viz.text(json.dumps(obj=vars(args), sort_keys=True, indent=4))
# build network
if args.dataset == 'sinusoid':
net_init, f = mlp(n_output=1,
n_hidden_layer=args.n_hidden_layer,
n_hidden_unit=args.n_hidden_unit,
bias_coef=args.bias_coef,
activation=args.activation,
norm=args.norm)
_, params = net_init(rng=random.PRNGKey(42), input_shape=(-1, 1))
elif args.dataset == 'omniglot':
net_init, f = conv_net(n_output=args.n_way,
n_conv_layer=args.n_hidden_layer,
n_filter=args.n_hidden_unit,
bias_coef=args.bias_coef,
activation='relu',
norm='None')
_, params = net_init(rng=random.PRNGKey(42), input_shape=(-1, 28, 28, 1))
elif args.dataset == 'circle':
net_init, f = mlp(n_output=args.n_way,
n_hidden_layer=args.n_hidden_layer,
n_hidden_unit=args.n_hidden_unit,
bias_coef=args.bias_coef,
activation=args.activation,
norm=args.norm)
_, params = net_init(rng=random.PRNGKey(42), input_shape=(-1, 2))
else:
raise ValueError
def test_model():
global image_number
correct = 0
while image_number > 0:
batch_x, batch_y = sess.run([images, labels])
batch_x = np.reshape(batch_x, [network.batch_size, network.input_size])
acc = sess.run([correct_pred], feed_dict={network.X: batch_x, network.Y: batch_y, keep_prob: 1})
image_number = image_number - network.batch_size
correct = correct + numpy.sum(acc)
print("Predicted %d out of %d; partial accuracy %.4f" % (correct, total_images - image_number, correct / (total_images - image_number)))
print(correct/total_images)
logits = network.conv_net(network.X, network.weights, network.biases, keep_prob)
prediction = tf.nn.softmax(logits)
loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=network.Y))
optimizer = tf.train.AdamOptimizer(learning_rate=network.learning_rate)
train_op = optimizer.minimize(loss=loss_op)
correct_pred = tf.equal(tf.argmax(prediction, 1), network.Y)
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
init = tf.global_variables_initializer()
saver = tf.train.Saver()
from params import * from network import conv_net, weights, biases from loadTable import convTable, poolTable from loadData import * np.set_printoptions(threshold=np.nan) os.environ["CUDA_VISIBLE_DEVICES"] = "3" TRAIN_MODE = False X = tf.placeholder(tf.float32, [None, 1, inWidth, inChannel]) Y = tf.placeholder(tf.float32, [None, num_classes]) logits = conv_net(X, weights, biases, convTable, poolTable, div) prediction = tf.nn.softmax(logits) loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits, labels=Y)) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) train_op = optimizer.minimize(loss_op) # Evaluate model correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # Initialize the variables (i.e. assign their default value) init = tf.global_variables_initializer() start_time = time.time() saver = tf.train.Saver()
def example():
# get face count of the group
face_count = get_face_count(group_similarity_src)
# select from database
print("\n\n\n############ Training Session ############")
datas = []
for cnn_build_time_range in cnn_build_time_range_set:
start_time = cnn_build_time_range[0]
end_time = cnn_build_time_range[1]
this_datas = get_data(start_time, end_time)
datas = datas + this_datas
results.clear()
# print import data result for banchmark
print("\n\n\n------ Original Result ------")
find_rate(datas, 'person_first_id')
# build automaton
root_c = build_model(datas)
# apply decision model
matched_face_table, unmatched_face_table, unmatched_validate_table, unmatched_face_objects = find_match(
root_c.next, face_count)
# convert data to vector for neuron network
unmatched_face_table, unmatched_validate_table, unmatched_face_objects = convert_to_vector(
matched_face_table, unmatched_face_table, unmatched_validate_table,
unmatched_face_objects)
# separate training and test data
division = int(unmatched_face_table.shape[0] * 0.7)
X = unmatched_face_table[:division]
y = unmatched_validate_table[:division]
X_test = unmatched_face_table[division:]
y_test = unmatched_validate_table[division:]
# build cnn, and save the model
model = conv_net(X, y, X_test, y_test)
# use the model to make prediction on the construct session
print("\n\n\n------ Unmatched Face (Corrected by CNN) ------")
cnn_result = make_prediction(model, unmatched_face_table,
unmatched_face_objects)
print("\n\n\n------ Overall Result (Corrected by CNN) ------")
all_result = list(results) + list(cnn_result)
find_rate(all_result, 'result_id')
print(predict_time_range_set)
for predict_time_range in predict_time_range_set:
print(predict_time_range)
# get data from different session for testing
print("\n\n\n############ Indipendent Testing Session ############")
start_time = predict_time_range[0]
end_time = predict_time_range[1]
datas = get_data(start_time, end_time)
results.clear()
# print import data result for banchmark
print("\n\n\n------ Original Result ------")
find_rate(datas, 'person_first_id')
# build automaton
root_c = build_model(datas)
# apply decision model
matched_face_table, unmatched_face_table, unmatched_validate_table, unmatched_face_objects = find_match(
root_c.next, face_count)
# convert data to vector for neuron network
unmatched_face_table, unmatched_validate_table, unmatched_face_objects = convert_to_vector(
matched_face_table, unmatched_face_table, unmatched_validate_table,
unmatched_face_objects)
# evaluate the model using data of the indipendent test session
score = model.evaluate(unmatched_face_table, unmatched_validate_table)
print("\nCross Session Accuracy: ", score[-1])
# use the model to make prediction on the indipendent test session
print("\n\n\n------ Unmatched Face ------")
find_rate(unmatched_face_objects, 'person_first_id')
print("\n\n\n------ Unmatched Face (Corrected by CNN) ------")
cnn_result = make_prediction(model, unmatched_face_table,
unmatched_face_objects)
print("\n\n\n------ Overall Result (Corrected by CNN) ------")
all_result = list(results) + list(cnn_result)
find_rate(all_result, 'result_id')
