def build_fclayer(self, layer, previous_layer, last_output, **kwargs):
# We need to reshape the last_output
# depending on what type of layer we had
if previous_layer['name'] in ['Pool', 'Convolution']:
os = previous_layer['output_shape']
last_output = last_output.reshape((
os[0],
os[1] * os[2] * os[3]
))
kwargs['n_in'] = os[1] * os[2] * os[3]
else:
# We can assume we have an fc layer
kwargs['n_in'] = previous_layer['output_shape'][1]
# With FCLayer we can just passing kwargs
entity = FCLayer(**kwargs)
layer['output_shape'] = entity.output_shape()
### Logging ###
self.logger.info("fclayer output shape")
self.logger.info(layer['output_shape'])
return (layer, entity, last_output)
print("####")
print((nkernels[1], nkernels[0], 4, 4))
print((batch_size, nkernels[0], 12, 12))
conv1 = Convolution.withoutFilters(filter_shape=(nkernels[1], nkernels[0], 4,
4),
image_shape=(batch_size, nkernels[0], 12,
12))
fm1 = conv1.get_output(pool_out0)
pool1 = Pool((2, 2))
pool_out1 = pool1.get_output(fm1)
pool_out1 = pool_out1.flatten(2)
print("FCLayer 0")
fc0 = FCLayer(n_in=320, n_out=500)
fc0_out = fc0.get_output(pool_out1)
print("Get softmax output")
soft0 = FCLayer(500, 62, activation=T.nnet.softmax)
output = soft0.get_output(fc0_out)
params = soft0.params + fc0.params + conv1.params + conv0.params
cost = T.mean(T.nnet.binary_crossentropy(y, output))
grads = T.grad(cost, params)
updates = [(param_i, param_i - .001 * grad_i)
for param_i, grad_i in zip(params, grads)]
print((nkernels[1],nkernels[0],4,4))
print((batch_size, nkernels[0], 12, 12))
conv1 = Convolution.withoutFilters(
filter_shape=(nkernels[1],nkernels[0],4,4),
image_shape=(batch_size, nkernels[0], 12, 12)
)
fm1 = conv1.get_output(pool_out0)
pool1 = Pool((2,2))
pool_out1 = pool1.get_output(fm1)
pool_out1 = pool_out1.flatten(2)
print("FCLayer 0")
fc0 = FCLayer(
n_in=320,
n_out=500
)
fc0_out = fc0.get_output(pool_out1)
print("Get softmax output")
soft0 = FCLayer(
500,
62,
activation=T.nnet.softmax
)
output = soft0.get_output(fc0_out)
params = soft0.params + fc0.params + conv1.params + conv0.params
cost = T.mean(T.nnet.binary_crossentropy(y, output))
