def test_prec_recall():
r1 = ["R", "F", "R", "F", "F", "F", "F", "F", "R", "R"]
r2 = ["F", "R", "F", "F", "R", "R", "R", "F", "F", "F"]
precision, recall = utils.precision_recall(r1)
interpolated_p = utils.interpolate_p(precision)
print("precision = {0}".format(precision))
print("recall = {0}".format(recall))
print("interpolated_p = {0}".format(interpolated_p))
precision, recall = utils.precision_recall(r2)
interpolated_p = utils.interpolate_p(precision)
print("precision = {0}".format(precision))
print("recall = {0}".format(recall))
print("interpolated_p = {0}".format(interpolated_p))
def show_result(sess):
# print ("Creating ply files...")
# bs = 0
# trData, trLabel = [], []
# batch_arr = []
# for item in glob.glob(directory + "/*.ply"):
# os.remove(item)
# batch_arr = []
# name_arr = []
# counter = 0
# for item in glob.glob(test_directory + '*.npy'):
# name_arr.append(str(item[12:]))
# loaded_file = np.load(item)
# batch_arr.append(utils.npy_cutter(loaded_file, scene_shape))
# counter += 1
# batch_arr = np.reshape( batch_arr, ( -1, scene_shape[0], scene_shape[1], scene_shape[2] ))
# trData = batch_arr[ :, 0:scene_shape[0], 0:scene_shape[1], 0:halfed_scene_shape ] # input
# trLabel = batch_arr[ :, 0:scene_shape[0], 0:scene_shape[1], halfed_scene_shape:scene_shape[2] ] # gt
# trData = np.reshape(trData, (-1, scene_shape[0] * scene_shape[1] * halfed_scene_shape))
# score = sess.run(ConvNet_class.generator, feed_dict={x: trData, keepProb: 1.0, phase: False})
# score = np.reshape(score, (counter, scene_shape[0], scene_shape[1], halfed_scene_shape, classes_count))
# score = np.argmax(score, 4)
# trData = np.reshape(trData, (-1, scene_shape[0], scene_shape[1], halfed_scene_shape))
# for i in range(counter):
# trData_i = trData[i,:,:,:]
# trData_i = np.reshape( trData_i, (scene_shape[0], scene_shape[1], halfed_scene_shape))
# score_i = score[i,:,:,:]
# score_i = np.reshape( score_i, (scene_shape[0], scene_shape[1], halfed_scene_shape))
# empty_scene = np.zeros((84,44,42))
# empty_space = np.zeros((scene_shape[0], scene_shape[1], 50))
# empty_scene = np.concatenate((trData_i, empty_scene), axis=2)
# empty_scene = np.concatenate((empty_scene, empty_space), axis=2)
# gen_scn = np.concatenate((trData_i, score_i), axis=2)
# gen_scn = np.concatenate((empty_scene, gen_scn), axis=2)
# empty_space = np.zeros((scene_shape[0], scene_shape[1], 50))
# gen_scn = np.concatenate((gen_scn, empty_space), axis=2)
# gen_scn = np.concatenate((gen_scn, batch_arr[i,:,:,:]), axis=2)
# output = open( directory + "/" + name_arr[i] + ".ply" , 'w')
# ply = ""
# numOfVrtc = 0
# for idx1 in range(gen_scn.shape[0]):
# for idx2 in range(gen_scn.shape[1]):
# for idx3 in range(gen_scn.shape[2]):
# if gen_scn[idx1][idx2][idx3] > 0:
# ply = ply + str(idx1)+ " " +str(idx2)+ " " +str(idx3) + str(utils.colors[ int(gen_scn[idx1][idx2][idx3]) ]) + "\n"
# numOfVrtc += 1
# output.write("ply" + "\n")
# output.write("format ascii 1.0" + "\n")
# output.write("comment VCGLIB generated" + "\n")
# output.write("element vertex " + str(numOfVrtc) + "\n")
# output.write("property float x" + "\n")
# output.write("property float y" + "\n")
# output.write("property float z" + "\n")
# output.write("property uchar red" + "\n")
# output.write("property uchar green" + "\n")
# output.write("property uchar blue" + "\n")
# output.write("property uchar alpha" + "\n")
# output.write("element face 0" + "\n")
# output.write("property list uchar int vertex_indices"+ "\n")
# output.write("end_header" + "\n")
# output.write( ply )
# output.close()
# print (test_data[i][12:] + ".ply" + " is Done!")
# batch_arr = []
# name_arr = []
# counter = 0
# ###################################################################
print("Creating ply files...")
