需要导入模块: from tensorflow.python.ops import gradient_checker [as 别名]
# 或者: from tensorflow.python.ops.gradient_checker import compute_gradient [as 别名]
def _computeGradient(self, np_input, bias, dtype, data_format):
input_shape = output_shape = np_input.shape
bias_shape = bias.shape
input_tensor = constant_op.constant(
np_input, shape=input_shape, dtype=dtype)
bias_tensor = constant_op.constant(bias, shape=bias_shape, dtype=dtype)
if context.executing_eagerly():
def bias_add(input_tensor, bias_tensor):
return nn_ops.bias_add(
input_tensor, bias_tensor, data_format=data_format)
# The following is a work-around for TF issue 33660. Instead of
# calculating the analytical and numerical gradients for both
# inputs in a single call to compute_gradient, compute_gradient
# is called for each input separately.
def bias_add_1(input_tensor):
return bias_add(input_tensor, bias_tensor)
def bias_add_2(bias_tensor):
return bias_add(input_tensor, bias_tensor)
input_jacob_a, input_jacob_n = gradient_checker_v2.compute_gradient(
bias_add_1, [input_tensor])
bias_jacob_a, bias_jacob_n = gradient_checker_v2.compute_gradient(
bias_add_2, [bias_tensor])
# Test gradient of BiasAddGrad
def bias_add_grad_function(upstream_gradients):
with backprop.GradientTape() as tape:
tape.watch(bias_tensor)
bias_add_output = bias_add(input_tensor, bias_tensor)
gradient_injector_output = bias_add_output * upstream_gradients
return tape.gradient(gradient_injector_output, bias_tensor)
upstream_tensor = self._random_tensor(output_shape, dtype)
grad_jacob_a, grad_jacob_n = gradient_checker_v2.compute_gradient(
bias_add_grad_function, [upstream_tensor])
else:
output_tensor = nn_ops.bias_add(
input_tensor, bias_tensor, data_format=data_format)
jacobians = gradient_checker.compute_gradient(
[input_tensor, bias_tensor], [input_shape, bias_shape],
output_tensor, output_shape)
(input_jacob_a, input_jacob_n), (bias_jacob_a, bias_jacob_n) = jacobians
# Test gradient of BiasAddGrad
bias_add_grad = gradients_impl.gradients(
nn_ops.l2_loss(output_tensor), bias_tensor)[0]
grad_jacob_a, grad_jacob_n = gradient_checker.compute_gradient(
output_tensor, output_shape, bias_add_grad, bias_shape)
return ((input_jacob_a, bias_jacob_a, grad_jacob_a),
(input_jacob_n, bias_jacob_n, grad_jacob_n))