class GoogLeNet:
network = input_data(shape=[None, 1024, 1024, 1])
network = conv_2d(network, 64, 9, strides=4, activation='relu', bias=False)
'''Bottleneck'''
network = tflearn.residual_bottleneck(network,
nb_blocks=3,
bottleneck_size=16,
out_channels=64)
network = tflearn.residual_bottleneck(network,
nb_blocks=1,
bottleneck_size=32,
out_channels=128,
downsample=True)
network = tflearn.residual_bottleneck(network,
nb_blocks=2,
bottleneck_size=32,
out_channels=128)
network = tflearn.residual_bottleneck(network,
nb_blocks=1,
bottleneck_size=64,
out_channels=256,
downsample=True)
network = tflearn.residual_bottleneck(network,
nb_blocks=2,
bottleneck_size=64,
out_channels=256)
network = batch_normalization(network)
network = tflearn.activation(network, 'relu')
network = global_avg_pool(network)
'''Output layer'''
output = fully_connected(network, 15, activation='sigmoid')
network = regression(output,
optimizer='momentum',
loss='binary_crossentropy',
learning_rate=0.01)
'''Set model + Save parameters + Tensorboard'''
model = tflearn.DNN(network,
checkpoint_path='params_resnet_cxr',
max_checkpoints=1,
tensorboard_verbose=0)
'''Feed the oxflowers17 dataset to the model'''
model.fit(train_x,
train_t,
n_epoch=10,
validation_set=(test_x, test_t),
show_metric=True,
batch_size=16,
snapshot_epoch=False,
snapshot_step=100,
run_id='resnet_cxr')
def ResNet1(network):
network = conv_2d(network, 64, 3, activation='relu', bias=False)
# Residual blocks
network = residual_bottleneck(network, 3, 16, 64)
network = residual_bottleneck(network, 1, 32, 128, downsample=True)
network = residual_bottleneck(network, 2, 32, 128)
network = residual_bottleneck(network, 1, 64, 256, downsample=True)
network = residual_bottleneck(network, 2, 64, 256)
network = batch_normalization(network)
network = activation(network, 'relu')
network = global_avg_pool(network)
# Regression
network = fully_connected(network, output_dim, activation='softmax')
return network
def architecture03(input, num_class):
net = tflearn.conv_2d(input, 64, 3, activation='relu', bias=False)
net = tflearn.residual_bottleneck(net, 3, 16, 64)
net = tflearn.residual_bottleneck(net, 1, 32, 128, downsample=False)
net = tflearn.residual_bottleneck(net, 2, 32, 128)
net = tflearn.residual_bottleneck(net, 1, 64, 256, downsample=False)
net = tflearn.residual_bottleneck(net, 2, 64, 256)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
net = tflearn.fully_connected(net, num_class, activation='softmax')
net = tflearn.regression(net,
optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.001)
return tflearn.DNN(net, tensorboard_verbose=0)
def train(X_train_R, Y_train, X_test_R, Y_test, predict_data):
X_train_R, mean = du.featurewise_zero_center(X_train_R)
X_test_R = du.featurewise_zero_center(X_test_R, mean)
net = tflearn.input_data(shape=[None, 28, 28, 1])
net = tflearn.conv_2d(net, 64, 3, activation='relu', bias=False)
net = tflearn.residual_bottleneck(net, 3, 16, 64)
net = tflearn.residual_bottleneck(net, 1, 32, 128, downsample=True)
net = tflearn.residual_bottleneck(net, 2, 32, 128)
net = tflearn.residual_bottleneck(net, 1, 64, 256, downsample=True)
net = tflearn.residual_bottleneck(net, 2, 64, 256)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
net = tflearn.fully_connected(net, 10, activation='softmax')
net = tflearn.regression(net,
optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.1)
model = tflearn.DNN(net,
checkpoint_path='model_Digit',
max_checkpoints=2,
tensorboard_verbose=0,
tensorboard_dir='logs')
model.fit(X_train_R,
Y_train,
n_epoch=1,
validation_set=(X_test_R, Y_test),
show_metric=True,
batch_size=10,
run_id='Digit')
model.save("model_Digit/digit_recognition")
print("model train OK!\n save model OK!")
model.load("model_Digit/digit_recognition")
print("model already loaded!")
print("model predict")
label = model.predict(predict_data)
print("predict label: ", label)
def demo():
import tflearn.datasets.mnist as mnist
x, y, test_x, test_y = mnist.load_data(one_hot='True')
print(x.shape)
x = x.reshape([-1, 28, 28, 1])
test_x = test_x.reshape([-1, 28, 28, 1])