bs = 0
trData, trLabel = [], []
batch_arr = []
precision = np.zeros(classes_count)
recall = np.zeros(classes_count)
accu1_all, accu2_all = 0.0, 0.0
for counter in range(num_of_vis_batch):
trData, trLabel = [], []
batch_arr = []
batch_arr_2d = []
bs = 0
test_data = utils.fetch_random_batch(train_directory, batch_size)
for test in test_data:
loaded_file = np.load(test)
batch_arr.append(utils.npy_cutter(loaded_file, scene_shape))
bs += 1
batch_arr = np.reshape(
batch_arr, (bs, scene_shape[0], scene_shape[1], scene_shape[2]))
trData = batch_arr[:, 0:scene_shape[0], 0:scene_shape[1],
0:halfed_scene_shape] # input
trLabel = batch_arr[:, 0:scene_shape[0], 0:scene_shape[1],
halfed_scene_shape:scene_shape[2]] # gt
trData = np.reshape(
trData, (-1, scene_shape[0] * scene_shape[1] * halfed_scene_shape))
score = sess.run(ConvNet_class.generator,
feed_dict={
x: trData,
keepProb: 1.0,
phase: False
})
score = np.reshape(score, (-1, scene_shape[0], scene_shape[1],
halfed_scene_shape, classes_count))
score = np.argmax(score, 4)
score = np.reshape(
score, (-1, scene_shape[0], scene_shape[1], halfed_scene_shape))
pre, rec = utils.precision_recall(score, trLabel, batch_size,
classes_count)
precision += pre
recall += rec
accu1, accu2 = accuFun(sess, trData, trLabel, bs)
accu1_all += accu1
accu2_all += accu2
logging.info("A1: %g, A2: %g" % (accu1, accu2))
print("A1: %g, A2: %g" % (accu1, accu2))
print precision / num_of_vis_batch * 1.0
print recall / num_of_vis_batch * 1.0
print accu1_all / num_of_vis_batch * 1.0
print accu2_all / num_of_vis_batch * 1.0
def eval(self,
query,
retrieval,
similarity_matrix,
query_modal,
retrieval_modal,
dis_metric,
radius=None):
saver = tf.train.Saver(var_list=self.image_encoder_vars +
self.text_encoder_vars)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
saver.restore(sess, 'saved_model/model.ckpt')
if query_modal == 'img':
query_latent = sess.run(self.image_latent,
feed_dict={
self.image_ph: query,
self.keep_prob_ph: 1.0,
self.training_ph: False
})
else:
query_latent = sess.run(self.text_latent,
feed_dict={
self.text_ph: query,
self.keep_prob_ph: 1.0,
self.training_ph: False
})
if retrieval_modal == 'img':
retrieval_latent = sess.run(self.image_latent,
feed_dict={
self.image_ph: retrieval,
self.keep_prob_ph: 1.0,
self.training_ph: False
})
else:
retrieval_latent = sess.run(self.text_latent,
feed_dict={
self.text_ph: retrieval,
self.keep_prob_ph: 1.0,
self.training_ph: False
})
MAP = optimized_mAP(query_latent,
retrieval_latent,
similarity_matrix,
dis_metric=dis_metric)
top_k_precision = precision_top_k(query_latent, retrieval_latent,
similarity_matrix,
[10, 20, 50, 100, 500], dis_metric)
if dis_metric == 'hash':
MAP_comp = optimized_mAP(query_latent,
retrieval_latent,
similarity_matrix,
dis_metric='cosine')
top_k_precision_comp = precision_top_k(query_latent,
retrieval_latent,
similarity_matrix,
[10, 20, 50, 100, 500],
dis_metric='cosine')
precision, recall = precision_recall(query_latent,
retrieval_latent,
similarity_matrix)
return MAP, top_k_precision, MAP_comp, top_k_precision_comp, precision, recall
return MAP, top_k_precision
def main():
ll_data_2g = utils.gongcan_to_ll()
train_data = utils.ll_to_grid(ll_data_2g)
# print(train_data)
# 删除原有的ID，不作为训练特征
for i in range(1, 8):
train_data.drop(['RNCID_' + str(i)], axis=1, inplace=True)
train_data.drop(['CellID_' + str(i)], axis=1, inplace=True)
# 将空余的信号强度，用0补填补
train_data = train_data.fillna(0)
# features和labels
X = train_data.drop(
['MRTime', 'Longitude', 'Latitude', 'Num_connected', 'grid_num'],
axis=1,
inplace=False).as_matrix()
y = train_data[['grid_num', 'Longitude', 'Latitude']].as_matrix()
# 通过设置每一次的随机数种子，保证不同分类器每一次的数据集是一样的
random_states = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20]
errors_all = []
top10_pres_all = []
top10_recalls_all = []
top10_fs_all = []
overall_pres_all = []
