def load_data(dict):
data_dict = {}
for key in dict:
data_dict[key] = None
for key in dict:
path = dict[key]
print(path)
npy_files = find_click.list_npy_files(path)
xs = np.empty((0, 320))
count = 0
for npy in npy_files:
npy_data = np.load(npy)
if npy_data.shape[0] == 0:
continue
xs = np.vstack((xs, npy_data))
count += npy_data.shape[0]
print('loaded clicks:', count)
data_dict[key] = xs
return data_dict
def load_data(dict):
n_class = len(dict)
# train_dict = {'0': None, '1': None, '2': None}
# test_dict = {'0': None, '1': None, '2': None}
train_dict = {}
test_dict = {}
for key in dict:
train_dict[key] = None
test_dict[key] = None
for key in dict:
path = dict[key]
# print(path)
c = int(key)
### split by date
# file_list = find_click.list_files(path)
# random_index = np.random.permutation(len(file_list))
# test_set = file_list[random_index[0]]
# train_set = [file_list[i] for i in random_index[1:]]
# # label = np.zeros(n_class)
# # label[c] = 1
#
# # training set
# xs = np.empty((0, 320))
# count = 0
# print('training set loading.......')
# for folder in train_set:
# # print('loading %s' % folder[-6:])
# npy_list = find_click.list_npy_files(folder)
# for npy in npy_list:
# # print('loading %s' % npy)
# npy_data = np.load(npy)
# if npy_data.shape[0] == 0:
# continue
# xs = np.vstack((xs, npy_data))
# count += npy_data.shape[0]
# print('loaded clicks:', count)
#
# # test set
# txs = np.empty((0, 320))
# count = 0
# print('test set loading.......')
# print('loading %s' % test_set[-6:])
# npy_list = find_click.list_npy_files(test_set)
# for npy in npy_list:
# # print('loading %s' % npy)
# npy_data = np.load(npy)
# if npy_data.shape[0] == 0:
# continue
# txs = np.vstack((txs, npy_data))
# count += npy_data.shape[0]
# print('loaded clicks:', count)
### split by file
npy_files = find_click.list_npy_files(path)
npy_num = len(npy_files)
random_index = np.random.permutation(npy_num)
split_point = int(npy_num/4)
test_set = [npy_files[i] for i in random_index[:split_point]]
train_set = [npy_files[i] for i in random_index[split_point:]]
# training set
xs = np.empty((0, 320))
count = 0
print('training set loading.......')
for npy in train_set:
npy_data = np.load(npy)
if npy_data.shape[0] == 0:
continue
xs = np.vstack((xs, npy_data))
count += npy_data.shape[0]
print('loaded clicks:', count)
# testing set
txs = np.empty((0, 320))
count = 0
print('testing set loading.......')
for npy in test_set:
print(npy)
npy_data = np.load(npy)
if npy_data.shape[0] == 0:
continue
txs = np.vstack((txs, npy_data))
count += npy_data.shape[0]
print('loaded clicks:', count)
train_dict[key] = xs
test_dict[key] = txs
return train_dict, test_dict
def test_cnn_batch_learn(data_path, label=3, n_class=8, batch_num=20):
c = label
npy_files = find_click.list_npy_files(data_path)
random_index = np.random.permutation(len(npy_files))
label = np.zeros(n_class)
label[c] = 1
# xs = np.empty((0, 256))
count = 0
#
tf.reset_default_graph()
x = tf.placeholder("float", [None, 96])
# 输入
x_image = tf.reshape(x, [-1, 1, 96, 1])
# 第一个卷积层
W_conv1 = weight_variable([1, 5, 1, 32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_1x2(h_conv1)
# 第二个卷积层
W_conv2 = weight_variable([1, 5, 32, 32])
b_conv2 = bias_variable([32])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_1x2(h_conv2)
# 密集链接层
W_fc1 = weight_variable([1 * 24 * 32, 256])
