def build_network(self):
# 32 layers: n=5, 56 layers: n=9, 110 layers: n=18
n = 5
#https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_cifar10.py
print('[+] Building RESIDUAL NETWORK')
print ('[-] COLOR: ' + str(COLOR))
print('[-] BATH_SIZE' + str(BATH_SIZE_CONSTANT))
print('[-] EXPERIMENTAL_LABEL' + EXPERIMENTO_LABEL)
self.network = input_data(shape=[None, SIZE_FACE, SIZE_FACE, COLOR])
self.network = tflearn.conv_2d(self.network, 16, 3, regularizer='L2', weight_decay=0.0001)
self.network = tflearn.residual_block(self.network, n, 16)
self.network = tflearn.residual_block(self.network, 1, 32, downsample=True)
self.network = tflearn.residual_block(self.network, n-1, 32)
self.network = tflearn.residual_block(self.network, 1, 64, downsample=True)
self.network = tflearn.residual_block(self.network, n-1, 64)
self.network = tflearn.batch_normalization(self.network)
self.network = tflearn.activation(self.network, 'relu')
self.network = tflearn.global_avg_pool(self.network)
# Regression
self.network = tflearn.fully_connected(self.network, len(EMOTIONS), activation='softmax')
self.mom = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True)
self.network = tflearn.regression(self.network, optimizer=self.mom,
loss='categorical_crossentropy')
self.model = tflearn.DNN(
self.network,
checkpoint_path = CHECKPOINT_DIR,
max_checkpoints = 1,
tensorboard_dir = TENSORBOARD_DIR,
#best_checkpoint_path = CHECKPOINT_DIR_BEST,
tensorboard_verbose = 1
)
self.load_model()
def resnet(X, y):
y = tf.one_hot(y, 10)
network = tflearn.conv_2d(X, 16, 3, regularizer="L2", weight_decay=0.0001)
network = tflearn.residual_block(network, 5, 16)
network = tflearn.residual_block(network, 1, 32, downsample=True)
network = tflearn.residual_block(network, 5 - 1, 32)
network = tflearn.residual_block(network, 1, 64, downsample=True)
network = tflearn.residual_block(network, 5 - 1, 64)
network = tflearn.batch_normalization(network)
network = tflearn.activation(network, "relu")
network = tflearn.global_avg_pool(network)
logits = tflearn.fully_connected(network, 10, activation="softmax")
loss = tflearn.categorical_crossentropy(logits, y)
step_tensor = tf.Variable(0., name="Training_step", trainable=False)
optimizer = tflearn.Momentum(0.1,
lr_decay=0.1,
decay_step=32000,
staircase=True)
optimizer.build(step_tensor)
train_op = optimizer().minimize(loss)
return logits, loss, train_op
def conv_res_integrated(img_size, lr, n, img_aug):
tf.reset_default_graph()
convnet = input_data(shape=[None, img_size, img_size, 1], name='input', data_augmentation=img_aug)
# conv layer 1 w/max pooling
conv1 = conv_2d(convnet, 32, 2, activation='relu', regularizer='L2', weight_decay=0.0001)
conv1 = max_pool_2d(conv1, 2)
# conv layer 2 w/max pooling etc
conv2 = conv_2d(conv1, 32, 2, activation='relu', regularizer='L2', weight_decay=0.0001)
conv2 = max_pool_2d(conv2, 2)
conv3 = conv_2d(conv2, 64, 2, activation='relu', regularizer='L2', weight_decay=0.0001)
conv3 = max_pool_2d(conv3, 2)
conv4 = conv_2d(conv3, 64, 2, activation='relu', regularizer='L2', weight_decay=0.0001)
conv4 = max_pool_2d(conv4, 2)
# residual block
res1 = residual_block(conv4, n, 128, downsample=True, regularizer='L2', weight_decay=0.0001)
batch_norm = batch_normalization(res1)
activ = tflearn.activation(batch_norm, 'relu')
gap = tflearn.global_avg_pool(activ)
# fully connected layer 1
fc1 = fully_connected(gap, 1024, activation='relu', regularizer='L2', weight_decay=0.0001)
fc1 = dropout(fc1, 0.85)
# fully connected layer 2
fc2 = tflearn.fully_connected(fc1, 2, activation='softmax')
# output layer
mom = tflearn.Momentum(0.1, lr_decay=0.01, decay_step=32000, staircase=True)
output = tflearn.regression(fc2, optimizer=mom, learning_rate=lr, loss='categorical_crossentropy')
# Training
model = tflearn.DNN(output, checkpoint_path='model_integrated',
max_checkpoints=2, tensorboard_verbose=0,
tensorboard_dir='log', clip_gradients=0.)