# 按功能划分的零中心将每个样本的中心置零，并指定平均值。如果未指定，则对所有样品评估平均值。
# Returns : A numpy array with same shape as input. Or a tuple (array, mean) if no mean value was specified.
x, mean = du.featurewise_zero_center(x)
test_x = du.featurewise_zero_center(test_x, mean)
net = tflearn.input_data(shape=[None, 28, 28, 1])
net = tflearn.conv_2d(net, 64, 3, activation='relu', bias=False)
net = tflearn.residual_bottleneck(net, 3, 16, 64)
net = tflearn.residual_bottleneck(net, 1, 32, 128, downsample=True)
net = tflearn.residual_bottleneck(net, 2, 32, 128)
net = tflearn.residual_bottleneck(net, 1, 64, 256, downsample=True)
net = tflearn.residual_bottleneck(net, 2, 64, 256)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
# Regression
net = tflearn.fully_connected(net, 10, activation='softmax')
net = tflearn.regression(net,
optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.1)
# Training
model = tflearn.DNN(net,
checkpoint_path='model_resnet_mnist',
max_checkpoints=10,
tensorboard_verbose=0)
model.fit(x,
y,
n_epoch=100,
validation_set=(test_x, test_y),
show_metric=True,
batch_size=256,
run_id='resnet_mnist')
def build_resnet():
''' Specify layers for ResNet and return constructed network '''
net = tflearn.input_data(
shape=[None, INPUT_DATA_SIZE_X, INPUT_DATA_SIZE_Y, 1])
net = tflearn.conv_2d(net, 64, 3, activation='relu', bias=False)
# Residual blocks
net = tflearn.residual_bottleneck(net, 3, 16, 64)
net = tflearn.residual_bottleneck(net, 1, 32, 128, downsample=True)
net = tflearn.residual_bottleneck(net, 2, 32, 128)
net = tflearn.residual_bottleneck(net, 1, 64, 256, downsample=True)
net = tflearn.residual_bottleneck(net, 2, 64, 256)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
# Regression
net = tflearn.fully_connected(net, OUTPUT_DATA_SIZE, activation='softmax')
net = tflearn.regression(net,
optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.1)
return net
fully_connect = tflearn.fully_connected(net,HIDDEN,activation='relu')
# [1000]
net = tflearn.fully_connected(fully_connect,LEN_OUT,activation='softmax')
# [7211]
#mom = tflearn.SGD(0.1,lr_decay=0.1,decay_step=3086 * 20)
mom = tflearn.Momentum(0.01,lr_decay=0.1,decay_step=int(395000 / BATCH_SIZE) * 10,staircase=True)
reg = tflearn.regression(net,optimizer=mom,loss='categorical_crossentropy')
model = tflearn.DNN(reg,checkpoint_path='models/{}'.format(MODEL_FILE),max_checkpoints=100,session=sess)
elif NET_TYPE == 'resnet50' or NET_TYPE == 'resnet101' or NET_TYPE == 'resnet152':
with tf.device("/gpu:{}".format(GPU_CORE)):
net = tflearn.input_data(shape=[None,IMG_SIZE,IMG_SIZE,3])
net = tflearn.conv_2d(net,64,7,2, regularizer='L2', weight_decay=0.0001)
# [64,64,32]
net = tflearn.max_pool_2d(net,3,2)
# [32,32,32]
net = tflearn.residual_bottleneck(net,3,64,256)
# [32,32,64]
net = tflearn.residual_bottleneck(net,1,128,512,downsample=True)
# [16,16,128]
net = tflearn.residual_bottleneck(net,3,128,512)
# [16,16,128]
net = tflearn.residual_bottleneck(net,1,256,1024,downsample=True)
# [16,16,256]
if NET_TYPE == 'resnet50':
net = tflearn.residual_bottleneck(net,5,256,1024)
elif NET_TYPE == 'resnet101':
net = tflearn.residual_bottleneck(net,22,256,1024)
elif NET_TYPE == 'resnet152':
net = tflearn.residual_bottleneck(net,35,256,1024)
# [16,16,256]
def get_resnet_feature(test_data, test_label, test_pid, pid_map):
n_dim = 6000
n_split = 300
tf.reset_default_graph()
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
### split
#X = X.reshape([-1, n_split, 1])
#testX = testX.reshape([-1, n_split, 1])
# Building Residual Network
net = tflearn.input_data(shape=[None, n_dim, 1])
############ reshape for sub_seq
net = tf.reshape(net, [-1, n_split, 1])
net = tflearn.conv_1d(net, 64, 16, 2)
#net = tflearn.conv_1d(net, 64, 16, 2, regularizer='L2', weight_decay=0.0001)
net = tflearn.batch_normalization(net)
# Residual blocks
net = tflearn.residual_bottleneck(net,
2,
16,
64,
downsample_strides=2,
downsample=True,
is_first_block=True)
net = tflearn.residual_bottleneck(net,
2,
16,
64,
downsample_strides=2,
downsample=True)
net = tflearn.residual_bottleneck(net,
2,
16,
128,
downsample_strides=2,
downsample=True)
net = tflearn.residual_bottleneck(net,
2,
16,
128,
downsample_strides=2,
downsample=True)
net = tflearn.residual_bottleneck(net,
2,
16,
256,
downsample_strides=2,
downsample=True)
net = tflearn.residual_bottleneck(net,
2,
16,
256,
downsample_strides=2,
downsample=True)
net = tflearn.residual_bottleneck(net,
2,
16,
512,
downsample_strides=2,
downsample=True)
net = tflearn.residual_bottleneck(net,
2,
16,
512,
downsample_strides=2,
downsample=True)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
#net = tflearn.global_avg_pool(net)
# LSTM
############ reshape for sub_seq
net = tf.reshape(net, [-1, n_dim // n_split, 512])
net = bidirectional_rnn(net, BasicLSTMCell(256), BasicLSTMCell(256))
#net = tflearn.layers.recurrent.lstm(net, n_units=512)
#print("after LSTM", net.get_shape())
net = dropout(net, 0.5)
# Regression
feature_layer = tflearn.fully_connected(net, 32, activation='sigmoid')
net = tflearn.dropout(feature_layer, 0.5)
net = tflearn.fully_connected(net, 4, activation='softmax')
net = tflearn.regression(
net,
optimizer='adam', #momentum',
loss='categorical_crossentropy')
#,learning_rate=0.1)
## save model
### load
model = tflearn.DNN(net)