b_fc1 = bias_variable([256])
h_pool2_flat = tf.reshape(h_pool2, [-1, 1 * 24 * 32])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
# Dropout
keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob=keep_prob)
# 输出层
W_fc2 = weight_variable([256, n_class])
b_fc2 = bias_variable([n_class])
y = tf.nn.relu(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
temp_y = tf.reshape(y, [-1, batch_num, n_class])
temp_y_t = tf.transpose(temp_y, perm=[0, 2, 1])
multi_y_t = tf.reshape(temp_y_t, [-1, batch_num])
multi_y = tf.transpose(multi_y_t, perm=[1, 0])
# 融合层
# # 单模式融合
# W_fuse = weight_variable([1, batch_num])
# b_fuse = bias_variable([n_class])
# fuse_out = tf.nn.softmax(tf.reshape(tf.matmul(W_fuse, multi_y), [-1, n_class]) + b_fuse)
# 多模式融合
fuse_mode = 9
W_fuse = weight_variable([fuse_mode, batch_num])
# b_fuse = bias_variable([fuse_mode, n_class])
multi_fuse_out = tf.nn.relu(tf.matmul(W_fuse, multi_y)) # + b_fuse)
mode_weight = weight_variable([1, fuse_mode])
mode_b = bias_variable([n_class])
fuse_out = tf.nn.softmax(
tf.reshape(tf.matmul(mode_weight, multi_fuse_out), [-1, n_class]) +
mode_b)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
print('==============================================')
total_correct = 0
total = 0
with tf.Session() as sess:
sess.run(init)
saver.restore(sess, "params/cnn_net_lwy.ckpt") # 加载训练好的网络参数
for i in range(len(npy_files)):
npy = npy_files[random_index[i]]
print('loading %s' % npy)
npy_data = np.load(npy)
if npy_data.shape[0] == 0:
continue
xs = np.empty((0, 320))
xs = np.vstack((xs, npy_data))
# xs = npy_data
count = npy_data.shape[0]
print('loaded clicks:', count)
# if count >= batch_num * n_total:
# break
click_batch = []
sample_num = xs.shape[0]
total_batch = int(sample_num / batch_num)
# print('the number of data(%(datasrc)s): %(d)d' % {'datasrc': data_path, 'd': total_batch})
for i in range(0, total_batch):
tmp_xs = np.empty((0, 96))
# for j in range(batch_num * i, batch_num * (i + 1)):
j = batch_num * i
if j > xs.shape[0]:
break
while j >= (batch_num * i) and j < (batch_num * (i + 1)):
if xs.shape[0] == 0:
break
index = j % xs.shape[0]
temp_x = xs[index]
beg_idx = np.random.randint(64, (64 + 32))
crop_x = temp_x[beg_idx:(beg_idx + 192)]
crop_x = np.reshape(crop_x, [1, 192])
crop_x = np.fft.fft(crop_x)
crop_x = np.sqrt(crop_x.real**2 + crop_x.imag**2)
crop_x = crop_x[0, :96]
crop_x = np.reshape(crop_x, [1, 96])
if c >= 0:
# peak值位于20k以下，70k以上的滤去
peak_index = np.argmax(crop_x)
if peak_index < 20 or peak_index > 70:
xs = np.delete(xs, index, 0)
continue
crop_x = energy_normalize(crop_x)
tmp_xs = np.vstack((tmp_xs, crop_x))
j += 1
# click_batch.append(tmp_xs)
label = [0] * n_class
label[c] = 1
label = np.array([[label]])
label = list(label)
tmp_xs = np.expand_dims(np.expand_dims(tmp_xs, axis=0), axis=0)
tmp_xs = list(tmp_xs)
sample = tmp_xs + label
click_batch.append(sample)
print('the number of batch:', len(click_batch))
if len(click_batch) == 0:
continue
total += len(click_batch)
count = 0
majority_mat = [0] * n_class
for i in range(len(click_batch)):
temp_xs = click_batch[i][0][0]
label = np.zeros(n_class)
out_y = sess.run(fuse_out,
feed_dict={
x: temp_xs,
keep_prob: 1.0
})
pre_y = np.argmax(out_y, 1)
ref_y = click_batch[i][1]
if np.equal(pre_y, np.argmax(ref_y)):
count += 1
total_correct += count
