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small_network.py
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import theano
from theano import tensor as T
import numpy as np
from inputFormat import *
from layers import *
import cPickle
class network:
def __init__(self, batch_size = 1, rng = None, load_file = None):
if(not rng): rng = np.random.RandomState(None)
self.input = T.tensor4('input') #position matrix
self.batch_size = batch_size
layer0_D3 = 12
layer0_D5 = 20
layer0 = HexConvLayer(
rng,
self.input,
(batch_size, num_channels, input_size, input_size),
layer0_D5,
layer0_D3
)
layer1_D3 = 16
layer1_D5 = 16
layer1 = HexConvLayer(
rng,
layer0.output,
(batch_size, layer0_D3+layer0_D5, input_size, input_size),
layer1_D5,
layer1_D3
)
layer2_D3 = 20
layer2_D5 = 12
layer2 = HexConvLayer(
rng,
layer1.output,
(batch_size, layer1_D3+layer1_D5, input_size, input_size),
layer2_D5,
layer2_D3
)
layer3_D3 = 24
layer3_D5 = 8
layer3 = HexConvLayer(
rng,
layer2.output,
(batch_size, layer2_D3+layer2_D5, input_size, input_size),
layer3_D5,
layer3_D3
)
layer4_D3 = 28
layer4_D5 = 4
layer4 = HexConvLayer(
rng,
layer3.output,
(batch_size, layer3_D3+layer3_D5, input_size, input_size),
layer4_D5,
layer4_D3
)
layer5_D3 = 32
layer5_D5 = 0
layer5 = HexConvLayer(
rng,
layer4.output,
(batch_size, layer4_D3+layer4_D5, input_size, input_size),
layer5_D5,
layer5_D3
)
layer6 = SigmoidLayer(
rng,
input = layer5.output.flatten(2),
n_in = (layer5_D3+layer5_D5)*input_size*input_size,
n_out = boardsize*boardsize
)
self.output = 2*layer6.output-1
self.params = layer0.params + layer1.params + layer2.params +layer3.params + layer4.params + layer5.params + layer6.params
self.mem_size = layer1.mem_size + layer2.mem_size + layer3.mem_size + layer4.mem_size + layer5.mem_size + layer6.mem_size
class policy_network:
def __init__(self, batch_size = None, rng = None, load_file = None, params = None):
if(not rng): rng = np.random.RandomState(None)
self.input = T.tensor4('input') #position matrix
self.batch_size = batch_size
layer0_D3 = 12
layer0_D5 = 20
layer0 = HexConvLayer(
rng,
self.input,
(batch_size, num_channels, input_size, input_size),
layer0_D5,
layer0_D3
)
layer1_D3 = 16
layer1_D5 = 16
layer1 = HexConvLayer(
rng,
layer0.output,
(batch_size, layer0_D3+layer0_D5, input_size, input_size),
layer1_D5,
layer1_D3
)
layer2_D3 = 20
layer2_D5 = 12
layer2 = HexConvLayer(
rng,
layer1.output,
(batch_size, layer1_D3+layer1_D5, input_size, input_size),
layer2_D5,
layer2_D3
)
layer3_D3 = 24
layer3_D5 = 8
layer3 = HexConvLayer(
rng,
layer2.output,
(batch_size, layer2_D3+layer2_D5, input_size, input_size),
layer3_D5,
layer3_D3
)
layer4_D3 = 28
layer4_D5 = 4
layer4 = HexConvLayer(
rng,
layer3.output,
(batch_size, layer3_D3+layer3_D5, input_size, input_size),
layer4_D5,
layer4_D3
)
layer5_D3 = 32
layer5_D5 = 0
layer5 = HexConvLayer(
rng,
layer4.output,
(batch_size, layer4_D3+layer4_D5, input_size, input_size),
layer5_D5,
layer5_D3
)
layer6 = FullyConnectedLayer(
rng,
layer5.output.flatten(2),
(layer5_D3+layer5_D5)*input_size*input_size,
boardsize*boardsize,
params[30:32] if params else None
)
not_played = T.and_(T.eq(self.input[:, white, padding:boardsize+padding, padding:boardsize+padding].flatten(2),0),\
T.eq(self.input[:, black, padding:boardsize+padding, padding:boardsize+padding].flatten(2),0))
playable_output = T.nnet.softmax(layer6.output[not_played.nonzero()])
output = T.switch(not_played, layer6.output, -1*np.inf)
self.output = T.nnet.softmax(output)
self.params = layer0.params + layer1.params + layer2.params + layer3.params + layer4.params + layer5.params + layer6.params
self.mem_size = layer1.mem_size+layer2.mem_size+layer3.mem_size+layer4.mem_size+layer5.mem_size+layer6.mem_size