بهره کارت اعتباری

TFP_Installation = "Stable TFP"

if TFP_Installation == "Most Recent TFP":
    !pip install -q tfp-nightly
    print("Most recent TFP version installed")
elif TFP_Installation == "Stable TFP":
    !pip install -q --upgrade tensorflow-probability
    print("Up-to-date, stable  TFP version installed")
elif TFP_Installation == "Stable TFP-GPU":
    !pip install -q --upgrade tensorflow-probability-gpu
    print("Up-to-date, stable TFP-GPU version installed")
    print("(make sure GPU is properly configured)")
elif TFP_Installation == "Most Recent TFP-GPU":
    !pip install -q tfp-nightly-gpu
    print("Most recent TFP-GPU version installed")
    print("(make sure GPU is properly configured)")
elif TFP_Installation == "TFP Already Installed":
    print("TFP already installed in this environment")
    pass
else:
    print("Installation Error: Please select a viable TFP installation option.")

from __future__ import absolute_import, division, print_function

warning_status = "ignore"
import warnings
warnings.filterwarnings(warning_status)
with warnings.catch_warnings():
    warnings.filterwarnings(warning_status, category=DeprecationWarning)
    warnings.filterwarnings(warning_status, category=UserWarning)

import numpy as np
import os
matplotlib_style = 'fivethirtyeight'
import matplotlib.pyplot as plt; plt.style.use(matplotlib_style)
import matplotlib.axes as axes;
from matplotlib.patches import Ellipse
%matplotlib inline
import seaborn as sns; sns.set_context('notebook')
notebook_screen_res = 'png'
%config InlineBackend.figure_format = notebook_screen_res

import tensorflow as tf

# Eager Execution
use_tf_eager = True


# Use try/except so we can easily re-execute the whole notebook.
if use_tf_eager:
  try:
    tf.compat.v1.enable_eager_execution()
  except:
    reset_session()


import tensorflow_probability as tfp
tfd = tfp.distributions
tfb = tfp.bijectors


def default_session_options(enable_gpu_ram_resizing=True,
                            enable_xla=False):
  """Creates default options for Graph-mode session."""
  config = tf.ConfigProto()
  config.log_device_placement = True
  if enable_gpu_ram_resizing:
    # `allow_growth=True` makes it possible to connect multiple
    # colabs to your GPU. Otherwise the colab malloc's all GPU ram.
    config.gpu_options.allow_growth = True
  if enable_xla:
    # Enable on XLA. https://www.tensorflow.org/performance/xla/.
    config.graph_options.optimizer_options.global_jit_level = (
        tf.OptimizerOptions.ON_1)
  return config


def reset_session(options=None):
  """Creates a new global, interactive session in Graph-mode."""
  if tf.executing_eagerly():
    return
  global sess
  try:
    tf.reset_default_graph()
    sess.close()
  except:
    pass
  if options is None:
    options = default_session_options()
  sess = tf.InteractiveSession(config=options)


def evaluate(tensors):
  """Evaluates Tensor or EagerTensor to Numpy `ndarray`s.
  Args:
  tensors: Object of `Tensor` or EagerTensor`s; can be `list`, `tuple`,
    `namedtuple` or combinations thereof.

  Returns:
    ndarrays: Object with same structure as `tensors` except with `Tensor` or
      `EagerTensor`s replaced by Numpy `ndarray`s.
  """
  if tf.executing_eagerly():
    return tf.contrib.framework.nest.pack_sequence_as(
        tensors,
        [t.numpy() if tf.contrib.framework.is_tensor(t) else t
         for t in tf.contrib.framework.nest.flatten(tensors)])
  return sess.run(tensors)


class _TFColor(object):
  """Enum of colors used in TF docs."""
  red = '#F15854'
  blue = '#5DA5DA'
  orange = '#FAA43A'
  green = '#60BD68'
  pink = '#F17CB0'
  brown = '#B2912F'
  purple = '#B276B2'
  yellow = '#DECF3F'
  gray = '#4D4D4D'
  def __getitem__(self, i):
    return [
        self.red,
        self.orange,
        self.green,
        self.blue,
        self.pink,
        self.brown,
        self.purple,
        self.yellow,
        self.gray,
    ][i % 9]
TFColor = _TFColor()