print('correct:', count, 'total:', len(click_batch))
print('cnn test accuracy (batch learn): ',
round(count / len(click_batch), 3))
# print('result:', majority_mat)
print('total correct: %d, total: %d, batch learn acc: %f' %
(total_correct, total, total_correct / total))
def test_cnn_data(data_path, label=3, n_class=8, batch_num=20):
c = label
npy_files = find_click.list_npy_files(data_path)
random_index = np.random.permutation(len(npy_files))
label = np.zeros(n_class)
label[c] = 1
# xs = np.empty((0, 256))
count = 0
#
tf.reset_default_graph()
x = tf.placeholder("float", [None, 96])
# 输入
x_image = tf.reshape(x, [-1, 1, 96, 1])
# 第一个卷积层
W_conv1 = weight_variable([1, 5, 1, 32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_1x2(h_conv1)
# 第二个卷积层
W_conv2 = weight_variable([1, 5, 32, 32])
b_conv2 = bias_variable([32])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_1x2(h_conv2)
# 密集链接层
W_fc1 = weight_variable([1 * 24 * 32, 256])
b_fc1 = bias_variable([256])
h_pool2_flat = tf.reshape(h_pool2, [-1, 1 * 24 * 32])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
# Dropout
keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob=keep_prob)
# 输出层
W_fc2 = weight_variable([256, n_class])
b_fc2 = bias_variable([n_class])
y = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
print('==============================================')
total_correct = 0
total = 0
with tf.Session() as sess:
sess.run(init)
saver.restore(sess, "params/cnn_net_lwy.ckpt") # 加载训练好的网络参数
for i in range(len(npy_files)):
npy = npy_files[random_index[i]]
print('loading %s' % npy)
npy_data = np.load(npy)
# x = np.arange(0, 320)
# plt.plot(x, npy_data[0])
# plt.show()
if npy_data.shape[0] == 0:
continue
# npy_data = np.divide(npy_data, 2 ** 10)
# energy = np.sqrt(np.sum(npy_data ** 2, 1))
# energy = np.tile(energy, (npy_data.shape[1], 1))
# energy = energy.transpose()
# npy_data = np.divide(npy_data, energy)
# plt.plot(x, npy_data[0])
# plt.show()
xs = np.empty((0, 320))
xs = np.vstack((xs, npy_data))
# xs = npy_data
count = npy_data.shape[0]
print('loaded clicks:', count)
# if count >= batch_num * n_total:
# break
click_batch = []
sample_num = xs.shape[0]
total_batch = int(sample_num / batch_num)
# print('the number of data(%(datasrc)s): %(d)d' % {'datasrc': data_path, 'd': total_batch})
for i in range(0, total_batch):
tmp_xs = np.empty((0, 96))
# for j in range(batch_num * i, batch_num * (i + 1)):
j = batch_num * i
if j > xs.shape[0]:
break
while j >= (batch_num * i) and j < (batch_num * (i + 1)):
if xs.shape[0] == 0:
break
index = j % xs.shape[0]
temp_x = xs[index]
beg_idx = np.random.randint(64, (64 + 32))
crop_x = temp_x[beg_idx:(beg_idx + 192)]
crop_x = np.reshape(crop_x, [1, 192])
crop_x = np.fft.fft(crop_x)
crop_x = np.sqrt(crop_x.real**2 + crop_x.imag**2)
crop_x = crop_x[0, :96]
crop_x = np.reshape(crop_x, [1, 96])
if c >= 0:
# peak值位于20k以下，70k以上的滤去
peak_index = np.argmax(crop_x)
if peak_index < 20 or peak_index > 70:
xs = np.delete(xs, index, 0)
continue
crop_x = energy_normalize(crop_x)
tmp_xs = np.vstack((tmp_xs, crop_x))
j += 1
label = [0] * n_class
label[c] = 1
label = np.array([[label]])
label = list(label)
tmp_xs = np.expand_dims(np.expand_dims(tmp_xs, axis=0), axis=0)
tmp_xs = list(tmp_xs)
sample = tmp_xs + label
click_batch.append(sample)
print('the number of batch:', len(click_batch))
