【发布时间】：2020-12-28 03:36:09
【问题描述】：
在 MacOS 10.13.6 上使用 MCMC 的 Tensorflow 2.0 出错
控制台报错：
2020-12-27 22:06:48.253835: I tensorflow/core/platform/cpu_feature_guard.cc:145] This TensorFlow binary is optimized with Intel(R) MKL-DNN to use the following CPU instructions in performance critical operations: SSE4.1 SSE4.2 AVX
To enable them in non-MKL-DNN operations, rebuild TensorFlow with the appropriate compiler flags.
2020-12-27 22:06:48.254353: I tensorflow/core/common_runtime/process_util.cc:115] Creating new thread pool with default inter op setting: 4. Tune using inter_op_parallelism_threads for best performance.
objc[69111]: Class zmAppHelper is implemented in both /Library/ScriptingAdditions/zOLPluginInjection.osax/Contents/MacOS/zOLPluginInjection (0x1a48eaf4f0) and /Library/Application Support/Microsoft/ZoomOutlookPlugin/zOutlookPlugin64.bundle/Contents/MacOS/zOutlookPlugin64 (0x1a490e0518). One of the two will be used. Which one is undefined.
objc[69111]: class `ERCalendarEventEditorWindowController' not linked into application
Traceback (most recent call last):
File "dc7.py", line 131, in <module>
chains, kernel_results = run_chain(initial_state)
File "/Users/ram/opt/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py", line 457, in __call__
result = self._call(*args, **kwds)
File "/Users/ram/opt/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py", line 503, in _call
self._initialize(args, kwds, add_initializers_to=initializer_map)
File "/Users/ram/opt/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py", line 408, in _initialize
*args, **kwds))
File "/Users/ram/opt/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py", line 1848, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File "/Users/ram/opt/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py", line 2150, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "/Users/ram/opt/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py", line 2041, in _create_graph_function
capture_by_value=self._capture_by_value),
File "/Users/ram/opt/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/framework/func_graph.py", line 915, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "/Users/ram/opt/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py", line 358, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File "/Users/ram/opt/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/framework/func_graph.py", line 905, in wrapper
raise e.ag_error_metadata.to_exception(e)
TypeError: in converted code:
dc7.py:117 run_chain *
return tfp.mcmc.sample_chain(
**TypeError: sample_chain() got an unexpected keyword argument 'seed'**
版本
MacOS 10.13.6 High Sierra
tensorflow 2.0.0 mkl_py37hda344b4_0
tensorflow-base 2.0.0 mkl_py37h66b1bf0_0
tensorflow-estimator 2.0.0 pyh2649769_0
tensorflow-probability 0.8.0 py_0 conda-forge
jupyter_client 6.1.7 py_0
jupyter_core 4.7.0 py37hecd8cb5_0
jupyterlab_pygments 0.1.2 py_0
ipython 7.19.0 py37h01d92e1_0
ipython_genutils 0.2.0 pyhd3eb1b0_1
python 3.7.9 h26836e1_0
python-dateutil 2.8.1 py_0
python_abi 3.7 1_cp37m conda-forge
源代码：
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
from pprint import pprint
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
#import tensorflow as tf
#print(tf.__version__)
import tensorflow.compat.v2 as tf
tf.enable_v2_behavior()
import tensorflow_probability as tfp
sns.reset_defaults()
sns.set_context(context = 'talk', font_scale = 0.7)
plt.rcParams['image.cmap'] = 'viridis'
#%matplotlib inline
tfd = tfp.distributions
tfb = tfp.bijectors
#### ============================================
#@title Utils { display-mode: "form" }
def print_subclasses_from_module(module, base_class, maxwidth=80):
import functools, inspect, sys
subclasses = [name for name, obj in inspect.getmembers(module)
if inspect.isclass(obj) and issubclass(obj, base_class)]
def red(acc, x):
if not acc or len(acc[-1]) + len(x) + 2 > maxwidth:
acc.append(x)
else:
acc[-1] += ", " + x
return acc
print('\n'.join(functools.reduce(red, subclasses, [])))
# Generate some data
def f(x, w):
# Pad x with 1's so we can add bias via matmul
x = tf.pad(x, [[1, 0], [0, 0]], constant_values=1)
linop = tf.linalg.LinearOperatorFullMatrix(w[..., np.newaxis])
result = linop.matmul(x, adjoint=True)
return result[..., 0, :]
num_features = 2
num_examples = 50
noise_scale = .5
true_w = np.array([-1., 2., 3.])