# 高斯朴素贝叶斯分类器
start = datetime.datetime.now()
errors = []
overall_pres = []
top10_pres = []
top10_recalls = []
top10_fs = []
for i in range(10):
# 切分训练集和验证集
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=random_states[i])
gnb = GaussianNB()
y_pred = gnb.fit(np.delete(X_train, 0, axis=1),
y_train[:, 0]).predict(np.delete(X_test, 0, axis=1))
overall_pre, top10_pre, top10_recall, top10_f = utils.precision_recall(
y_test[:, 0], y_pred)
overall_pres.append(overall_pre)
top10_pres.append(top10_pre)
top10_recalls.append(top10_recall)
top10_fs.append(top10_f)
errors.append(utils.pos_error(y_test, y_pred))
print("Gaussian")
print("Overall precision: %.3f" % np.mean(np.array(overall_pres)))
print("Top10 precision: %.3f" % np.array(top10_pres).mean(axis=0).mean())
print("Top10 recall: %.3f" % np.array(top10_recalls).mean(axis=0).mean())
print("Top10 f-measurement: %.3f" % np.array(top10_fs).mean(axis=0).mean())
print("Median error: {}".format(
np.percentile(np.array(errors).mean(axis=0), 50)))
print("Time spend: {}".format(datetime.datetime.now() - start))
errors_all.append(errors)
top10_recalls_all.append(np.array(top10_recalls).mean(axis=0).mean())
top10_pres_all.append(np.array(top10_pres).mean(axis=0).mean())
overall_pres_all.append(np.mean(np.array(overall_pres)))
top10_fs_all.append(np.array(top10_fs).mean(axis=0).mean())
print("****************************")
# K近邻分类器
start = datetime.datetime.now()
errors = []
overall_pres = []
top10_pres = []
top10_recalls = []
top10_fs = []
for i in range(10):
# 切分训练集和验证集
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=random_states[i])
neigh = KNeighborsClassifier()
y_pred = neigh.fit(np.delete(X_train, 0, axis=1),
y_train[:, 0]).predict(np.delete(X_test, 0, axis=1))
overall_pre, top10_pre, top10_recall, top10_f = utils.precision_recall(
y_test[:, 0], y_pred)
overall_pres.append(overall_pre)
top10_pres.append(top10_pre)
top10_recalls.append(top10_recall)
top10_fs.append(top10_f)
errors.append(utils.pos_error(y_test, y_pred))
print("KNeighbors")
print("Overall precision: %.3f" % np.mean(np.array(overall_pres)))
print("Top10 precision: %.3f" % np.array(top10_pres).mean(axis=0).mean())
print("Top10 recall: %.3f" % np.array(top10_recalls).mean(axis=0).mean())
print("Top10 f-measurement: %.3f" % np.array(top10_fs).mean(axis=0).mean())
print("Median error: {}".format(
np.percentile(np.array(errors).mean(axis=0), 50)))
print("Time spend: {}".format(datetime.datetime.now() - start))
errors_all.append(errors)
top10_recalls_all.append(np.array(top10_recalls).mean(axis=0).mean())
top10_pres_all.append(np.array(top10_pres).mean(axis=0).mean())
overall_pres_all.append(np.mean(np.array(overall_pres)))
top10_fs_all.append(np.array(top10_fs).mean(axis=0).mean())
print("****************************")
# 决策树分类器
start = datetime.datetime.now()
errors = []
overall_pres = []
top10_pres = []
top10_recalls = []
top10_fs = []
for i in range(10):
# 切分训练集和验证集
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=random_states[i])
clf = DecisionTreeClassifier()
y_pred = clf.fit(np.delete(X_train, 0, axis=1),
y_train[:, 0]).predict(np.delete(X_test, 0, axis=1))
overall_pre, top10_pre, top10_recall, top10_f = utils.precision_recall(
y_test[:, 0], y_pred)
overall_pres.append(overall_pre)
top10_pres.append(top10_pre)
top10_recalls.append(top10_recall)
top10_fs.append(top10_f)
errors.append(utils.pos_error(y_test, y_pred))
print("DecisionTree")
print("Overall precision: %.3f" % np.mean(np.array(overall_pres)))
print("Top10 precision: %.3f" % np.array(top10_pres).mean(axis=0).mean())
print("Top10 recall: %.3f" % np.array(top10_recalls).mean(axis=0).mean())
print("Top10 f-measurement: %.3f" % np.array(top10_fs).mean(axis=0).mean())
print("Median error: {}".format(
np.percentile(np.array(errors).mean(axis=0), 50)))
print("Time spend: {}".format(datetime.datetime.now() - start))