if len(click_batch) == 0:
continue
total += len(click_batch)
count = 0
majority_mat = [0] * n_class
for i in range(len(click_batch)):
temp_xs = click_batch[i][0]
label = np.zeros(n_class)
for j in range(0, temp_xs.shape[1]):
txs = temp_xs[0, j, :]
txs = np.reshape(txs, [1, 96])
out_y = sess.run(y, feed_dict={x: txs, keep_prob: 1.0})
pre_y = np.argmax(out_y, 1)
label[pre_y] += 1
ref_y = click_batch[i][1]
predict = np.argmax(label)
majority_mat[int(predict)] += 1
if np.equal(np.argmax(label), np.argmax(ref_y)):
count += 1
total_correct += count
print('correct:', count, 'total:', len(click_batch))
print('cnn test accuracy (majority voting): ',
round(count / len(click_batch), 3))
print('result:', majority_mat)
# count = 0
# weight_vote_mat = [0] * n_class
# weight = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
# for i in range(len(click_batch)):
# temp_xs = click_batch[i][0]
# label = np.zeros(n_class)
# for j in range(0, temp_xs.shape[1]):
# txs = temp_xs[0, j, :]
# txs = np.reshape(txs, [1, 192])
# out = sess.run(weight, feed_dict={x: txs, keep_prob: 1.0})
# out = np.reshape(out, label.shape)
# label = label + out
#
# ref_y = click_batch[i][1]
# predict = np.argmax(label)
# weight_vote_mat[int(predict)] += 1
# if np.equal(np.argmax(label), np.argmax(ref_y)):
# count += 1
#
# print('cnn test accuracy (weight voting): ', round(count / len(click_batch), 3))
# print('result:', weight_vote_mat)
#
# count = 0
# softmax_mat = [0] * n_class
# for i in range(len(click_batch)):
# temp_xs = click_batch[i][0]
# label = np.zeros(n_class)
# for j in range(0, temp_xs.shape[1]):
# txs = temp_xs[0, j, :]
# txs = np.reshape(txs, [1, 192])
# out = sess.run(y, feed_dict={x: txs, keep_prob: 1.0})
# out = np.reshape(out, label.shape)
# label = label + out
#
# ref_y = click_batch[i][1]
# predict = np.argmax(label)
# softmax_mat[int(predict)] += 1
# if np.equal(np.argmax(label), np.argmax(ref_y)):
# count += 1
#
# print('cnn test accuracy (sum of softmax voting): ', round(count / len(click_batch), 3))
# print('result:', softmax_mat)
print('total correct:', total_correct, 'total batch:', total)
print('%s mean acc: %f' % (data_path, total_correct / total))
def load_npy_data(batch_num=20, n_total=500):
dict = {'0': '', '1': '', '2': ''}
# dict["0"] = "/home/fish/ROBB/CNN_click/click/TKEO_wk5_complete/Melon"
# dict["1"] = "/home/fish/ROBB/CNN_click/click/TKEO_wk5_complete/Spinner"
# dict["2"] = "/home/fish/ROBB/CNN_click/click/TKEO_wk5_complete/Tt"
dict[
"0"] = "/home/fish/ROBB/CNN_click/click/TKEO_wk5_complete_filtered/Melon"
dict[
"1"] = "/home/fish/ROBB/CNN_click/click/TKEO_wk5_complete_filtered/Spinner"
dict["2"] = "/home/fish/ROBB/CNN_click/click/TKEO_wk5_complete_filtered/Tt"
n_class = len(dict)
# train_xs = np.empty((0, 96))
train_ys = np.empty((0, n_class))
# test_xs = np.empty((0, 96))
test_ys = np.empty((0, n_class))
train_xs = []
# train_ys = []
test_xs = []
# test_ys = []
for key in dict:
path = dict[key]
print(path)
c = int(key)
# npy_files = find_click.list_npy_files(path)
file_list = find_click.list_files(path)
random_index = np.random.permutation(len(file_list))
test_set = file_list[random_index[0]]
train_set = [file_list[i] for i in random_index[1:]]
label = np.zeros(n_class)
label[c] = 1
# training set
xs = np.empty((0, 320))
count = 0
print('training set loading.......')