return model
def res_graph(X):
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
img_aug = ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_rotation(max_angle=25.)
img_aug.add_random_crop([64, 64], padding=4)
n = 5
net = input_data(shape=[None, 64, 64, 3], data_preprocessing=img_prep, data_augmentation=img_aug)
net = tflearn.conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001)
net = tflearn.residual_block(net, n, 16)
net = tflearn.residual_block(net, 1, 32, downsample=True)
net = tflearn.residual_block(net, n - 1, 32)
net = tflearn.residual_block(net, 1, 64, downsample=True)
net = tflearn.residual_block(net, n - 1, 64)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
net = tflearn.fully_connected(net, 2, activation='softmax')
mom = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True)
net = tflearn.regression(net, optimizer=mom,
loss='categorical_crossentropy')
model = tflearn.DNN(net)
# rnn typo -> res
model.load('model\\res\\jun_rnn_cat_dog.tflearn')
res_result = model.predict(X)
return res_result
def create_dual_network(self, inputs, s_dim):
with tf.variable_scope(self.scope + '-dual', reuse=self.reuse):
split_array = []
for i in range(s_dim[0]):
tmp = tf.reshape(inputs[:, i:i + 1, :], (-1, s_dim[1], 1))
branch1 = tflearn.conv_1d(tmp,
FEATURE_NUM,
3,
activation='relu')
branch2 = tflearn.conv_1d(tmp,
FEATURE_NUM,
4,
activation='relu')
branch3 = tflearn.conv_1d(tmp,
FEATURE_NUM,
5,
activation='relu')
network = tflearn.merge([branch1, branch2, branch3],
mode='concat',
axis=1)
network = tf.expand_dims(network, 2)
network = tflearn.global_avg_pool(network)
split_array.append(network)
#out, _ = self.attention(split_array, FEATURE_NUM)
out = tflearn.merge(split_array, 'concat')
self.reuse = True
return out
def create_Resnet(num_classes):
# Residual blocks
# 32 layers: n=5, 56 layers: n=9, 110 layers: n=18
n = 9
network = tflearn.input_data(
shape=[None, 64, 64,
3]) #, data_preprocessing=img_prep, data_augmentation=img_aug)
network = tflearn.conv_2d(network,
16,
3,
regularizer='L2',
weight_decay=0.0001)
network = tflearn.residual_block(network, n, 16)
network = tflearn.residual_block(network, 1, 32, downsample=True)
network = tflearn.residual_block(network, n - 1, 32)
network = tflearn.residual_block(network, 1, 64, downsample=True)
network = tflearn.residual_block(network, n - 1, 64)
network = tflearn.batch_normalization(network)
network = tflearn.activation(network, 'relu')
network = tflearn.global_avg_pool(network)
# Regression
network = tflearn.fully_connected(network,
num_classes,
activation='softmax')
mom = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True)
network = tflearn.regression(network,
optimizer=mom,
loss='categorical_crossentropy')
return network
def ResNet32(tensorWidth, tensorHeight, tensorDepth, tb_verb=0):
n = 5
net = tflearn.input_data(
shape=[None, tensorWidth, tensorHeight, tensorDepth])
net = tflearn.conv_2d(net,
16,
3,
regularizer='L2',
weight_decay=0.0001)
net = tflearn.residual_block(net, n, 16)
net = tflearn.residual_block(net, 1, 32, downsample=True)
net = tflearn.residual_block(net, n - 1, 32)
net = tflearn.residual_block(net, 1, 64, downsample=True)
net = tflearn.residual_block(net, n - 1, 64)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
# Regression
net = tflearn.fully_connected(net, 2, activation='softmax')
mom = tflearn.Momentum(0.1,
lr_decay=0.1,
decay_step=32000,
staircase=True)
net = tflearn.regression(net,
optimizer=mom,
loss='categorical_crossentropy')
# Training
model = tflearn.DNN(net,
checkpoint_path='model_resnet_bee1',
max_checkpoints=10,
tensorboard_verbose=tb_verb,
clip_gradients=0.)