run_id = 'resnet_6000_500_10_5_v1'
model.load('../model/resNet/' + run_id)
### create new model, and get features
m2 = tflearn.DNN(feature_layer, session=model.session)
tmp_feature = []
num_of_test = len(test_data)
cur_data = []
pre = []
for i in range(num_of_test):
cur_data.append(test_data[i])
if (i % 2000 == 0 or i == (num_of_test - 1)) and i != 0:
#tmp_test_data = test_data[i].reshape([-1, n_dim, 1])
tmp_testX = np.array(cur_data, dtype=np.float32)
tmp_feature.extend(m2.predict(tmp_testX.reshape([-1, n_dim, 1])))
cur_data = []
pre.extend(model.predict(tmp_testX))
tmp_feature = np.array(tmp_feature)
test_pid = np.array(test_pid, dtype=np.string_)
y_num = len(pid_map)
features = [[0. for j in range(32)] for i in range(y_num)]
re_labels = [[0. for j in range(4)] for i in range(y_num)]
y_pre = [[0. for j in range(4)] for i in range(y_num)]
y_sec_pre = [[0. for j in range(4)] for i in range(y_num)]
y_third_pre = [[0. for j in range(4)] for i in range(y_num)]
y_fourth_pre = [[0. for j in range(4)] for i in range(y_num)]
y_fifth_pre = [[0. for j in range(4)] for i in range(y_num)]
y_sixth_pre = [[0. for j in range(4)] for i in range(y_num)]
y_seventh_pre = [[0. for j in range(4)] for i in range(y_num)]
y_groundtruth = [[0. for j in range(4)] for i in range(y_num)]
#print(y_num)
for j in range(len(tmp_feature)):
feature_pred = np.array(tmp_feature[j], dtype=np.float32)
#print(len(feature_pred))
i_pred = np.array(pre[j], dtype=np.float32)
cur_pid = str(test_pid[j], 'utf-8')
list_id = pid_map[cur_pid]
#print (list_id)
temp_feature = np.array(features[list_id], dtype=np.float32)
temp_pre = np.array(y_pre[list_id], dtype=np.float32)
temp_sec_pre = np.array(y_sec_pre[list_id], dtype=np.float32)
temp_third_pre = np.array(y_third_pre[list_id], dtype=np.float32)
#print(temp_pre)
max_p = temp_pre[np.argmax(temp_pre)]
max_sec_p = temp_sec_pre[np.argmax(temp_sec_pre)]
max_third_p = temp_third_pre[np.argmax(temp_third_pre)]
sec_p = 0
sec_sec_p = 0
sec_third_p = 0
for k in range(len(temp_pre)):
if temp_pre[k] == max_p:
continue
if temp_pre[k] > sec_p:
sec_p = temp_pre[k]
if temp_sec_pre[k] == max_sec_p:
continue
if temp_sec_pre[k] > sec_sec_p:
sec_sec_p = temp_sec_pre[k]
if temp_third_pre[k] == max_third_p:
continue
if temp_third_pre[k] > sec_third_p:
sec_third_p = temp_third_pre[k]
cur_max_p = i_pred[np.argmax(i_pred)]
cur_sec_p = 0
for k in range(len(i_pred)):
if i_pred[k] == cur_max_p:
continue
if i_pred[k] > cur_sec_p:
cur_sec_p = i_pred[k]
if (cur_max_p - cur_sec_p) > (max_p - sec_p):
y_seventh_pre[list_id] = y_sixth_pre[list_id]
y_sixth_pre[list_id] = y_fifth_pre[list_id]
y_fifth_pre[list_id] = y_fourth_pre[list_id]
y_fourth_pre[list_id] = y_third_pre[list_id]
y_third_pre[list_id] = y_sec_pre[list_id]
y_sec_pre[list_id] = y_pre[list_id]
y_pre[list_id] = i_pred
elif (cur_max_p - cur_sec_p) > (max_sec_p - sec_sec_p):
y_seventh_pre[list_id] = y_sixth_pre[list_id]
y_sixth_pre[list_id] = y_fifth_pre[list_id]
y_fifth_pre[list_id] = y_fourth_pre[list_id]
y_fourth_pre[list_id] = y_third_pre[list_id]
y_third_pre[list_id] = y_sec_pre[list_id]
y_sec_pre[list_id] = i_pred
elif (cur_max_p - cur_sec_p) > (max_third_p - sec_third_p):
y_seventh_pre[list_id] = y_sixth_pre[list_id]
y_sixth_pre[list_id] = y_fifth_pre[list_id]
y_fifth_pre[list_id] = y_fourth_pre[list_id]
y_fourth_pre[list_id] = y_third_pre[list_id]
y_third_pre[list_id] = i_pred
elif (cur_max_p - cur_sec_p) > (max_fourth_p - sec_fourth_p):
y_seventh_pre[list_id] = y_sixth_pre[list_id]
y_sixth_pre[list_id] = y_fifth_pre[list_id]
y_fifth_pre[list_id] = y_fourth_pre[list_id]
y_fourth_pre[list_id] = i_pred
elif (cur_max_p - cur_sec_p) > (max_fifth_p - sec_fifth_p):
y_seventh_pre[list_id] = y_sixth_pre[list_id]
y_sixth_pre[list_id] = y_fifth_pre[list_id]
y_fifth_pre[list_id] = i_pred
elif (cur_max_p - cur_sec_p) > (max_sixth_p - sec_sixth_p):
y_seventh_pre[list_id] = y_sixth_pre[list_id]
y_sixth_pre[list_id] = i_pred
elif (cur_max_p - cur_sec_p) > (max_seventh_p - sec_seventh_p):
y_seventh_pre[list_id] = i_pred
max_f = 0
for k in range(len(temp_feature)):
if temp_feature[k] > max_f:
max_f = temp_feature[k]
if max_f > 0:
feature_pred = (feature_pred + temp_feature) / 2
#for k in range(len(temp_feature)):
# feature_pred[k] = (feature_pred[k]+temp_feature[k])/2
features[list_id] = feature_pred
y_groundtruth[list_id] = test_label[j]
gt_list = ["N", "A", "O", "~"]
pred_1 = gt_list[np.argmax(i_pred)]
if pred_1 == 'N':
re_labels[list_id][0] += 1
elif pred_1 == 'A':
re_labels[list_id][1] += 1
elif pred_1 == 'O':
re_labels[list_id][2] += 1
elif pred_1 == '~':
re_labels[list_id][3] += 1
else:
print('wrong label')
out_feature = []
for i in range(len(features)):
out_feature.append(features[i])
out_feature = np.array(out_feature)
for k in range(len(y_pre)):
labels = [0. for j in range(4)]
pred_1 = np.argmax(y_pre[k])
labels[pred_1] += 1
pred_2 = np.argmax(y_sec_pre[k])
labels[pred_2] += 1
pred_3 = np.argmax(y_third_pre[k])
labels[pred_3] += 1
if pred_1 == 2:
print("O was selected!")
continue
elif pred_2 == 2:
y_pre[k] = y_sec_pre[k]
print("O was selected!")
elif pred_3 == 2:
y_pre[k] = y_third_pre[k]
print("O was selected!")
if pred_1 != np.argmax(labels):
if pred_2 == np.argmax(labels):
y_pre[k] = y_sec_pre[k]
print("Second was selected!")