for folder in train_set:
# print('loading %s' % folder[-6:])
npy_list = find_click.list_npy_files(folder)
for npy in npy_list:
# print('loading %s' % npy)
npy_data = np.load(npy)
if npy_data.shape[0] == 0:
continue
xs = np.vstack((xs, npy_data))
count += npy_data.shape[0]
print('loaded clicks:', count)
# test set
txs = np.empty((0, 320))
count = 0
print('test set loading.......')
print('loading %s' % test_set[-6:])
npy_list = find_click.list_npy_files(test_set)
for npy in npy_list:
# print('loading %s' % npy)
npy_data = np.load(npy)
if npy_data.shape[0] == 0:
continue
txs = np.vstack((txs, npy_data))
count += npy_data.shape[0]
print('loaded clicks:', count)
# xs0, xs1 = split_data(xs)
# print('crop and split clicks...')
# temp_train_xs = random_crop_filter_click(xs, batch_num, n_total, key)
# temp_test_xs = random_crop_filter_click(txs, batch_num, n_total=0, key=key)
print('training set crop...')
temp_train_xs = random_crop(xs, batch_num, n_total, key)
print('testing set crop...')
temp_test_xs = random_crop(txs, batch_num, n_total=0, key=key)
temp_train_ys = np.tile(label, (len(temp_train_xs), 1))
temp_test_ys = np.tile(label, (len(temp_test_xs), 1))
train_xs += temp_train_xs
train_ys = np.vstack((train_ys, temp_train_ys))
test_xs += temp_test_xs
test_ys = np.vstack((test_ys, temp_test_ys))
train_xs = np.array(train_xs)
test_xs = np.array(test_xs)
# xs0, xs1 = split_data(xs)
# print('crop training clicks...')
# temp_train_xs = random_crop(xs0, batch_num, int(n_total * 4 / 5), key)
# print('crop testing clicks...')
# temp_test_xs = random_crop(xs1, batch_num, int(n_total / 5), key)
# print('crop training clicks...')
# temp_train_xs = random_crop_average_click(xs0, batch_num, int(n_total * 4 / 5), key)
# print('crop testing clicks...')
# temp_test_xs = random_crop_average_click(xs1, batch_num, int(n_total / 5), key)
# temp_train_ys = np.tile(label, (temp_train_xs.shape[0], 1))
# temp_test_ys = np.tile(label, (temp_test_xs.shape[0], 1))
#
# train_xs = np.vstack((train_xs, temp_train_xs))
# train_ys = np.vstack((train_ys, temp_train_ys))
# test_xs = np.vstack((test_xs, temp_test_xs))
# test_ys = np.vstack((test_ys, temp_test_ys))
return train_xs, train_ys, test_xs, test_ys
if not os.path.exists(root_save_path):
os.makedirs(root_save_path)
for key in dict:
count = 0
print(dict[key])
path = dict[key]
specie_name = path.split('/')[-1]
file_list = find_click.list_files(path)
save_specie_path = os.path.join(root_save_path, specie_name)
for date_path in file_list:
date = date_path.split('/')[-1]
save_path = os.path.join(save_specie_path, date)
if not os.path.exists(save_path):
os.makedirs(save_path)
npy_list = find_click.list_npy_files(date_path)
for npy in npy_list:
npy_data = np.load(npy)
num = npy_data.shape[0]
xs = np.empty((0, 320))
for index in range(num):
temp_x = npy_data[index]
beg_idx = np.random.randint(64, (64 + 32))
crop_x = temp_x[beg_idx:(beg_idx + 192)]
crop_x = np.reshape(crop_x, [1, 192])
crop_x = np.fft.fft(crop_x)
crop_x = np.sqrt(crop_x.real**2 + crop_x.imag**2)
crop_x = crop_x[0, :96]
crop_x = np.reshape(crop_x, [1, 96])
def load_npy_data(batch_num=20, n_total=500):
