def test_mlp_use_bias_pushed_when_not_explicitly_specified():
mlp = MLP(activations=[Tanh(), Tanh(), None],
dims=[4, 5, 6, 7],
prototype=Linear(use_bias=False),
use_bias=True)
mlp.push_allocation_config()
assert [lin.use_bias for lin in mlp.linear_transformations]
def test_mlp_use_bias_not_pushed_when_not_explicitly_specified():
mlp = MLP(activations=[Tanh(), Tanh(), None],
dims=[4, 5, 6, 7], prototype=Linear(use_bias=False))
mlp.push_allocation_config()
assert [not lin.use_bias for lin in mlp.linear_transformations]
def build_submodel(image_size,
num_channels,
L_dim_conv_layers,
L_filter_size,
L_pool_size,
L_activation_conv,
L_dim_full_layers,
L_activation_full,
dropout,
prediction,
allow_comment=False,
sub_dropout=0,
L_pool_step=[],
L_pool_padding=[]):
# CONVOLUTION
params_channels = [10**(-i) for i in range(len(L_dim_conv_layers) + 1)]
index_params = 0
params_channels.reverse()
output_dim = num_channels * np.prod(image_size)
conv_layers = []
assert len(L_dim_conv_layers) == len(L_filter_size)
assert len(L_dim_conv_layers) == len(L_pool_size)
assert len(L_dim_conv_layers) == len(L_activation_conv)
if len(L_pool_step) == 0:
L_pool_step = [(1, 1) for i in range(len(L_dim_conv_layers))]
L_pool_padding = [(0, 0) for i in range(len(L_dim_conv_layers))]
assert len(L_dim_conv_layers) == len(L_pool_step)
assert len(L_dim_conv_layers) == len(L_pool_padding)
L_conv_dropout = [dropout] * len(
L_dim_conv_layers) # unique value of dropout for now
convnet = None
mlp = None
if len(L_dim_conv_layers):
for (num_filters, filter_size, pool_size, activation_str, dropout,
index, step, padding) in zip(L_dim_conv_layers, L_filter_size,
L_pool_size, L_activation_conv,
L_conv_dropout,
xrange(len(L_dim_conv_layers)),
L_pool_step, L_pool_padding):
# convert filter_size and pool_size in tuple
filter_size = tuple(filter_size)
if pool_size is None:
pool_size = (0, 0)
else:
pool_size = tuple(pool_size)
# TO DO : leaky relu
if activation_str.lower() == 'rectifier':
activation = Rectifier()
elif activation_str.lower() == 'tanh':
activation = Tanh()
elif activation_str.lower() in ['sigmoid', 'logistic']:
activation = Logistic()
elif activation_str.lower() in ['id', 'identity']:
activation = Identity()
else:
raise Exception("unknown activation function : %s",
activation_str)
assert 0.0 <= dropout and dropout < 1.0
num_filters = num_filters - int(num_filters * dropout)
layer_conv = Convolutional(filter_size=filter_size,
num_filters=num_filters,
name="layer_%d" % index,
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0.0))
conv_layers.append(layer_conv)
conv_layers.append(activation)
index_params += 1
if not (pool_size[0] == 0 and pool_size[1] == 0):
#pool = MaxPooling(pooling_size=pool_size, step=step, padding=padding)
pool = MaxPooling(pooling_size=pool_size)
conv_layers.append(pool)
convnet = ConvolutionalSequence(conv_layers,
num_channels=num_channels,
image_size=image_size,
name="conv_section")
convnet.push_allocation_config()
convnet.initialize()
output_dim = np.prod(convnet.get_dim('output'))
# MLP
assert len(L_dim_full_layers) == len(L_activation_full)
L_full_dropout = [dropout] * len(
L_dim_full_layers) # unique value of dropout for now
# reguarding the batch dropout : the dropout is applied on the filter
# which is equivalent to the output dimension
# you have to look at the dropout_rate of the next layer
# that is why we throw away the first value of L_exo_dropout_full_layers
pre_dim = output_dim
if allow_comment:
print "When constructing the model, the output_dim of the conv section is %d." % output_dim
activations = []
dims = [pre_dim]
if len(L_dim_full_layers):
for (dim, activation_str, dropout, index) in zip(
L_dim_full_layers, L_activation_full, L_full_dropout,
range(len(L_dim_conv_layers),
len(L_dim_conv_layers) + len(L_dim_full_layers))):
# TO DO : leaky relu
if activation_str.lower() == 'rectifier':
activation = Rectifier().apply
elif activation_str.lower() == 'tanh':
activation = Tanh().apply
elif activation_str.lower() in ['sigmoid', 'logistic']:
activation = Logistic().apply
elif activation_str.lower() in ['id', 'identity']:
activation = Identity().apply
else:
raise Exception("unknown activation function : %s",
activation_str)
activations.append(activation)
assert 0.0 <= dropout and dropout < 1.0
dim = dim - int(dim * dropout)
if allow_comment:
print "When constructing the fully-connected section, we apply dropout %f to add an MLP going from pre_dim %d to dim %d." % (
dropout, pre_dim, dim)
dims.append(dim)
#now construct the full MLP in one pass:
activations.append(Identity())
#params_channels[index_params]
dims.append(prediction)
mlp = MLP(activations=activations,
dims=dims,
weights_init=IsotropicGaussian(0.1),
biases_init=Constant(0.0),
name="layer_%d" % index)
mlp.push_allocation_config()
mlp.initialize()
return (convnet, mlp)