errors_all.append(errors)
top10_recalls_all.append(np.array(top10_recalls).mean(axis=0).mean())
top10_pres_all.append(np.array(top10_pres).mean(axis=0).mean())
overall_pres_all.append(np.mean(np.array(overall_pres)))
top10_fs_all.append(np.array(top10_fs).mean(axis=0).mean())
print("****************************")
# 随机森林
start = datetime.datetime.now()
errors = []
overall_pres = []
top10_pres = []
top10_recalls = []
top10_fs = []
for i in range(10):
# 切分训练集和验证集
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=random_states[i])
clf = RandomForestClassifier(max_depth=20, random_state=0)
y_pred = clf.fit(np.delete(X_train, 0, axis=1),
y_train[:, 0]).predict(np.delete(X_test, 0, axis=1))
overall_pre, top10_pre, top10_recall, top10_f = utils.precision_recall(
y_test[:, 0], y_pred)
overall_pres.append(overall_pre)
top10_pres.append(top10_pre)
top10_recalls.append(top10_recall)
top10_fs.append(top10_f)
errors.append(utils.pos_error(y_test, y_pred))
print("RandomForest")
print("Overall precision: %.3f" % np.mean(np.array(overall_pres)))
print("Top10 precision: %.3f" % np.array(top10_pres).mean(axis=0).mean())
print("Top10 recall: %.3f" % np.array(top10_recalls).mean(axis=0).mean())
print("Top10 f-measurement: %.3f" % np.array(top10_fs).mean(axis=0).mean())
print("Median error: {}".format(
np.percentile(np.array(errors).mean(axis=0), 50)))
print("Time spend: {}".format(datetime.datetime.now() - start))
errors_all.append(errors)
top10_recalls_all.append(np.array(top10_recalls).mean(axis=0).mean())
top10_pres_all.append(np.array(top10_pres).mean(axis=0).mean())
overall_pres_all.append(np.mean(np.array(overall_pres)))
top10_fs_all.append(np.array(top10_fs).mean(axis=0).mean())
print("****************************")
# AdaBoost
start = datetime.datetime.now()
errors = []
overall_pres = []
top10_pres = []
top10_recalls = []
top10_fs = []
for i in range(10):
# 切分训练集和验证集
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=random_states[i])
clf = AdaBoostClassifier(
base_estimator=DecisionTreeClassifier(max_depth=20),
learning_rate=0.01,
n_estimators=30,
algorithm='SAMME.R')
y_pred = clf.fit(np.delete(X_train, 0, axis=1),
y_train[:, 0]).predict(np.delete(X_test, 0, axis=1))
overall_pre, top10_pre, top10_recall, top10_f = utils.precision_recall(
y_test[:, 0], y_pred)
overall_pres.append(overall_pre)
top10_pres.append(top10_pre)
top10_recalls.append(top10_recall)
top10_fs.append(top10_f)
errors.append(utils.pos_error(y_test, y_pred))
print("AdaBoost")
print("Overall precision: %.3f" % np.mean(np.array(overall_pres)))
print("Top10 precision: %.3f" % np.array(top10_pres).mean(axis=0).mean())
print("Top10 recall: %.3f" % np.array(top10_recalls).mean(axis=0).mean())
print("Top10 f-measurement: %.3f" % np.array(top10_fs).mean(axis=0).mean())
print("Median error: {}".format(
np.percentile(np.array(errors).mean(axis=0), 50)))
print("Time spend: {}".format(datetime.datetime.now() - start))
errors_all.append(errors)
top10_recalls_all.append(np.array(top10_recalls).mean(axis=0).mean())
top10_pres_all.append(np.array(top10_pres).mean(axis=0).mean())
overall_pres_all.append(np.mean(np.array(overall_pres)))
top10_fs_all.append(np.array(top10_fs).mean(axis=0).mean())
print("****************************")
# Bagging
start = datetime.datetime.now()
errors = []
overall_pres = []
top10_pres = []
top10_recalls = []
top10_fs = []
for i in range(10):
# 切分训练集和验证集
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=random_states[i])
clf = BaggingClassifier(n_estimators=20)
y_pred = clf.fit(np.delete(X_train, 0, axis=1),
y_train[:, 0]).predict(np.delete(X_test, 0, axis=1))
overall_pre, top10_pre, top10_recall, top10_f = utils.precision_recall(
y_test[:, 0], y_pred)
overall_pres.append(overall_pre)
top10_pres.append(top10_pre)
top10_recalls.append(top10_recall)
top10_fs.append(top10_f)
errors.append(utils.pos_error(y_test, y_pred))