# dict = {'0': '', '1': '', '2': '', '3':'', '4':'', '5':'', '6':'', '7':''}
# # dict = {'0': '', '1': '', '2': '', '3': '', '4': '', '5': ''}
#
# dict["0"] = "/home/fish/ROBB/CNN_click/click/Data/BBW/Blainvilles_beaked_whale_(Mesoplodon_densirostris)"
# dict["1"] = "/home/fish/ROBB/CNN_click/click/Data/Gm/Pilot_whale_(Globicephala_macrorhynchus)"
# dict["2"] = "/home/fish/ROBB/CNN_click/click/Data/Gg/Rissos_(Grampus_grisieus)"
#
# dict["3"] = "/home/fish/ROBB/CNN_click/click/Data/Dc/Dc"
# dict["4"] = "/home/fish/ROBB/CNN_click/click/Data/Dd/Dd"
# dict["5"] = "/home/fish/ROBB/CNN_click/click/Data/Melon/palmyra2006"
# dict["6"] = "/home/fish/ROBB/CNN_click/click/Data/Spinner/palmyra2006"
# dict["7"] = "/home/fish/ROBB/CNN_click/click/Data/Tt/palmyra2006"
# dict["0"] = "/home/fish/ROBB/CNN_click/click/CNNDetection/BBW/Blainvilles_beaked_whale_(Mesoplodon_densirostris)"
# dict["1"] = "/home/fish/ROBB/CNN_click/click/CNNDetection/Gm/Pilot_whale_(Globicephala_macrorhynchus)"
# dict["2"] = "/home/fish/ROBB/CNN_click/click/CNNDetection/Gg/Rissos_(Grampus_grisieus)"
#
# # dict["3"] = "/home/fish/ROBB/CNN_click/click/CNNDetection/Tt/palmyra2006"
# # dict["4"] = "/home/fish/ROBB/CNN_click/click/CNNDetection/Dc/Dc"
# # dict["5"] = "/home/fish/ROBB/CNN_click/click/CNNDetection/Dd/Dd"
# # dict["6"] = "/home/fish/ROBB/CNN_click/click/CNNDetection/Melon/palmyra2006"
# # dict["7"] = "/home/fish/ROBB/CNN_click/click/CNNDetection/Spinner/palmyra2006"
#
# # dict["3"] = "/home/fish/ROBB/CNN_click/click/CNNDetection/Tt/palmyra2006"
# dict["3"] = "/home/fish/ROBB/CNN_click/click/CNNDetection/Dc/Dc"
# dict["4"] = "/home/fish/ROBB/CNN_click/click/CNNDetection/Dd/Dd"
# dict["5"] = "/home/fish/ROBB/CNN_click/click/CNNDetection/Melon/palmyra2006"
# # dict["6"] = "/home/fish/ROBB/CNN_click/click/CNNDetection/Spinner/palmyra2006"
dict = {'0': '', '1': '', '2': '', '3': '', '4': '', '5': '', '6': ''}
# dict = {'0': '', '1': '', '2': '', '3': '', '4': '', '5': ''}
dict["0"] = "/home/fish/ROBB/CNN_click/click/ClearData/Mesoplodon/Blainvilles_beaked_whale_(Mesoplodon_densirostris)"
dict["1"] = "/home/fish/ROBB/CNN_click/click/ClearData/Gg/Rissos_(Grampus_grisieus)"
dict["2"] = "/home/fish/ROBB/CNN_click/click/ClearData/Gm/Pilot_whale_(Globicephala_macrorhynchus)"
dict["3"] = "/home/fish/ROBB/CNN_click/click/Data/Melon/palmyra2006"
dict["4"] = "/home/fish/ROBB/CNN_click/click/Data/Dd/Dd"
dict["5"] = "/home/fish/ROBB/CNN_click/click/Data/Spinner/palmyra2006"
# dict["0"] = "/home/fish/ROBB/CNN_click/click/Data/Dc/Dc"
dict["6"] = "/home/fish/ROBB/CNN_click/click/Data/Tt/palmyra2006"
n_class = len(dict)
train_xs = np.empty((0, 96))
train_ys = np.empty((0, n_class))
test_xs = np.empty((0, 96))
test_ys = np.empty((0, n_class))
for key in dict:
path = dict[key]
c = int(key)
npy_files = find_click.list_npy_files(path)
random_index = np.random.permutation(len(npy_files))
label = np.zeros(n_class)
label[c] = 1
xs = np.empty((0, 320))
count = 0
#
for i in range(len(npy_files)):
npy = npy_files[i]
print('loading %s' % npy)
npy_data = np.load(npy)
if npy_data.shape[0] == 0:
continue
xs = np.vstack((xs, npy_data))
count += npy_data.shape[0]
# if count >= batch_num * n_total:
# break
if count >= 100000:
break
print('loaded clicks:', count)
xs0, xs1 = split_data(xs)
print('crop training clicks...')
