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import numpy as np
import theano
import theano.tensor as tensor

rng = np.random.RandomState(1)
srng = RandomStreams(rng.randint(1234))

def get_one_hot(inp, nb_samples, nb_class):
    m = tensor.zeros((nb_samples, nb_class))
    m = tensor.set_subtensor(m[tensor.arange(nb_samples), tensor.argmax(inp, -1)], 1)

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# Faster RCNN VGG Net 
"""
The VGG network is adapted from: https://github.com/Lasagne/Recipes/blob/master/modelzoo/vgg16.py
"""
import theano
import theano.tensor as tensor
from lasagne.layers import InputLayer, DenseLayer, NonlinearityLayer, DropoutLayer
from lasagne.layers import Pool2DLayer as PoolLayer, RoIPoolLayer
from lasagne.layers import Conv2DLayer as ConvLayer, get_output, get_all_params
from lasagne.init import Normal

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import theano
from theano import tensor as T
from theano.tensor.signal.pool import pool_2d
import numpy as np

def spp_pooling(data, out_dimension, data_shape):

	input_size = data_shape[2:]
	pooled_data_list = []
	for pool_dim in out_dimension:

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import numpy as np
import theano
import theano.tensor as T
import resource

def random_matrix_mult():
	mat1 = T.fmatrix('mat1')
	mat2 = T.fmatrix('mat2')
	col_index = np.arange(0, 150000, 5000)
	row_index = np.arange(10000)

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alabaster==0.7.7
appdirs==1.4.0
appnope==0.1.0
Babel==2.2.0
backports-abc==0.4
backports.ssl-match-hostname==3.5.0.1
certifi==2015.11.20.1
cv2==1.0
cycler==0.10.0
Cython==0.23.4

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name: "CIFAR10_full_deploy"
# N.B. input image must be in CIFAR-10 format
# as described at http://www.cs.toronto.edu/~kriz/cifar.html
layer {
  name: "data"
  type: "Input"
  top: "data"
  input_param { shape: { dim: 1 dim: 3 dim: 32 dim: 32 } }
}
layer {

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name: "CaffeNet"
layer {
  name: "data"
  type: "Input"
  top: "data"
  input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }
}
layer {
  name: "conv1"
  type: "Convolution"

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#!/usr/bin/python
import theano.tensor as T
import theano
from cgt.utils import Message
import time
import cgt

if __name__ == '__main__':	
	a = T.scalar(name='a', dtype='int64') # 
	b = T.scalar(name='b', dtype='int64')

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class Node():
	pass


nodes = []
for i in range( 100 ):
	n = Node()
	nodes.append( n )