print("Bagging")
print("Overall precision: %.3f" % np.mean(np.array(overall_pres)))
print("Top10 precision: %.3f" % np.array(top10_pres).mean(axis=0).mean())
print("Top10 recall: %.3f" % np.array(top10_recalls).mean(axis=0).mean())
print("Top10 f-measurement: %.3f" % np.array(top10_fs).mean(axis=0).mean())
print("Median error: {}".format(
np.percentile(np.array(errors).mean(axis=0), 50)))
print("Time spend: {}".format(datetime.datetime.now() - start))
errors_all.append(errors)
top10_recalls_all.append(np.array(top10_recalls).mean(axis=0).mean())
top10_pres_all.append(np.array(top10_pres).mean(axis=0).mean())
overall_pres_all.append(np.mean(np.array(overall_pres)))
top10_fs_all.append(np.array(top10_fs).mean(axis=0).mean())
print("****************************")
# GradientBoosting
start = datetime.datetime.now()
errors = []
overall_pres = []
top10_pres = []
top10_recalls = []
top10_fs = []
for i in range(10):
print(i)
# 切分训练集和验证集
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=random_states[i])
clf = GradientBoostingClassifier(n_estimators=60, learning_rate=0.01)
y_pred = clf.fit(np.delete(X_train, 0, axis=1),
y_train[:, 0]).predict(np.delete(X_test, 0, axis=1))
overall_pre, top10_pre, top10_recall, top10_f = utils.precision_recall(
y_test[:, 0], y_pred)
overall_pres.append(overall_pre)
top10_pres.append(top10_pre)
top10_recalls.append(top10_recall)
top10_fs.append(top10_f)
errors.append(utils.pos_error(y_test, y_pred))
print("GradientBoosting")
print("Overall precision: %.3f" % np.mean(np.array(overall_pres)))
print("Top10 precision: %.3f" % np.array(top10_pres).mean(axis=0).mean())
print("Top10 recall: %.3f" % np.array(top10_recalls).mean(axis=0).mean())
print("Top10 f-measurement: %.3f" % np.array(top10_fs).mean(axis=0).mean())
print("Median error: {}".format(
np.percentile(np.array(errors).mean(axis=0), 50)))
print("Time spend: {}".format(datetime.datetime.now() - start))
errors_all.append(errors)
top10_recalls_all.append(np.array(top10_recalls).mean(axis=0).mean())
top10_pres_all.append(np.array(top10_pres).mean(axis=0).mean())
overall_pres_all.append(np.mean(np.array(overall_pres)))
top10_fs_all.append(np.array(top10_fs).mean(axis=0).mean())
print("****************************")
utils.cdf_figure(errors_all)
utils.figure(overall_pres_all, top10_pres_all, top10_recalls_all,
top10_fs_all)
model.load_weights(options.model_weights)
opt = keras.optimizers.Adam(float(options.lr))
model.compile(loss='binary_crossentropy',
optimizer=opt,
metrics=['accuracy'])
raise KeyboardInterrupt
print 'Loss and Validations history stored in ', options.outputFile + '_' + curr_time
#model.save_weights('../Output/ModelParams/LSTM_params_BiDi_%s.h5'%curr_time)
print 'Best Model parameters stored at ../Output/ModelParams/LSTM_BiDi_%s.h5' % curr_time
mfalseLength, mactualLength = utils.analyze_false(validData,
validDataNumbers,
validLabels, model)
c1p, c1r, c0p, c0r, acc, c1f, c0f = utils.precision_recall(
validDataNumbers,
validLabels,
model,
weightsPath=options.outputWeights)
print 'Run %d results :-' % (run + 1)
scores['c1_precision'].append(c1p)
scores['c1_recall'].append(c1r)
scores['c0_precision'].append(c0p)
scores['c0_recall'].append(c0r)
scores['accuracy'].append(acc)
scores['c1_fscore'].append(c1f)
scores['c0_fscore'].append(c0f)
scores['mean_actual_length'].append(mactualLength)
scores['mean_false_length'].append(mfalseLength)
scores['sample_info'].append(
(len(trainData), pos_train_samples, len(validData), pos_valid_samples))
curr_time = datetime.datetime.strftime(datetime.datetime.now(), '%dth-%H:%M:%S')
with open(options.outputFile+'_'+curr_time + '.pkl','w') as f:
json.dump(Hist.history,f)
elif os.path.exists(options.model_weights):
model.load_weights(options.model_weights)
opt = keras.optimizers.Adam(float(options.lr))
model.compile(loss = 'binary_crossentropy',optimizer = opt,metrics = ['accuracy'] )