MyEval.F1Score3_num(pre, test_label[:len(pre)])
MyEval.F1Score3_num(y_pre, y_groundtruth)
MyEval.F1Score3_num(re_labels, y_groundtruth)
return out_feature
def get_resnet_feature(test_data):
n_dim = 6000
n_split = 300
tf.reset_default_graph()
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
### split
#X = X.reshape([-1, n_split, 1])
#testX = testX.reshape([-1, n_split, 1])
# Building Residual Network
net = tflearn.input_data(shape=[None, n_dim, 1])
print("input", net.get_shape())
############ reshape for sub_seq
net = tf.reshape(net, [-1, n_split, 1])
print("reshaped input", net.get_shape())
net = tflearn.conv_1d(net, 64, 16, 2)
#net = tflearn.conv_1d(net, 64, 16, 2, regularizer='L2', weight_decay=0.0001)
print("cov1", net.get_shape())
net = tflearn.batch_normalization(net)
print("bn1", net.get_shape())
net = tflearn.activation(net, 'relu')
print("relu1", net.get_shape())
# Residual blocks
'''net = tflearn.residual_bottleneck(net, 2, 16, 64, downsample_strides = 2, downsample=True, is_first_block = True)
print("resn2", net.get_shape())
net = tflearn.residual_bottleneck(net, 2, 16, 128, downsample_strides = 2, downsample=True)
print("resn4", net.get_shape())
net = tflearn.residual_bottleneck(net, 2, 16, 256, downsample_strides = 2, downsample=True)
print("resn6", net.get_shape())
net = tflearn.residual_bottleneck(net, 2, 16, 512, downsample_strides = 2, downsample=True)
print("resn8", net.get_shape())
net = tflearn.residual_bottleneck(net, 2, 16, 1024, downsample_strides = 2, downsample=True)
print("resn10", net.get_shape())'''
net = tflearn.residual_bottleneck(net, 2, 16, 64, downsample_strides = 2, downsample=True, is_first_block = True)
print("resn2", net.get_shape())
net = tflearn.residual_bottleneck(net, 2, 16, 64, downsample_strides = 2, downsample=True)
print("resn4", net.get_shape())
net = tflearn.residual_bottleneck(net, 2, 16, 128, downsample_strides = 2, downsample=True)
print("resn6", net.get_shape())
net = tflearn.residual_bottleneck(net, 2, 16, 128, downsample_strides = 2, downsample=True)
print("resn8", net.get_shape())
net = tflearn.residual_bottleneck(net, 2, 16, 256, downsample_strides = 2, downsample=True)
print("resn10", net.get_shape())
net = tflearn.residual_bottleneck(net, 2, 16, 256, downsample_strides = 2, downsample=True)
print("resn12", net.get_shape())
net = tflearn.residual_bottleneck(net, 2, 16, 512, downsample_strides = 2, downsample=True)
print("resn14", net.get_shape())
net = tflearn.residual_bottleneck(net, 2, 16, 512, downsample_strides = 2, downsample=True)
print("resn16", net.get_shape())
'''net = tflearn.residual_bottleneck(net, 2, 16, 1024, downsample_strides = 2, downsample=True)
print("resn18", net.get_shape())
net = tflearn.residual_bottleneck(net, 2, 16, 1024, downsample_strides = 2, downsample=True)
print("resn20", net.get_shape())'''
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
#net = tflearn.global_avg_pool(net)
# LSTM
print("before LSTM, before reshape", net.get_shape())
############ reshape for sub_seq
net = tf.reshape(net, [-1, n_dim//n_split, 512])
print("before LSTM", net.get_shape())
net = bidirectional_rnn(net, BasicLSTMCell(256), BasicLSTMCell(256))
print("after LSTM", net.get_shape())
#net = tflearn.layers.recurrent.lstm(net, n_units=512)
#print("after LSTM", net.get_shape())
net = dropout(net, 0.5)
# Regression
net = tflearn.fully_connected(net, 32, activation='sigmoid')
net = tflearn.dropout(net, 0.5)
# net, feature_layer = tflearn.fully_connected(net, 4, activation='softmax', return_logit = True)
feature_layer = tflearn.fully_connected(net, 4, activation='softmax')
print('feature_layer: ', feature_layer.get_shape())
print("dense", net.get_shape())
net = tflearn.regression(net, optimizer='adam',#momentum',
loss='categorical_crossentropy')
#,learning_rate=0.1)
print('final output: ', net.get_shape())
## save model
### load
model = tflearn.DNN(net)
run_id = 'resnet_6000_500_10_5_v1'
model.load('../model/resNet/'+run_id)
# print(tflearn.variables.get_all_variables())
### create new model, and get features
m2 = tflearn.DNN(feature_layer, session=model.session)
tmp_feature = []
num_of_test = len(test_data)
cur_data = []
pre = []
for i in range(num_of_test):
cur_data.append(test_data[i])
if (i % 2000 == 0 or i == (num_of_test - 1)) and i !=0:
#tmp_test_data = test_data[i].reshape([-1, n_dim, 1])
tmp_testX = np.array(cur_data, dtype=np.float32)
tmp_feature.extend(m2.predict(tmp_testX.reshape([-1, n_dim, 1])))
cur_data = []
pre.extend(model.predict(tmp_testX))
print(i, len(tmp_feature), len(tmp_feature[0]))
tmp_feature = np.array(tmp_feature)
return tmp_feature
def make_core_network(net, regularizer='L2'):
############ reshape for sub_seq
net = tf.reshape(net, [-1, n_split, 1])
print("reshaped input", net.get_shape())
net = tflearn.conv_1d(net, 64, 16, 2)
