blorgblerg

def PReLU(_x, name):
    _alpha = tf.get_variable(name, shape=_x.get_shape()[-1],
                             dtype=_x.dtype, initializer=tf.constant_initializer(0.1))
    return tf.maximum(0.0, _x) + _alpha * tf.minimum(0.0, _x)

blorgblerg

def batch_normify(batch, depth=1):
    """Returns a normalized batch. 
    Inputs:
    -batch: a batch tensor
    -depth: the dimension of the axis you want to keep unnormalized"""
    with tf.variable_scope('bn'):
        beta = tf.Variable(tf.constant(0.0, shape=[depth]),
                                     name='beta', trainable=True)
        gamma = tf.Variable(tf.constant(1.0, shape=[depth]),
                                      name='gamma', trainable=True)

blorgblerg

import time

def timeme(some_function):
    """Place above function definition."""
    def time_function(*args,**kwargs):
        starttime = time.time()
        print('Timed: {} s. '.format(time.time() - starttime))
        return some_function(*args, **kwargs)
    return time_function

blorgblerg

import sys

# These are the usual ipython objects, including this one you are creating
ipython_vars = ['In', 'Out', 'exit', 'quit', 'get_ipython', 'ipython_vars']

# Get a sorted list of the objects and their sizes
sorted([(x, sys.getsizeof(globals().get(x))) for x in dir() if not x.startswith('_') and x not in sys.modules and x not in ipython_vars], key=lambda x: x[1], reverse=True)