raise KeyboardInterrupt
print 'Loss and Validations history stored in ',options.outputFile+'_'+curr_time
#model.save_weights('../Output/ModelParams/LSTM_params_BiDi_%s.h5'%curr_time)
print 'Best Model parameters stored at ../Output/ModelParams/LSTM_BiDi_%s.h5' %curr_time
mfalseLength,mactualLength = utils.analyze_false(validData,validDataNumbers,validLabels,model)
c1p,c1r,c0p,c0r,acc,c1f,c0f = utils.precision_recall(validDataNumbers,validLabels,model,weightsPath = options.outputWeights)
print 'Run %d results :-' %(run+1)
scores['c1_precision'].append(c1p)
scores['c1_recall'].append(c1r)
scores['c0_precision'].append(c0p)
scores['c0_recall'].append(c0r)
scores['accuracy'].append(acc)
scores['c1_fscore'].append(c1f)
scores['c0_fscore'].append(c0f)
scores['mean_actual_length'].append(mactualLength)
scores['mean_false_length'].append(mfalseLength)
scores['sample_info'].append((len(trainData),pos_train_samples,len(validData),pos_valid_samples))
results_info = curr_time + '\tHyperParameters:- \nWord-Index Dictionary : %s \tWordvectors file : %s \tLearning Rate : %f \t split-ratio : %f \tEpochs : %d \tOutput Weights : %s \tResults File : %s\n Neurons : %s \nModel-Layers: %s\nResults averaged over %d runs' %(options.vec_dict, options.pretrained, float(options.lr), float(options.split), int(options.nEpochs),options.outputWeights, options.outputFile,str(options.neurons),str(options.nLayers),int(options.runs))
results += '\nLabel : %s' %options.label
def main():
overall_start_time = datetime.datetime.now()
# Allow user to compare only a subset of the faces
(
number_of_people_to_scan,
attempting_all,
file_str_prefix,
peoples_faces_to_scan,
) = get_number_faces_to_scan(lfw_path, overall_start_time)
# Build up encodings dataset
all_encodings, encodings_start_time, lists_of_images = encodings_builder(
lfw_path, number_of_people_to_scan, peoples_faces_to_scan,
IMAGES_TO_EXCLUDE)
# Compare the encodings
(
same_face_distances_df,
different_face_distances_df,
comparisons_start_time,
comparison_counter,
) = encodings_comparer(all_encodings)
# Make graphs
graph_start_time = all_graphs(
same_face_distances_df,
different_face_distances_df,
comparison_counter,
lists_of_images,
file_str_prefix,
doing_graphs,
CUMULATIVE_GRAPHS,
)
# Calculate precision and recall
precision_recall_start_time = precision_recall(
same_face_distances_df,
different_face_distances_df,
file_str_prefix,
doing_precision_recall,
)
# Find lookalikes and different-looking images of same person
(
different_face_distances_df_sorted,
same_face_distances_df_sorted,
) = output_most_similar_different_people_and_most_different_same_faces(
different_face_distances_df, same_face_distances_df, file_str_prefix)
# Image of lookalikes etc
combine_face_images(different_face_distances_df_sorted, file_str_prefix,
"_8_lookalikes.jpg")
combine_face_images(
same_face_distances_df_sorted,
file_str_prefix,
"_9_different_looking_same_people.jpg",
)
# First names wordcloud
plot_first_names_wordcloud(file_str_prefix, lists_of_images)
# Write out timings and info about images that failed
run_outputs(
attempting_all,
overall_start_time,
encodings_start_time,
comparisons_start_time,
graph_start_time,
precision_recall_start_time,
file_str_prefix,
lists_of_images,
)
def train(self, tr_X, tr_y, te_X, te_y, batchSize=32, maxIter=50,
start=10, period=2, threshold=10, earlyStopTol=2, totalStopTol=2):
trainfn = self.trainfn
lr = self.lr
tr_va_split = int(tr_X.shape[0] * 0.7)
tr_X, va_X = tr_X[:tr_va_split], tr_X[tr_va_split:]
tr_y, va_y = tr_y[:tr_va_split], tr_y[tr_va_split:]
earlyStop = earlyStopGen(start, period, threshold, earlyStopTol)
earlyStop.next() # 初始化生成器
totalStopCount = 0
for epoch in xrange(maxIter): # every epoch
# In each epoch, we do a full pass over the training data:
trAllPred = None
trRandy = None
trCostSum = 0.
