testing_data, testing_tags = get_all_triplet_combinations_testing(all_data_per_char_as_matrix,
target_per_char_as_matrix,
train_mode_per_block)
valid_data = triplet_data_collection(all_data_per_char_as_matrix[train_mode_per_block == 2],
target_per_char_as_matrix[train_mode_per_block == 2], 80)
total_number_of_char_in_training = all_data_per_char_as_matrix[train_mode_per_block == 1].shape[0]/10
# region Build the P300Net model
# model = get_graph_lstm(3, 10, 25,55)
# endregion
# region the P300Net identification model
P300IdentificationModel = get_item_lstm_subgraph(25, 55)
P300IdentificationModel.compile(loss='binary_crossentropy', class_mode="binary", optimizer='rmsprop')
# endregion
# region train the P300Net model
# model.fit_generator(data_generator, 2880, nb_epoch=10, validation_data=valid_data)
# model.fit_generator(data_generator, 80*5, nb_epoch=20, validation_data=valid_data)
# endregion
# all_train_data = dict()
train_p300_model(P300IdentificationModel, all_data_per_char_as_matrix[train_mode_per_block == 1],
target_per_char_as_matrix[train_mode_per_block == 1])
def run_experiment_test(base_dir):
data_base_dir = base_dir #= r'C:\Users\ORI\Documents\Thesis\dataset_all'
all_subjects = [
"RSVP_Color116msVPicr.mat", "RSVP_Color116msVPpia.mat",
"RSVP_Color116msVPfat.mat", "RSVP_Color116msVPgcb.mat",
"RSVP_Color116msVPgcc.mat", "RSVP_Color116msVPgcd.mat",
"RSVP_Color116msVPgcf.mat", "RSVP_Color116msVPgcg.mat",
"RSVP_Color116msVPgch.mat", "RSVP_Color116msVPiay.mat",
"RSVP_Color116msVPicn.mat"
]
for subject in all_subjects:
# subject = "RSVP_Color116msVPgcd.mat"
file_name = os.path.join(data_base_dir, subject)
all_data_per_char, target_per_char, train_mode_per_block, all_data_per_char_as_matrix, target_per_char_as_matrix = create_data_rep_training(
file_name, -200, 800, downsampe_params=8)
data_generator = triplet_data_generator(
all_data_per_char_as_matrix[train_mode_per_block == 1],
target_per_char_as_matrix[train_mode_per_block == 1], 80)
testing_data, testing_tags = get_all_triplet_combinations_testing(
all_data_per_char_as_matrix, target_per_char_as_matrix,
train_mode_per_block)
valid_data = triplet_data_collection(
all_data_per_char_as_matrix[train_mode_per_block == 2],
target_per_char_as_matrix[train_mode_per_block == 2], 80)
total_number_of_char_in_training = all_data_per_char_as_matrix[
train_mode_per_block == 1].shape[0] / 10
# region Build the P300Net model
model = get_graph_lstm(3, 10, 25, 55)
# endregion
# region the P300Net identification model
P300IdentificationModel = get_item_lstm_subgraph(25, 55)
P300IdentificationModel.compile(loss='binary_crossentropy',
class_mode="binary",
optimizer='rmsprop')
# endregion
# region train the P300Net model
# model.fit_generator(data_generator, 2880, nb_epoch=10, validation_data=valid_data)
model.fit_generator(data_generator,
80 * 40,
nb_epoch=2,
validation_data=valid_data)
# endregion
# all_train_data = dict()
train_p300_model(
P300IdentificationModel,
all_data_per_char_as_matrix[train_mode_per_block == 1],
target_per_char_as_matrix[train_mode_per_block == 1])
final_model = get_item_lstm_subgraph(25, 55)
final_model_original_weights = final_model.get_weights()
final_model.compile(loss='binary_crossentropy',
class_mode="binary",
optimizer='sgd')
final_model.set_weights(
list(model.nodes['item_latent'].layer.get_weights()))
all_prediction_P300Net = predict_p300_model(
final_model,
all_data_per_char_as_matrix[train_mode_per_block != 1])
all_prediction_normal = predict_p300_model(
P300IdentificationModel,
all_data_per_char_as_matrix[train_mode_per_block != 1])
all_prediction_normal = all_prediction_P300Net
plt.subplot(1, 4, 1)
# plt.imshow(all_prediction, interpolation='none')
plt.subplot(1, 4, 2)
x = T.dmatrix('x')
import theano
softmax_res_func = theano.function([x], T.nnet.softmax(x))
#
# plt.imshow(softmax_res_func(all_prediction), interpolation='none')
# plt.subplot(1, 4, 3)
# plt.imshow(softmax_res_func(np.mean(all_prediction.reshape((-1, 10, 30)), axis=1)).astype(np.int),
# interpolation='none')
plt.subplot(1, 4, 3)
test_tags = target_per_char_as_matrix[
train_mode_per_block !=
1] # np.array([target_per_char[x][train_mode_per_block != 1] for x in range(30)]).T
# plt.imshow(np.mean(all_res.reshape((-1, 10, 30)), axis=1), interpolation='none')
all_res = test_tags
# plt.imshow(softmax_res_func(all_prediction_normal.reshape((-1, 30))), interpolation='none')
actual_untrained = np.argmax(softmax_res_func(
np.mean(all_prediction_normal.reshape((-1, 10, 30)), axis=1)),
axis=1)
# actual = np.where(np.round(softmax_res_func(np.mean(all_prediction.reshape((-1, 10, 30)), axis=1))) == 1)[0];
gt = np.argmax(np.mean(all_res.reshape((-1, 10, 30)), axis=1), axis=1)
# np.intersect1d(actual, gt)
# accuracy = len(np.intersect1d(actual, gt)) / float(len(gt))
plt.subplot(1, 2, 1)
plt.imshow(
softmax_res_func(
np.mean(all_prediction_normal.reshape((-1, 10, 30)), axis=1)))
plt.subplot(1, 2, 2)
plt.imshow(np.mean(all_res.reshape((-1, 10, 30)), axis=1))
plt.show()
accuracy = 0
accuracy_untrained = np.sum(actual_untrained == gt) / float(len(gt))
print "subject:{0} accu:{1} acc_untrained{2}".format(
subject, accuracy, accuracy_untrained)
optimizer='rmsprop')
# endregion
next_batch = data_generator.next()
temp = model.evaluate(next_batch[0],
next_batch[1],
batch_size=len(next_batch[0]))
# region train the P300Net model
# model.fit_generator(data_generator, 2880, nb_epoch=10, validation_data=valid_data)
model.fit_generator(data_generator, 80 * 5, nb_epoch=20)
# endregion
# all_train_data = dict()
# train_p300_model(P300IdentificationModel, all_data_per_char_as_matrix[train_mode_per_block == 1],
# target_per_char_as_matrix[train_mode_per_block == 1])
final_model = get_item_lstm_subgraph(25, 55)
final_model_original_weights = final_model.get_weights()
final_model.compile(loss='binary_crossentropy',
class_mode="binary",
optimizer='sgd')
final_model.set_weights(
list(model.nodes['item_latent'].layer.get_weights()))
all_prediction_P300Net = predict_p300_model(
final_model,
all_data_per_char_as_matrix[train_mode_per_block != 1])
all_prediction_normal = predict_p300_model(
P300IdentificationModel,
all_data_per_char_as_matrix[train_mode_per_block != 1])
# all_prediction_normal = all_prediction_P300Net