train_folds = range(i+1)
train_data = join_folds(folds,train_folds)
#mins, maxs = MaxMin(train_data['labels'])
#T_l = MaxMinFit(train_data['labels'], mins, maxs)
#t_l = MaxMinFit(test['labels'], mins, maxs)
T_l = train_data['labels']
t_l = test['labels']
Dense_sizes = [300]
Dense_l2_regularizers = [0.37173327555716984,0.000165584846072854]
Dense_acivity_l2_regularizers = [0.9593094177755246,0.0011426757779919388]
CNN_filters = 5
CNN_rows = 6
max_input_length = test['features'].shape[1]
is_trainable = True
opt = 'adadelta' #sgd, rmsprop, adagrad, adadelta, adam
model = create_CNN( CNN_filters, CNN_rows, Dense_sizes, Dense_l2_regularizers, Dense_acivity_l2_regularizers, emb, max_input_length, is_trainable,opt)
if(weights!=None):
model.set_weights(weights)
else:
weights = model.get_weights()
t = model.fit( train_data['features'], T_l, batch_size=64, nb_epoch=4500)
scores_on_train = model.evaluate(train_data['features'],T_l)
scores_on_test = model.evaluate(test['features'],t_l)
print('mse on train : ' + str(scores_on_train))
print('mse on test : ' + str(scores_on_test))
print('')
model = None
gc.collect()
ret.append([scores_on_train, scores_on_test])
pprint(ret)
Dense_acivity_l2_regularizers = [
0.01,
0.01
]
CNN_filters = [10]
CNN_rows = [2]
max_input_length = test['features'].shape[1]
is_trainable = False
opt = 'adadelta' #sgd, rmsprop, adagrad, adadelta, adam
model = create_CNN(
CNN_filters,
CNN_rows,
Dense_sizes,
Dense_l2_regularizers,
Dense_acivity_l2_regularizers,
emb,
max_input_length,
is_trainable,
opt
)
t = model.fit(
train_data['features'],
T_l,
batch_size=64,
nb_epoch=200 ,
validation_data=(
validation_data['features'],
t_l
)