startTime = time.time()
for batch in miniBatchGen(tr_X, tr_y, batchSize, shuffle=True):
Xb, yb = batch
trCost, trPred = trainfn(Xb, yb)
trCostSum += trCost
trAllPred = np.concatenate((trAllPred, trPred), axis=0) \
if trAllPred is not None else trPred
trRandy = np.concatenate((trRandy, yb)) if trRandy is not None else yb
trIter = len(tr_X) // batchSize
if len(tr_X) % batchSize != 0: trIter += 1
trCostMean = trCostSum / trIter
trAcc = accuracy(trAllPred, trRandy)
trP, trR = precision_recall(trAllPred, trRandy)
# And a full pass over the validation data:
vaAllPred = None
vaCostSum = 0.
for batch in miniBatchGen(va_X, va_y, batchSize, shuffle=False):
Xb, yb = batch
vaCost, vaPred = self.vatefn(Xb, yb)
vaCostSum += vaCost
vaAllPred = np.concatenate((vaAllPred, vaPred), axis=0) \
if vaAllPred is not None else vaPred
vaIter = len(va_X) // batchSize
if len(va_X) % batchSize != 0: vaIter += 1
vaCostMean = vaCostSum / vaIter
vaAcc = accuracy(vaAllPred, va_y)
vaP, vaR = precision_recall(vaAllPred, va_y)
print 'epoch ', epoch, ' time: %.3f' % (time.time() - startTime),
print 'trcost: %.5f tracc: %.5f trp: %.5f trr: %.5f' % (trCostMean, trAcc, trP, trR),
print 'vacost: %.5f vaacc: %.5f vap: %.5f var: %.5f' % (vaCostMean, vaAcc, vaP, vaR)
# Then we decide whether to early stop:
if earlyStop.send((trCostMean, vaCostMean)):
lr /= 10 # 如果一次早停止发生，则学习率降低继续迭代
updatesDict = updates.nesterov_momentum(self.trCost, self.params, lr, self.momentum)
trainfn = makeFunc([self.X, self.y], [self.trCost, self.yDropProb], updatesDict)
totalStopCount += 1
if totalStopCount > totalStopTol: # 如果学习率降低仍然发生早停止，则退出迭代
print 'stop'
break
print 'learning rate decreases to ', lr
################################################################################################################
self.istrained = True
params = layers.get_all_param_values(self.outprob)
cPickle.dump(params, open(dataset_path + 'plain_cnn.pkl', 'w'))
################################################################################################################
teAllPred = None
teCostSum = 0.
