我们如何在 TensorFlow 2.0 中使用 lbfgs_minimize

Question

我无法从安装了 tensorflow 2.0 的 tensorflow 重现 this example。

这是原始片段：

# A high-dimensional quadratic bowl.
ndims = 60
minimum = np.ones([ndims], dtype='float64')
scales = np.arange(ndims, dtype='float64') + 1.0

# The objective function and the gradient.
def quadratic(x):
    value = tf.reduce_sum(scales * (x - minimum) ** 2)
    return value, tf.gradients(value, x)[0]

start = np.arange(ndims, 0, -1, dtype='float64')
optim_results = tfp.optimizer.lbfgs_minimize(
  quadratic, initial_position=start, num_correction_pairs=10,
  tolerance=1e-8)

with tf.Session() as session:
    results = session.run(optim_results)
# Check that the search converged
assert(results.converged)
# Check that the argmin is close to the actual value.
np.testing.assert_allclose(results.position, minimum)

以下错误不起作用：

RuntimeError: tf.gradients is not supported when eager execution is enabled. Use tf.GradientTape instead.

如果我更改代码并改用渐变胶带，如下所示：

def quadratic(x):

    x = tf.Variable(x, dtype='float64')
    with tf.GradientTape() as t:
        value = tf.reduce_sum(scales * (x - minimum) ** 2)
    grad = t.gradient(value, x)

    return value, grad

我也收到以下错误：

TypeError: Tensor is unhashable if Tensor equality is enabled. Instead, use tensor.experimental_ref() as the key.

总的来说，我尝试过的任何东西都不起作用，而且我不知道如何在 tensorflow 2.0 中使用 lbfgs。

Answer 1

使用tf.function in your objective function so it is executed as a graph, then you will be able to use tf.gradients:

import tensorflow as tf
import tensorflow_probability as tfp
import numpy as np

# A high-dimensional quadratic bowl.
ndims = 60
minimum = tf.ones([ndims], dtype='float64')
scales = tf.range(ndims, dtype='float64') + 1.0

# The objective function and the gradient.
@tf.function
def quadratic(x):
    value = tf.reduce_sum(scales * (x - minimum) ** 2)
    return value, tf.gradients(value, x)[0]

start = tf.range(ndims, 0, -1, dtype='float64')
optim_results = tfp.optimizer.lbfgs_minimize(
  quadratic, initial_position=start, num_correction_pairs=10,
  tolerance=1e-8)

# Check that the search converged
print(optim_results.converged.numpy())
# True

# Check that the argmin is close to the actual value.
print(np.allclose(optim_results.position.numpy(), minimum.numpy()))
# True

Answer 2

只是为@jdehesa 的答案添加一点 - 在这种情况下使用 tfp.math.value_and_gradient 也很有用，如果您使用急切模式，它将为您创建渐变带。例如：

import tensorflow as tf
import tensorflow_probability as tfp

ndims = 60
minimum = tf.ones([ndims], dtype="float64")
scales = tf.range(ndims, dtype="float64") + 1.0


def quadratic(x):
    value = tf.reduce_sum(scales * (x - minimum) ** 2)
    return value


start = tf.range(ndims, 0, -1, dtype="float64")

optim_results = tfp.optimizer.lbfgs_minimize(
    lambda x: tfp.math.value_and_gradient(quadratic, x),
    initial_position=start,
    num_correction_pairs=10,
    tolerance=1e-8,
)

我们如何在 TensorFlow 2.0 中使用 lbfgs_minimize

How can we use lbfgs_minimize in TensorFlow 2.0

python

deep-learning

tensorflow

tensorflow2.0