def dnn_net_simple(num_classes):
# defining hyperparameter
h_num_hidden = D([64, 128, 256, 512, 1024]) # number of hidden units for affine transform module
h_nonlin_name = D(['relu', 'tanh', 'elu']) # nonlinearity function names to choose from
h_opt_drop = D([0, 1]) # dropout optional hyperparameter; 0 is exclude, 1 is include
h_drop_keep_prob = D([0.25, 0.5, 0.75]) # dropout probability to choose from
h_opt_bn = D([0, 1]) # batch_norm optional hyperparameter
h_swap = D([0, 1]) # order of swapping for permutation
h_num_repeats = D([1, 2]) # 1 is appearing once, 2 is appearing twice
# defining search space topology
model = mo.siso_sequential([
flatten(),
mo.siso_repeat(lambda: mo.siso_sequential([
dense(h_num_hidden),
nonlinearity(h_nonlin_name),
mo.siso_permutation([
lambda: mo.siso_optional(lambda: dropout(h_drop_keep_prob), h_opt_drop),
lambda: mo.siso_optional(batch_normalization, h_opt_bn),
], h_swap)
]), h_num_repeats),
dense(D([num_classes]))
])
return model
def module(h_num_filters, h_kernel_size, h_swap, h_opt_drop, h_drop_rate,
h_num_repeats):
return mo.siso_repeat(
lambda: mo.siso_sequential([
conv2d(h_num_filters, h_kernel_size, D([1])),
mo.siso_permutation([relu, batch_normalization], h_swap),
mo.siso_optional(lambda: dropout(h_drop_rate), h_opt_drop)
]), h_num_repeats)
def dnn_net(num_classes):
h_nonlin_name = D(['relu', 'tanh', 'elu'])
h_opt_drop = D([0, 1])
return mo.siso_sequential([
flatten(),
mo.siso_repeat(
lambda: dnn_cell(D([64, 128, 256, 512, 1024]), h_nonlin_name,
h_opt_drop, D([0.25, 0.5, 0.75])), D([1, 2])),
dense(D([num_classes]))
])
def dnn_net(num_classes):
h_nonlin_name = D(['relu', 'relu6', 'crelu', 'elu', 'softplus'],
name='Mutatable')
h_swap = D([0, 1], name='Mutatable_sub')
h_opt_drop = D([0, 1], name='Mutatable_sub')
h_opt_bn = D([0, 1], name='Mutatable_sub')
return mo.siso_sequential([
mo.siso_repeat(
lambda: dnn_cell(D([64, 128, 256, 512, 1024]),
h_nonlin_name, h_swap, h_opt_drop, h_opt_bn,
D([0.25, 0.5, 0.75])), D([1, 2])),
affine_simplified(D([num_classes]))
])