#net = tflearn.conv_1d(net, 64, 16, 2, regularizer='L2', weight_decay=0.0001)
print("cov1", net.get_shape())
net = tflearn.batch_normalization(net)
print("bn1", net.get_shape())
net = tflearn.activation(net, 'relu')
print("relu1", net.get_shape())
# Residual blocks
'''net = tflearn.residual_bottleneck(net, 2, 16, 64, downsample_strides = 2, downsample=True, is_first_block = True)
print("resn2", net.get_shape())
net = tflearn.residual_bottleneck(net, 2, 16, 128, downsample_strides = 2, downsample=True)
print("resn4", net.get_shape())
net = tflearn.residual_bottleneck(net, 2, 16, 256, downsample_strides = 2, downsample=True)
print("resn6", net.get_shape())
net = tflearn.residual_bottleneck(net, 2, 16, 512, downsample_strides = 2, downsample=True)
print("resn8", net.get_shape())
net = tflearn.residual_bottleneck(net, 2, 16, 1024, downsample_strides = 2, downsample=True)
print("resn10", net.get_shape())'''
net = tflearn.residual_bottleneck(net,
2,
16,
64,
downsample_strides=2,
downsample=True,
is_first_block=True)
print("resn2", net.get_shape())
net = tflearn.residual_bottleneck(net,
2,
16,
64,
downsample_strides=2,
downsample=True)
print("resn4", net.get_shape())
net = tflearn.residual_bottleneck(net,
2,
16,
128,
downsample_strides=2,
downsample=True)
print("resn6", net.get_shape())
net = tflearn.residual_bottleneck(net,
2,
16,
128,
downsample_strides=2,
downsample=True)
print("resn8", net.get_shape())
net = tflearn.residual_bottleneck(net,
2,
16,
256,
downsample_strides=2,
downsample=True)
print("resn10", net.get_shape())
net = tflearn.residual_bottleneck(net,
2,
16,
256,
downsample_strides=2,
downsample=True)
print("resn12", net.get_shape())
net = tflearn.residual_bottleneck(net,
2,
16,
512,
downsample_strides=2,
downsample=True)
print("resn14", net.get_shape())
net = tflearn.residual_bottleneck(net,
2,
16,
512,
downsample_strides=2,
downsample=True)
print("resn16", net.get_shape())
'''net = tflearn.residual_bottleneck(net, 2, 16, 1024, downsample_strides = 2, downsample=True)
print("resn18", net.get_shape())
net = tflearn.residual_bottleneck(net, 2, 16, 1024, downsample_strides = 2, downsample=True)
print("resn20", net.get_shape())'''
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
#net = tflearn.global_avg_pool(net)
# LSTM
print("before LSTM, before reshape", net.get_shape())
############ reshape for sub_seq
net = tf.reshape(net, [-1, n_dim // n_split, 512])
print("before LSTM", net.get_shape())
net = bidirectional_rnn(net, BasicLSTMCell(256), BasicLSTMCell(256))
print("after LSTM", net.get_shape())
#net = tflearn.layers.recurrent.lstm(net, n_units=512)
#print("after LSTM", net.get_shape())
net = dropout(net, 0.5)
# Regression
feature_layer = tflearn.fully_connected(net, 32, activation='sigmoid')
net = tflearn.dropout(feature_layer, 0.5)
net = tflearn.fully_connected(net, 4, activation='softmax')
print("dense", net.get_shape())
return net, feature_layer
import tflearn
import tflearn.data_utils as du
# Data loading and preprocessing
import tflearn.datasets.mnist as mnist
X, Y, testX, testY = mnist.load_data(one_hot=True)
X = X.reshape([-1, 28, 28, 1])
testX = testX.reshape([-1, 28, 28, 1])
X, mean = du.featurewise_zero_center(X)
testX = du.featurewise_zero_center(testX, mean)
# Building Residual Network
net = tflearn.input_data(shape=[None, 28, 28, 1])
net = tflearn.conv_2d(net, 64, 3, activation='relu', bias=False)
# Residual blocks
net = tflearn.residual_bottleneck(net, 3, 16, 64)
net = tflearn.residual_bottleneck(net, 1, 32, 128, downsample=True)
net = tflearn.residual_bottleneck(net, 2, 32, 128)
net = tflearn.residual_bottleneck(net, 1, 64, 256, downsample=True)
net = tflearn.residual_bottleneck(net, 2, 64, 256)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
# Regression
net = tflearn.fully_connected(net, 10, activation='softmax')
net = tflearn.regression(net, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.1)
# Training
model = tflearn.DNN(net, checkpoint_path='model_resnet_mnist',
max_checkpoints=10, tensorboard_verbose=0)
def get_deep_centerwave_feature(test_data):
tf.reset_default_graph()
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
_, n_dim = test_data.shape
############################### model
net = tflearn.input_data(shape=[None, n_dim, 1])
print("input", net.get_shape())
net = tflearn.avg_pool_1d(net, kernel_size=5, strides=5)
print("avg_pool_1d", net.get_shape())
net = tflearn.conv_1d(net, 64, 16, 2)
print("cov1", net.get_shape())
net = tflearn.batch_normalization(net)
print("bn1", net.get_shape())
net = tflearn.activation(net, 'relu')
print("relu1", net.get_shape())
net = tflearn.residual_bottleneck(net,
2,
16,
64,
downsample_strides=2,
downsample=True,
is_first_block=True)
print("resn2", net.get_shape())
net = tflearn.residual_bottleneck(net,
2,
16,
128,
downsample_strides=2,