temp_train_xs = random_crop(xs0, batch_num, int(n_total * 4 / 5), key)
print('crop testing clicks...')
temp_test_xs = random_crop(xs1, batch_num, int(n_total / 5), key)
temp_train_ys = np.tile(label, (temp_train_xs.shape[0], 1))
temp_test_ys = np.tile(label, (temp_test_xs.shape[0], 1))
train_xs = np.vstack((train_xs, temp_train_xs))
train_ys = np.vstack((train_ys, temp_train_ys))
test_xs = np.vstack((test_xs, temp_test_xs))
test_ys = np.vstack((test_ys, temp_test_ys))
return train_xs, train_ys, test_xs, test_ys
def load_data(batch_num=20):
dict = {'0': '', '1': '', '2': ''}
# dict["0"] = "/home/fish/ROBB/CNN_click/click/TKEO_wk5_complete/Melon"
# dict["1"] = "/home/fish/ROBB/CNN_click/click/TKEO_wk5_complete/Spinner"
# dict["2"] = "/home/fish/ROBB/CNN_click/click/TKEO_wk5_complete/Tt"
dict["0"] = "/home/fish/ROBB/CNN_click/click/CNNDet12_filtered/Melon"
dict["1"] = "/home/fish/ROBB/CNN_click/click/CNNDet12_filtered/Spinner"
dict["2"] = "/home/fish/ROBB/CNN_click/click/CNNDet12_filtered/Tt"
n_class = len(dict)
test_ys = np.empty((0, n_class))
test_xs = []
# gmm_dict = {'0': None, '1': None, '2': None}
train_dict = {'0': None, '1': None, '2': None}
for key in dict:
path = dict[key]
print(path)
c = int(key)
# npy_files = find_click.list_npy_files(path)
file_list = find_click.list_files(path)
random_index = np.random.permutation(len(file_list))
test_set = file_list[random_index[0]]
train_set = [file_list[i] for i in random_index[1:]]
label = np.zeros(n_class)
label[c] = 1
# training set
xs = np.empty((0, 320))
count = 0
print('training set loading.......')
for folder in train_set:
# print('loading %s' % folder[-6:])
npy_list = find_click.list_npy_files(folder)
for npy in npy_list:
# print('loading %s' % npy)
npy_data = np.load(npy)
if npy_data.shape[0] == 0:
continue
xs = np.vstack((xs, npy_data))
count += npy_data.shape[0]
print('loaded clicks:', count)
# test set
txs = np.empty((0, 320))
count = 0
print('test set loading.......')
print('loading %s' % test_set[-6:])
npy_list = find_click.list_npy_files(test_set)
for npy in npy_list:
# print('loading %s' % npy)
npy_data = np.load(npy)
if npy_data.shape[0] == 0:
continue
txs = np.vstack((txs, npy_data))
count += npy_data.shape[0]
print('loaded clicks:', count)
print('crop training clicks...')
train_xs = random_crop(xs, batch_num, n_total=0)
train_xs = np.array(train_xs)
print('crop testing clicks...')
temp_test_xs = random_crop(txs, batch_num, n_total=0)
train_dict[key] = train_xs
# gmm = GMM(n_components=16).fit(train_xs)
#
# gmm_dict[key] = gmm
temp_test_ys = np.tile(label, (len(temp_test_xs), 1))
test_xs += temp_test_xs
test_ys = np.vstack((test_ys, temp_test_ys))
test_xs = np.array(test_xs)
return train_dict, test_xs, test_ys