debug_mode=False)
else:
# cross_validation_indexes = list(cross_validation.KFold(len(train_mode_per_block)/10, n_folds=4,
# random_state=42, shuffle=True))
def flatten_repetitions(data_to_flatten):
return np.reshape(
np.reshape(data_to_flatten.T * 10,
(-1, 1)) + np.arange(10), (-1))
train_indexes = flatten_repetitions(
cross_validation_indexes[0])
test_indexes = flatten_repetitions(cross_validation_indexes[1])
data_generator_batch = simple_data_generator_no_dict(
all_data_per_char_as_matrix[train_indexes],
target_per_char_as_matrix[train_indexes],
shuffle_data=False)
validation_data_generator_batch = simple_data_generator_no_dict(
all_data_per_char_as_matrix[validation_indexes],
target_per_char_as_matrix[validation_indexes],
shuffle_data=False)
from keras.models import Sequential
from keras.layers import merge, Input, Dense, Flatten, Activation, Lambda, LSTM, noise
eeg_sample_shape = (25, 55)
only_p300_model_1 = get_only_P300_model_LSTM(eeg_sample_shape)
only_p300_model_1.summary()
use_p300net = False
select=select,
debug_mode=False)
else:
# cross_validation_indexes = list(cross_validation.ShuffleSplit(len(train_mode_per_block)/10, n_iter=1,
# test_size=.667, random_state=42))
#
# def flatten_repetitions(data_to_flatten):
# return np.reshape(np.reshape(data_to_flatten.T * 10, (-1, 1)) + np.arange(10), (-1))
#
# train_indexes = flatten_repetitions(cross_validation_indexes[0][0])
# test_indexes = flatten_repetitions(cross_validation_indexes[0][1])
train_indexes = train_mode_per_block == 1
test_indexes = train_mode_per_block != 1
data_generator_batch = simple_data_generator_no_dict(
all_data_per_char_as_matrix[train_indexes],
target_per_char_as_matrix[train_indexes])
from keras.models import Sequential
from keras.layers import merge, Input, Dense, Flatten, Activation, Lambda, LSTM, noise
eeg_sample_shape = (25, 55)
only_p300_model_1 = get_only_P300_model_LSTM(eeg_sample_shape)
use_p300net = False
if use_p300net:
model = get_P300_model(only_p300_model_1, select=select)
else:
only_p300_model_1.compile(