downsample=True)
print("resn4", net.get_shape())
net = tflearn.residual_bottleneck(net,
2,
16,
256,
downsample_strides=2,
downsample=True)
print("resn6", net.get_shape())
# net = tflearn.residual_bottleneck(net, 2, 16, 512, downsample_strides = 2, downsample=True)
# print("resn8", net.get_shape())
# net = tflearn.residual_bottleneck(net, 2, 16, 1024, downsample_strides = 2, downsample=True)
# print("resn10", net.get_shape())
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
feature_layer = tflearn.fully_connected(net, 32, activation='sigmoid')
print("feature_layer", feature_layer.get_shape())
net = feature_layer
net = tflearn.fully_connected(net, 4, activation='softmax')
print("dense", net.get_shape())
net = tflearn.regression(net,
optimizer='adam',
loss='categorical_crossentropy',
learning_rate=0.01)
###############################
### load
model = tflearn.DNN(net)
model.load(
'../model/model_deep_centerwave_0810_all/model_deep_centerwave_resnet')
### create new model, and get features
m2 = tflearn.DNN(feature_layer, session=model.session)
out_feature = []
pred = []
num_of_test = len(test_data)
for i in range(num_of_test):
tmp_test_data = test_data[i].reshape([-1, n_dim, 1])
out_feature.append(m2.predict(tmp_test_data)[0])
# pred.append(model.predict(tmp_test_data)[0])
out_feature = np.array(out_feature)
# ### eval
# print(len(pred), pred[0], all_label[0])
# MyEval.F1Score3_num(pred, all_label[:num_of_test])
return out_feature
def get_deep_mimic_feats(test_data):
n_dim = 6000
n_split = 300
tf.reset_default_graph()
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# Building Residual Network
net = tflearn.input_data(shape=[None, n_dim, 1])
print("input", net.get_shape())
############ reshape for sub_seq
net = tf.reshape(net, [-1, n_split, 1])
print("reshaped input", net.get_shape())
net = tflearn.conv_1d(net, 64, 16, 2)
#net = tflearn.conv_1d(net, 64, 16, 2, regularizer='L2', weight_decay=0.0001)
print("cov1", net.get_shape())
net = tflearn.batch_normalization(net)
print("bn1", net.get_shape())
net = tflearn.activation(net, 'relu')
print("relu1", net.get_shape())
# Residual blocks
net = tflearn.residual_bottleneck(net, 2, 16, 64, downsample_strides = 2, downsample=True, is_first_block = True)
print("resn2", net.get_shape())
net = tflearn.residual_bottleneck(net, 2, 16, 128, downsample_strides = 4, downsample=True)
print("resn4", net.get_shape())
# net = tflearn.residual_bottleneck(net, 2, 16, 256, downsample_strides = 4, downsample=True)
# print("resn6", net.get_shape())
# net = tflearn.residual_bottleneck(net, 2, 16, 512, downsample_strides = 4, downsample=True)
# print("resn8", net.get_shape())
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
print("before reshape", net.get_shape())
# net = tf.reshape(net, [-1, n_dim//n_split*net.get_shape()[-2], net.get_shape()[-1]])
# LSTM
############ reshape for sub_seq
before_reshaped_shape = net.get_shape().as_list()
net = tf.reshape(net, [-1, n_dim//n_split, before_reshaped_shape[1]*before_reshaped_shape[2]])
print("before LSTM", net.get_shape())
net = bidirectional_rnn(net, BasicLSTMCell(64), BasicLSTMCell(64))
print("after LSTM", net.get_shape())
net = dropout(net, 0.5)
# Regression
net = tflearn.fully_connected(net, 32, activation='sigmoid')
net = tflearn.fully_connected(net, 4, activation='softmax')
print("dense", net.get_shape())
net = tflearn.regression(net, optimizer='adam', loss='mean_square')
# Training
model = tflearn.DNN(net)
model.load('../model/mimic/mimic_model_offline_v4.1')
pred = []
num_of_test = len(test_data)
cur_data = []
for i in range(num_of_test):
cur_data.append(test_data[i])
if (i % 2000 == 0 or i == (num_of_test - 1)) and i !=0:
tmp_testX = np.array(cur_data, dtype=np.float32)
pred.extend(model.predict(tmp_testX.reshape([-1, n_dim, 1])))
cur_data = []
return pred
X, Y, valX, valY, testX, testY = data.train.images, data.train.labels, \
data.validation.images, data.validation.labels, \
data.test.images, data.test.labels
X = X.reshape([-1, 28, 28, 1])
valX = valX.reshape([-1, 28, 28, 1])
testX = testX.reshape([-1, 28, 28, 1])
X, mean = du.featurewise_zero_center(X)
valX = du.featurewise_zero_center(valX, mean)
testX = du.featurewise_zero_center(testX, mean)
# Building Residual Network
net = tflearn.input_data(shape=[None, 28, 28, 1])
net = tflearn.conv_2d(net, 64, 3, activation='relu', bias=False)
# Residual blocks
net = tflearn.residual_bottleneck(net, 3, 16, 64)
net = tflearn.residual_bottleneck(net, 1, 32, 128, downsample=True)
net = tflearn.residual_bottleneck(net, 2, 32, 128)
net = tflearn.residual_bottleneck(net, 1, 64, 256, downsample=True)
net = tflearn.residual_bottleneck(net, 2, 64, 256)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
# Regression
net = tflearn.fully_connected(net, 10, activation='softmax')
