network = dropout(network, 0.8)
softmax = fully_connected(network, nClass, activation='softmax')
sgd = tflearn.SGD(learning_rate=0.1, lr_decay=0.96, decay_step=1000)
top_k = tflearn.metrics.Top_k(3)
network = tflearn.regression(softmax, optimizer=sgd, metric=top_k,
loss='categorical_crossentropy', name='target')
# Training
model = tflearn.DNN(network, tensorboard_verbose=0)
model.fit({'input': X}, {'target': Y}, validation_set=0.1, n_epoch=n_epoch1,
snapshot_step=100, run_id='convnet_buflo'+str(iter))
# Save model
model.save(os.path.join(model_path, 'Conv_buflo_'+str(iter)+'.tflearn'))
prob_vector = model.predict(testX)
k_list = []
# confidence thresholds
confs = [0.1,0.3,0.5,0.7, 0.9]
# top k accuracy
k_list = [1, 2, 3]
for topK in k_list:
for conf in confs:
file = os.path.join(HOME, 'results',
'Conv_Buflo' + '_top_' + str(topK) + '_' + gb + str(
dim) + '_' + str(nClass) + '_' + str(dim) + '_conf_' + str(conf) + '.txt')
nn_metrics.getMetricsTopK(exp, prob_vector, testY, nClass, file, topK, conf, iter, 0, mon_instance * nClass)
model = tflearn.DNN(net, tensorboard_verbose=0)
model.fit(X, Y, batch_size=47, n_epoch=n_epoch1, validation_set=0.1)
# Save model
model.save(os.path.join(model_path, 'PFP_M_' + str(iter) + '.tflearn'))
prob_vector = model.predict(testX)
# print(prob_vector)
k_list = []
confs = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
if b_label: ## Binary classification
k_list = [1]
else:
k_list = [1, 2, 3, 4, 5]
for topK in k_list:
if not b_label:
file = os.path.join(
HOME, 'results', 'PFP_M_' + str(topK) + '_' + exp + '_' + gb +
str(dim) + '_' + str(mon_instance * (nClass - 1)) + '_' +
str(unClass) + '_' + str(n_epoch1) + '.txt')
for conf in confs:
nn_metrics.getMetricsTopK(exp, prob_vector, testY, nClass,
file, topK, conf, iter, 0, 0)
else:
file = os.path.join(
HOME, 'results', 'PFP_M_' + exp + '_' + gb + str(dim) + '_' +
str(mon_instance * (nClass - 1)) + '_' + str(unClass) + '_' +
str(n_epoch1) + '.txt')
for conf in confs:
nn_metrics.getMetricsTopK(exp, prob_vector, testY, nClass, file, topK,
conf, iter, 0, 0)