igul222

"""
debug tools for theano
Ishaan Gulrajani
"""

import numpy as np
import theano
from theano import gof

class DebugOp(gof.Op):

igul222

def Conv1D(name, input_dim, output_dim, filter_size, inputs, mask_type=None, he_init=False):
    """
    inputs.shape: (batch size, input_dim, 1, width)
    mask_type: None, 'a', 'b'
    output.shape: (batch size, output_dim, 1, width)
    """

    if mask_type is not None:
        mask = numpy.ones(
            (output_dim, input_dim, 1, filter_size), 

igul222

_baseslice = slice
class Slice(object):
    def __init__(self, _tensor):
        self.tensor = _tensor

    def __getitem__(self, slice_spec):
        if not isinstance(slice_spec, (list, tuple)):
            slice_spec = [slice_spec]
        indices = []

igul222

train summary	epoch:0	n_inputs:21888	cost:1.72562180904
dev summary	epoch:0	n_inputs:1152	cost:1.67490259806
train summary	epoch:1	n_inputs:21888	cost:1.49495576557
dev summary	epoch:1	n_inputs:1152	cost:1.57830005222
train summary	epoch:2	n_inputs:21888	cost:1.4162766306
dev summary	epoch:2	n_inputs:1152	cost:1.52844831679
train summary	epoch:3	n_inputs:21888	cost:1.36915802537
dev summary	epoch:3	n_inputs:1152	cost:1.50779215495
train summary	epoch:4	n_inputs:21888	cost:1.33636599535
dev summary	epoch:4	n_inputs:1152	cost:1.4916501575

igul222

Epoch 0	7132.9565351009s	train cost 1.6981946635	correct 1.6981946635	dev cost 1.6462526276	correct 1.6462526276
Epoch 1	7147.1999368668s	train cost 1.4829269145	correct 1.4829269145	dev cost 1.5761406715	correct 1.5761406715
Epoch 2	7138.0306470394s	train cost 1.4255136971	correct 1.4255136971	dev cost 1.5523425980	correct 1.5523425980
Epoch 3	7127.6019258499s	train cost 1.3985411839	correct 1.3985411839	dev cost 1.5382623590	correct 1.5382623590
Epoch 4	7125.7831079960s	train cost 1.3843282114	correct 1.3843282114	dev cost 1.5298263626	correct 1.5298263626
Epoch 5	7124.6437451839s	train cost 1.3768820110	correct 1.3768820110	dev cost 1.5249164810	correct 1.5249164810
Epoch 6	7126.9552760124s	train cost 1.3730651317	correct 1.3730651317	dev cost 1.5251269255	correct 1.5251269255
Epoch 7	7125.7269859314s	train cost 1.3717070357	correct 1.3717070357	dev cost 1.5228043980	correct 1.5228043980
Epoch 8	7125.5283191204s	train cost 1.3718353560	correct 1.3718353560	dev cost 1.5230966063	correct 1.5230966063
Epoch 9

igul222

# Author: Ishaan Gulrajani
# License: BSD 3-clause

import numpy
import theano
import theano.tensor as T

# log(0) = -infinity, but this leads to
# NaN errors in log_add and elsewhere,
# so we'll just use a large negative value.

igul222

# Balanced binary tree of height 2. Boring, I know...
node_count = 7
edges = [
	(0,1),
	(0,2),
	(1,3),
	(1,4),
	(2,5),
	(2,6)
]

igul222

class Node(object):
	def __init__(self, _children):
		self.memo = None
		self.children = _children

dest = Node([])
source = Node([
	dest,         # source -> dest
	Node([dest]), # source -> intermediate -> dest
	Node([Node([dest])]), # source -> a -> b -> dest

igul222

Moves =
  start: () ->
    {type: 'start'}

class Players

  constructor: (@game) ->
    @teams: [
        active: true
        players: []

igul222

class Game
  @XMax: 25
  @YMax: 15
  @DotsPerPlayer: 2
  @FunctionAnimationSpeed: 0.005 # graph units per ms
  @DotRadius: 1
  @TurnTime: 60000 # ms
  @ObstacleCount: 10
  @ObstacleRadiusMin: 0
  @ObstacleRadiusMax: 5