net = tflearn.regression(net,
optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.1)
# Training
model = tflearn.DNN(net,
def get_resNet_proba(long_data, long_pid, model_path):
all_pid = np.array(long_pid)
all_feature = np.array(long_data)
all_label = np.array([])
test_data, test_label, test_pid = slide_and_cut(all_feature, all_label,
all_pid)
pid_map = {}
for i in range(len(all_pid)):
pid_map[all_pid[i]] = i
n_dim = 6000
n_split = 300
tf.reset_default_graph()
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# Building Residual Network
net = tflearn.input_data(shape=[None, n_dim, 1])
############ reshape for sub_seq
net = tf.reshape(net, [-1, n_split, 1])
net = tflearn.conv_1d(net, 64, 16, 2)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
# Residual blocks
net = tflearn.residual_bottleneck(net,
2,
16,
64,
downsample_strides=2,
downsample=True,
is_first_block=True)
net = tflearn.residual_bottleneck(net,
2,
16,
64,
downsample_strides=2,
downsample=True)
net = tflearn.residual_bottleneck(net,
2,
16,
128,
downsample_strides=2,
downsample=True)
net = tflearn.residual_bottleneck(net,
2,
16,
128,
downsample_strides=2,
downsample=True)
net = tflearn.residual_bottleneck(net,
2,
16,
256,
downsample_strides=2,
downsample=True)
net = tflearn.residual_bottleneck(net,
2,
16,
256,
downsample_strides=2,
downsample=True)
net = tflearn.residual_bottleneck(net,
2,
16,
512,
downsample_strides=2,
downsample=True)
net = tflearn.residual_bottleneck(net,
2,
16,
512,
downsample_strides=2,
downsample=True)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
#net = tflearn.global_avg_pool(net)
# LSTM
############ reshape for sub_seq
net = tf.reshape(net, [-1, n_dim // n_split, 512])
net = bidirectional_rnn(net, BasicLSTMCell(256), BasicLSTMCell(256))
net = dropout(net, 0.5)
# Regression
feature_layer = tflearn.fully_connected(net, 32, activation='sigmoid')
net = tflearn.dropout(feature_layer, 0.5)
net = tflearn.fully_connected(net, 4, activation='softmax')
net = tflearn.regression(
net,
optimizer='adam', #momentum',
loss='categorical_crossentropy')
#,learning_rate=0.1)
## save model
### load
model = tflearn.DNN(net)
model.load(model_path)
### create new model, and get features
num_of_test = len(test_data)
cur_data = []
pre = []
for i in range(num_of_test):
cur_data.append(test_data[i])
if (num_of_test > 1 and (i % 2000 == 0 or i == (num_of_test - 1))
and i != 0) or (num_of_test == 1):
tmp_testX = np.array(cur_data, dtype=np.float32)
cur_data = []
pre.extend(model.predict(tmp_testX))
test_pid = np.array(test_pid, dtype=np.string_)
y_num = len(pid_map)
y_pre = [[0. for j in range(4)] for i in range(y_num)]
y_sec_pre = [[0. for j in range(4)] for i in range(y_num)]
y_third_pre = [[0. for j in range(4)] for i in range(y_num)]
#print(y_num)
for j in range(len(pre)):
i_pred = np.array(pre[j], dtype=np.float32)
cur_pid = str(test_pid[j], 'utf-8')
list_id = pid_map[cur_pid]
temp_pre = np.array(y_pre[list_id], dtype=np.float32)
temp_sec_pre = np.array(y_sec_pre[list_id], dtype=np.float32)
temp_third_pre = np.array(y_third_pre[list_id], dtype=np.float32)
max_p = temp_pre[np.argmax(temp_pre)]
max_sec_p = temp_sec_pre[np.argmax(temp_sec_pre)]
max_third_p = temp_third_pre[np.argmax(temp_third_pre)]
sec_p = 0
sec_sec_p = 0
sec_third_p = 0
for k in range(len(temp_pre)):
if temp_pre[k] == max_p:
continue
if temp_pre[k] > sec_p:
sec_p = temp_pre[k]
if temp_sec_pre[k] == max_sec_p:
continue
if temp_sec_pre[k] > sec_sec_p:
sec_sec_p = temp_sec_pre[k]
if temp_third_pre[k] == max_third_p:
continue
if temp_third_pre[k] > sec_third_p:
sec_third_p = temp_third_pre[k]
cur_max_p = i_pred[np.argmax(i_pred)]
cur_sec_p = 0
for k in range(len(i_pred)):
if i_pred[k] == cur_max_p:
continue
if i_pred[k] > cur_sec_p:
cur_sec_p = i_pred[k]
if (cur_max_p - cur_sec_p) > (max_p - sec_p):
y_third_pre[list_id] = y_sec_pre[list_id]
y_sec_pre[list_id] = y_pre[list_id]
y_pre[list_id] = i_pred
elif (cur_max_p - cur_sec_p) > (max_sec_p - sec_sec_p):
y_third_pre[list_id] = y_sec_pre[list_id]
y_sec_pre[list_id] = i_pred
elif (cur_max_p - cur_sec_p) > (max_third_p - sec_third_p):
y_third_pre[list_id] = i_pred
for k in range(len(y_pre)):
labels = [0. for j in range(4)]
pred_1 = np.argmax(y_pre[k])
labels[pred_1] += 1
pred_2 = np.argmax(y_sec_pre[k])
labels[pred_2] += 1
pred_3 = np.argmax(y_third_pre[k])
labels[pred_3] += 1
if pred_1 == 2: # and (abs(y_pre[k][np.argmax(labels)] - y_pre[k][2])/y_pre[k][np.argmax(labels)] <= 0.2):
continue
elif pred_2 == 2: # and (abs(y_pre[k][np.argmax(labels)] - y_sec_pre[k][2])/y_pre[k][np.argmax(labels)] <= 0.2):
y_pre[k] = y_sec_pre[k]
elif pred_3 == 2: # and (abs(y_pre[k][np.argmax(labels)] - y_third_pre[k][2])/y_pre[k][np.argmax(labels)] <= 0.2):
y_pre[k] = y_third_pre[k]
elif pred_1 != np.argmax(labels):
if pred_2 == np.argmax(labels):
