def get_cnn_model(model_num, search_space):
space = cnn_space(search_space)
hparams = {'model_' + model_num: 'CNN',
'word_vectors_' + model_num: ('word2vec', True),
'delta_' + model_num: True,
'flex_' + model_num: (True, .15),
'filters_' + model_num: hp.quniform('filters_' + model_num, *space['filters_'], 1),
'kernel_size_' + model_num: hp.quniform('kernel_size_' + model_num, *space['kernel_size_'], 1),
'kernel_increment_' + model_num: hp.quniform('kernel_increment_' + model_num, *space['kernel_increment_'], 1),
'kernel_num_' + model_num: hp.quniform('kernel_num_' + model_num, *space['kernel_num_'], 1),
'dropout_' + model_num: hp.uniform('dropout_' + model_num, *space['dropout_']),
'batch_size_' + model_num: hp.quniform('batch_size_' + model_num, *space['batch_size_'], 1),
'activation_fn_' + model_num: hp.choice('activation_fn_' + model_num, space['activation_fn_'])}
if space['no_reg']:
hparams['regularizer_cnn_' + model_num] = hp.choice('regularizer_cnn_' + model_num, [
(None, 0.0),
('l2', hp.uniform('l2_strength_cnn_' + model_num, *space['l2_'])),
('l2_clip', hp.uniform('l2_clip_norm_' + model_num, *space['l2_clip_']))
])
else:
hparams['regularizer_cnn_' + model_num] = hp.choice('regularizer_cnn_' + model_num, [
('l2', hp.uniform('l2_strength_cnn_' + model_num, *space['l2_'])),
('l2_clip', hp.uniform('l2_clip_norm_' + model_num, *space['l2_clip_']))
])
if space['search_lr']:
hparams['learning_rate_' + model_num] = hp.lognormal('learning_rate_' + model_num, 0, 1) / 3000
else:
hparams['learning_rate_' + model_num] = .0003
def get_cnn_model(model_num, search_space):
space = cnn_space(search_space)
param_dist = {
'model_' + model_num:
'CNN',
'word_vectors_' + model_num: ('word2vec', True),
'delta_' + model_num:
random.choice(space['delta_']),
'flex_' + model_num:
(True,
random.random() * (space['flex_amt_'][1] - space['flex_amt_'][1]) +
space['flex_amt_'][0]),
'dropout_' + model_num:
random.random() * (space['dropout_'][1] - space['dropout_'][0]) +
space['dropout_'][0],
'activation_fn_' + model_num:
random.choice(space['activation_fn_']),
'learning_rate_' + model_num:
.0003,
'filters_' + model_num:
random.randint(*space['filters_']),
'kernel_size_' + model_num:
random.randint(*space['kernel_size_']),
'kernel_increment_' + model_num:
random.randint(*space['kernel_increment_']),
'kernel_num_' + model_num:
random.randint(*space['kernel_num_']),
'batch_size_' + model_num:
random.randint(*space['batch_size_'])
}
#regularize with option of no rig if no reg
if space['no_reg'] == True:
param_dist['regularizer_cnn_' + model_num] = random.choice([
(None, 0.0),
('l2', (random.random() * (space['l2_'][1] - space['l2_'][0]) +
space['l2_'][0])),
('l2_clip',
(random.random() * (space['l2_clip_'][1] - space['l2_clip_'][0]) +
space['l2_clip_'][0]))
])
else:
param_dist['regularizer_cnn_' + model_num] = random.choice([
('l2', (random.random() * (space['l2_'][1] - space['l2_'][0]) +
space['l2_'][0])),
('l2_clip',
(random.random() * (space['l2_clip_'][1] - space['l2_clip_'][0]) +
space['l2_clip_'][0]))
])
#overwrite previously set values to search over some parameters depending on options
if space['search_lr'] == True:
param_dist['learning_rate_' +
model_num] = (random.lognormvariate(0, 1)) / 3000
return param_dist