for batch in miniBatchGen(te_X, te_y, batchSize, shuffle=False):
Xb, yb = batch
teCost, tePred = self.vatefn(Xb, yb)
teCostSum += teCost
teAllPred = np.concatenate((teAllPred, tePred), axis=0) \
if teAllPred is not None else tePred
teIter = len(te_X) // batchSize
if len(te_X) % batchSize != 0: teIter += 1
teCostMean = teCostSum / teIter
teAcc = accuracy(teAllPred, te_y)
teP, teR = precision_recall(teAllPred, te_y)
print 'tecost: %.5f teacc: %.5f tep: %.5f ter: %.5f' % (teCostMean, teAcc, teP, teR)
y_hat = model(x)
loss = K.binary_crossentropy(y, y_hat)
# Weight the loss
pos_weights = y * pos_weight
neg_weights = (1.0 - y) * neg_weight
loss_weights = pos_weights + neg_weights
loss_weighted = tf.reduce_mean(loss * loss_weights)
grads = tape.gradient(loss_weighted, model.weights)
optimizer.apply_gradients(zip(grads, model.weights))
acc = utils.accuracy(y, y_hat)
acc_topk = utils.accuracy_topk(y, y_hat)
precision, recall = utils.precision_recall(y, y_hat)
auc_metric = tf.keras.metrics.AUC()
auc_metric.update_state(y, y_hat)
tf.summary.image('Inputs', x, step=step)
tf.summary.scalar('ClassLoss', loss_weighted, step=step)
tf.summary.scalar('Acc', acc, step=step)
tf.summary.scalar('AuC', auc_metric.result(), step=step)
tf.summary.scalar('AccTopK', acc_topk, step=step)
tf.summary.scalar('Precision', precision, step=step)
tf.summary.scalar('Recall', recall, step=step)
tf.summary.histogram('Labels', y, step=step)
tf.summary.histogram('Predictions', y_hat, step=step)
auc_metric.reset_states()
print('Step: ', step,
acc.numpy() * 100, precision.numpy(), recall.numpy(),
def validation(x_valid, y_valid, val_batch_size, num_classes, sess, model, epoch, start_time, w_plus):
loss_batch_all = np.array([])
acc_batch_all = y_pred_all = logits_all = np.zeros((0, num_classes))
model.is_train = False
x_valid, y_valid = randomize(x_valid, y_valid)
step_count = int(len(x_valid) / val_batch_size)
for step in range(step_count):
start = step * val_batch_size
end = (step + 1) * val_batch_size
x_batch, y_batch = get_next_batch(x_valid, y_valid, start, end)
feed_dict_val = {model.x: x_batch, model.y: y_batch, model.w_plus: w_plus}
acc_valid, loss_valid, y_pred, logits = sess.run(
[model.accuracy, model.loss, model.prediction, model.get_logits],
feed_dict=feed_dict_val)
acc_batch_all = np.concatenate((acc_batch_all, acc_valid.reshape([1, num_classes])))
y_pred_all = np.concatenate((y_pred_all, y_pred.reshape([val_batch_size, num_classes])))
logits_all = np.concatenate((logits_all, logits.reshape([val_batch_size, num_classes])))
loss_batch_all = np.append(loss_batch_all, loss_valid)
mean_acc = np.mean(acc_batch_all, axis=0)
mean_loss = np.mean(loss_batch_all)
num_examples = np.sum(y_valid, axis=0)
num_preds = np.sum(y_pred_all, axis=0)
epoch_time = time.time() - start_time
print('******************************************************************************'
'********************************************************')
print('--------------------------------------------------------Validation, Epoch: {}'
' -----------------------------------------------------------'.format(epoch + 1))
print("Atlc\tCrdmg\tEffus\tInflt\tMass\tNodle\tPnum\tPntrx\tConsd"
"\tEdma\tEmpys\tFbrss\tTkng\tHrna\t|Avg.\t|Loss\t|Run Time")
for accu in mean_acc:
print '{:.01%}\t'.format(accu),
print '|{0:.01%}\t|{1:0.02}\t|{2}'.format(np.mean(mean_acc), mean_loss, epoch_time)
for exm in num_examples:
print '{:}\t'.format(exm),
print("Count of pathalogies")
for pred in num_preds:
print '{:}\t'.format(pred),
print("Count of recognized pathalogies")
P = R = np.zeros((1, args.n_cls))
for cond in range(args.n_cls):
y_true = y_valid[:, cond]
y_pred = y_pred_all[:, cond]
P[0, cond], R[0, cond] = precision_recall(y_true, y_pred)
P = np.reshape(P, args.n_cls)
R = np.reshape(R, args.n_cls)
for p in P:
print '{:0.03}\t'.format(p),
print("Precision")
for r in R:
print '{:0.03}\t'.format(r),
print("Recall")
plot_precision_recall_curve(y_valid[:logits_all.shape[0], :], logits_all, epoch)
write_acc_loss_csv(mean_acc, mean_loss, epoch)
write_precision_recall_csv(P, R, epoch)
return mean_acc, mean_loss