y_pre[k] = y_sec_pre[k]
return y_pre
# Residual blocks
'''net = tflearn.residual_bottleneck(net, 2, 16, 64, downsample_strides = 2, downsample=True, is_first_block = True)
print("resn2", net.get_shape())
net = tflearn.residual_bottleneck(net, 2, 16, 128, downsample_strides = 2, downsample=True)
print("resn4", net.get_shape())
net = tflearn.residual_bottleneck(net, 2, 16, 256, downsample_strides = 2, downsample=True)
print("resn6", net.get_shape())
net = tflearn.residual_bottleneck(net, 2, 16, 512, downsample_strides = 2, downsample=True)
print("resn8", net.get_shape())
net = tflearn.residual_bottleneck(net, 2, 16, 1024, downsample_strides = 2, downsample=True)
print("resn10", net.get_shape())'''
net = tflearn.residual_bottleneck(net,
2,
16,
64,
downsample_strides=2,
downsample=True,
is_first_block=True)
print("resn2", net.get_shape())
net = tflearn.residual_bottleneck(net,
2,
16,
64,
downsample_strides=2,
downsample=True)
print("resn4", net.get_shape())
net = tflearn.residual_bottleneck(net,
2,
16,
128,
def get_model():
n_dim = 6000
n_split = 300
tf.reset_default_graph()
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
### split
#X = X.reshape([-1, n_split, 1])
#testX = testX.reshape([-1, n_split, 1])
# Building Residual Network
net = tflearn.input_data(shape=[None, n_dim, 1])
print("input", net.get_shape())
############ reshape for sub_seq
net = tf.reshape(net, [-1, n_split, 1])
print("reshaped input", net.get_shape())
net = tflearn.conv_1d(net, 64, 16, 2)
#net = tflearn.conv_1d(net, 64, 16, 2, regularizer='L2', weight_decay=0.0001)
print("cov1", net.get_shape())
net = tflearn.batch_normalization(net)
print("bn1", net.get_shape())
net = tflearn.activation(net, 'relu')
print("relu1", net.get_shape())
# Residual blocks
'''net = tflearn.residual_bottleneck(net, 2, 16, 64, downsample_strides = 2, downsample=True, is_first_block = True)
print("resn2", net.get_shape())
net = tflearn.residual_bottleneck(net, 2, 16, 128, downsample_strides = 2, downsample=True)
print("resn4", net.get_shape())
net = tflearn.residual_bottleneck(net, 2, 16, 256, downsample_strides = 2, downsample=True)
print("resn6", net.get_shape())
net = tflearn.residual_bottleneck(net, 2, 16, 512, downsample_strides = 2, downsample=True)
print("resn8", net.get_shape())
net = tflearn.residual_bottleneck(net, 2, 16, 1024, downsample_strides = 2, downsample=True)
print("resn10", net.get_shape())'''
net = tflearn.residual_bottleneck(net,
2,
16,
64,
downsample_strides=2,
downsample=True,
is_first_block=True)
print("resn2", net.get_shape())
net = tflearn.residual_bottleneck(net,
2,
16,
64,
downsample_strides=2,
downsample=True)
print("resn4", net.get_shape())
net = tflearn.residual_bottleneck(net,
2,
16,
128,
downsample_strides=2,
downsample=True)
print("resn6", net.get_shape())
net = tflearn.residual_bottleneck(net,
2,
16,
128,
downsample_strides=2,
downsample=True)
print("resn8", net.get_shape())
net = tflearn.residual_bottleneck(net,
2,
16,
256,
downsample_strides=2,
downsample=True)
print("resn10", net.get_shape())
net = tflearn.residual_bottleneck(net,
2,
16,
256,
downsample_strides=2,
downsample=True)
print("resn12", net.get_shape())
net = tflearn.residual_bottleneck(net,
2,
16,
512,
downsample_strides=2,
downsample=True)
print("resn14", net.get_shape())
net = tflearn.residual_bottleneck(net,
2,
16,
512,
downsample_strides=2,
downsample=True)
print("resn16", net.get_shape())
'''net = tflearn.residual_bottleneck(net, 2, 16, 1024, downsample_strides = 2, downsample=True)
print("resn18", net.get_shape())
net = tflearn.residual_bottleneck(net, 2, 16, 1024, downsample_strides = 2, downsample=True)
print("resn20", net.get_shape())'''
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
#net = tflearn.global_avg_pool(net)
# LSTM
print("before LSTM, before reshape", net.get_shape())
############ reshape for sub_seq
net = tf.reshape(net, [-1, n_dim // n_split, 512])
print("before LSTM", net.get_shape())
net = bidirectional_rnn(net, BasicLSTMCell(256), BasicLSTMCell(256))
print("after LSTM", net.get_shape())
#net = tflearn.layers.recurrent.lstm(net, n_units=512)
#print("after LSTM", net.get_shape())
net = dropout(net, 0.5)
# Regression
feature_layer = tflearn.fully_connected(net, 32, activation='sigmoid')
net = tflearn.dropout(feature_layer, 0.5)
net = tflearn.fully_connected(net, 4, activation='softmax')
print("dense", net.get_shape())
net = tflearn.regression(
net,
optimizer='adam', #momentum',
loss='categorical_crossentropy')
#,learning_rate=0.1)
## save model
### load
model = tflearn.DNN(net)
run_id = 'resnet_6000_500_10_5_v1'
model.load('../model/resNet/' + run_id)
all_names = tflearn.variables.get_all_variables()
print(all_names[0])
ttt = model.get_weights(all_names[0])
print(type(ttt))
print(ttt)
# tflearn.variables.get_value(all_names[0], xxx)
return all_names
