rh314

## Convert python dict to json dict
#!/usr/bin/env python3
'''
Run the script, then paste a "python-like" dictionary as input and press enter.
The script will output the json parseable version of the input.
'''
import json
import ast


def transform(obj):

## func_load_options.py
# Gist for discussion: https://github.com/fchollet/keras/pull/8592#discussion_r153287881


# OPTION 1 - replace list comprehension with for loop:

def func_load(code, defaults=None, closure=None, globs=None):
    """Deserializes a user defined function.

    # Arguments
        code: bytecode of the function.

## A_zmq-pytorch-segfault-2017-08-21.txt
See Dockerfile and run-stuff.sh

## variational_optimisation.py

# Python version of: https://gist.github.com/davidbarber/16708b9135f13c9599f754f4010a0284
# as per blog post: https://davidbarber.github.io/blog/2017/04/03/variational-optimisation/
# also see https://www.reddit.com/r/MachineLearning/comments/63dhfc/r_evolutionary_optimization_as_a_variational/

from __future__ import print_function

import matplotlib
matplotlib.use('Agg')
	#!/usr/bin/env python3
	'''
	Run the script, then paste a "python-like" dictionary as input and press enter.
	The script will output the json parseable version of the input.
	'''
	import json
	import ast


	def transform(obj):
	# Gist for discussion: https://github.com/fchollet/keras/pull/8592#discussion_r153287881


	# OPTION 1 - replace list comprehension with for loop:

	def func_load(code, defaults=None, closure=None, globs=None):
	"""Deserializes a user defined function.

	# Arguments
	code: bytecode of the function.

	# Python version of: https://gist.github.com/davidbarber/16708b9135f13c9599f754f4010a0284
	# as per blog post: https://davidbarber.github.io/blog/2017/04/03/variational-optimisation/
	# also see https://www.reddit.com/r/MachineLearning/comments/63dhfc/r_evolutionary_optimization_as_a_variational/

	from __future__ import print_function

	import matplotlib
	matplotlib.use('Agg')