def get_cnn_model(model_num, search_space):
space = cnn_space(search_space)
hparams = {
'model_' + model_num:
'CNN',
'word_vectors_' + model_num: ('word2vec', True),
'delta_' + model_num:
True,
'flex_' + model_num: (True, .15),
'filters_' + model_num:
hp.quniform('filters_' + model_num, *space['filters_'], 1),
'kernel_size_' + model_num:
hp.quniform('kernel_size_' + model_num, *space['kernel_size_'], 1),
'kernel_increment_' + model_num:
hp.quniform('kernel_increment_' + model_num,
*space['kernel_increment_'], 1),
'kernel_num_' + model_num:
hp.quniform('kernel_num_' + model_num, *space['kernel_num_'], 1),
'dropout_' + model_num:
hp.uniform('dropout_' + model_num, *space['dropout_']),
'batch_size_' + model_num:
hp.quniform('batch_size_' + model_num, *space['batch_size_'], 1),
'activation_fn_' + model_num:
hp.choice('activation_fn_' + model_num, space['activation_fn_'])
}
if space['no_reg']:
hparams['regularizer_cnn_' + model_num] = hp.choice(
'regularizer_cnn_' + model_num,
[(None, 0.0),
('l2', hp.uniform('l2_strength_cnn_' + model_num, *space['l2_'])),
('l2_clip',
hp.uniform('l2_clip_norm_' + model_num, *space['l2_clip_']))])
else:
hparams['regularizer_cnn_' + model_num] = hp.choice(
'regularizer_cnn_' + model_num,
[('l2', hp.uniform('l2_strength_cnn_' + model_num, *space['l2_'])),
('l2_clip',
hp.uniform('l2_clip_norm_' + model_num, *space['l2_clip_']))])
if space['search_lr']:
hparams['learning_rate_' + model_num] = hp.lognormal(
'learning_rate_' + model_num, 0, 1) / 3000
else:
hparams['learning_rate_' + model_num] = .0003
def get_cnn_model(model_num, search_space):
space = cnn_space(search_space)
param_dist = {'model_' + model_num: 'CNN',
'word_vectors_' + model_num: ('word2vec', True),
'delta_' + model_num: random.choice(space['delta_']),
'flex_' + model_num: (True, random.random() * (space['flex_amt_'][1] -
space['flex_amt_'][1]) + space['flex_amt_'][0]),
'dropout_' + model_num: random.random() * (space['dropout_'][1] -
space['dropout_'][0]) + space['dropout_'][0],
'activation_fn_' + model_num: random.choice(space['activation_fn_']),
'learning_rate_' + model_num: .0003,
'filters_' + model_num: random.randint(*space['filters_']),
'kernel_size_' + model_num: random.randint(*space['kernel_size_']),
'kernel_increment_' + model_num: random.randint(*space['kernel_increment_']),
'kernel_num_' + model_num: random.randint(*space['kernel_num_']),
'batch_size_' + model_num: random.randint(*space['batch_size_'])
}
#regularize with option of no rig if no reg
if space['no_reg'] == True:
param_dist['regularizer_cnn_' + model_num] = random.choice([
(None, 0.0),
('l2', (random.random() * (space['l2_'][1] - space['l2_'][0]) + space['l2_'][0])),
('l2_clip', (random.random() * (space['l2_clip_'][1] - space['l2_clip_'][0]) + space['l2_clip_'][0]))
])
else:
param_dist['regularizer_cnn_' + model_num] = random.choice([
('l2', (random.random() * (space['l2_'][1] - space['l2_'][0]) + space['l2_'][0])),
('l2_clip', (random.random() * (space['l2_clip_'][1] - space['l2_clip_'][0]) + space['l2_clip_'][0]))
])
#overwrite previously set values to search over some parameters depending on options
if space['search_lr'] == True:
param_dist['learning_rate_' + model_num] = (random.lognormvariate(0, 1)) / 3000
return param_dist