xs = np.random.uniform(-1., 1., [num_features, num_examples])
ys = f(xs, true_w) + np.random.normal(0., noise_scale, size=num_examples)
# Visualize the data set
plt.scatter(*xs, c=ys, s=100, linewidths=0)
grid = np.meshgrid(*([np.linspace(-1, 1, 100)] * 2))
xs_grid = np.stack(grid, axis=0)
fs_grid = f(xs_grid.reshape([num_features, -1]), true_w)
fs_grid = np.reshape(fs_grid, [100, 100])
plt.colorbar()
plt.contour(xs_grid[0, ...], xs_grid[1, ...], fs_grid, 20, linewidths=1)
plt.show()
### Sampling the noise scale
# Define the joint_log_prob function, and our unnormalized posterior.
def joint_log_prob(w, sigma, x, y):
# Our model in maths is
# w ~ MVN([0, 0, 0], diag([1, 1, 1]))
# y_i ~ Normal(w @ x_i, noise_scale), i=1..N
rv_w = tfd.MultivariateNormalDiag(
loc=np.zeros(num_features + 1),
scale_diag=np.ones(num_features + 1))
rv_sigma = tfd.LogNormal(np.float64(1.), np.float64(5.))
rv_y = tfd.Normal(f(x, w), sigma[..., np.newaxis])
return (rv_w.log_prob(w) +
rv_sigma.log_prob(sigma) +
tf.reduce_sum(rv_y.log_prob(y), axis=-1))
# Create our unnormalized target density by currying x and y from the joint.
def unnormalized_posterior(w, sigma):
return joint_log_prob(w, sigma, xs, ys)
# Create an HMC TransitionKernel
hmc_kernel = tfp.mcmc.HamiltonianMonteCarlo(
target_log_prob_fn=unnormalized_posterior,
step_size=np.float64(.1),
num_leapfrog_steps=4)
# Create a TransformedTransitionKernl
transformed_kernel = tfp.mcmc.TransformedTransitionKernel(
inner_kernel=hmc_kernel,
bijector=[tfb.Identity(), # w
tfb.Invert(tfb.Softplus())]) # sigma
# Apply a simple step size adaptation during burnin
@tf.function
def run_chain(initial_state, num_results=1000, num_burnin_steps=500):
adaptive_kernel = tfp.mcmc.SimpleStepSizeAdaptation(
transformed_kernel,
num_adaptation_steps=int(.8 * num_burnin_steps),
target_accept_prob=np.float64(.75))
return tfp.mcmc.sample_chain(
num_results=num_results,
num_burnin_steps=num_burnin_steps,
current_state=initial_state,
kernel=adaptive_kernel,
seed=(0, 1),
trace_fn=lambda cs, kr: kr)
# Instead of a single set of initial w's, we create a batch of 8.
num_chains = 8
initial_state = [np.zeros([num_chains, num_features + 1]),
.54 * np.ones([num_chains], dtype=np.float64)]
chains, kernel_results = run_chain(initial_state)
r_hat = tfp.mcmc.potential_scale_reduction(chains)
print("Acceptance rate:", kernel_results.inner_results.inner_results.is_accepted.numpy().mean())
print("R-hat diagnostic (per w variable):", r_hat[0].numpy())
print("R-hat diagnostic (sigma):", r_hat[1].numpy())
w_chains, sigma_chains = chains
【问题讨论】：
标签： typeerror tensorflow2.0 macos-high-sierra mcmc