return model
def build_network(self):
"""
Build the convnet.
Input is 48x48
3072 nodes in fully connected layer
"""
# Real-time data preprocessing
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center(
per_channel=True, mean=[0.53990436, 0.4405486, 0.39328504])
# Real-time data augmentation
img_aug = tflearn.ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_crop([49, 49], padding=4)
# Building Residual Network
self.network = tflearn.input_data(shape=[None, 49, 49, 3],
data_preprocessing=img_prep,
data_augmentation=img_aug)
self.network = tflearn.conv_2d(self.network,
16,
3,
regularizer='L2',
weight_decay=0.0001)
self.network = tflearn.resnext_block(self.network, 5, 16, 32)
self.network = tflearn.resnext_block(self.network,
1,
32,
32,
downsample=True)
self.network = tflearn.resnext_block(self.network, 4, 32, 32)
self.network = tflearn.resnext_block(self.network,
1,
64,
32,
downsample=True)
self.network = tflearn.resnext_block(self.network, 4, 64, 32)
self.network = tflearn.batch_normalization(self.network)
self.network = tflearn.activation(self.network, 'relu')
self.network = tflearn.global_avg_pool(self.network)
# Regression
self.network = tflearn.fully_connected(self.network,
11,
activation='softmax')
opt = tflearn.Momentum(0.1,
lr_decay=0.1,
decay_step=32000,
staircase=True)
self.network = tflearn.regression(self.network,
optimizer=opt,
loss='categorical_crossentropy')
# Training
self.model = tflearn.DNN(self.network,
checkpoint_path='Snapshots/model_resnext',
max_checkpoints=10,
tensorboard_verbose=0,
tensorboard_dir='Logs/',
clip_gradients=0.)
self.load_model()
def resnet():
# residual blocks: 32 layers: n=5, 56 layers: n=9, 110 layers: n=18
n = 5
# building Residual Network
network = tflearn.input_data(shape=[None, 128, 128, 3])
network = tflearn.conv_2d(network,
16,
3,
regularizer='L2',
weight_decay=0.0001)
network = tflearn.residual_block(network, n, 16)
network = tflearn.residual_block(network, 1, 32, downsample=True)
network = tflearn.residual_block(network, n - 1, 32)
network = tflearn.residual_block(network, 1, 64, downsample=True)
network = tflearn.residual_block(network, n - 1, 64)
network = tflearn.batch_normalization(network)
network = tflearn.activation(network, 'relu')
network = tflearn.global_avg_pool(network)
# regression
network = tflearn.fully_connected(network, 2, activation='softmax')
network = tflearn.regression(network,
optimizer='adam',
loss='categorical_crossentropy')
return network
def ResNet26(network, num_out, drop_prob=1.0):
n = 2 # number of residual blocks per layer
network = tflearn.conv_2d(network,
32,
7,
regularizer='L2',
strides=2,
activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = tflearn.residual_block(network, n, 32, activation='relu')
network = tflearn.residual_block(network, n, 32, activation='relu')
network = tflearn.residual_block(network,
n,
64,
downsample=True,
activation='relu')
network = tflearn.residual_block(network, n, 64, activation='relu')
network = tflearn.residual_block(network, n, 128, activation='relu')
network = tflearn.residual_block(network, n, 128, activation='relu')
network = batch_normalization(network)
network = activation(network, 'relu')
network = global_avg_pool(network)
network = tflearn.fully_connected(network, num_out, activation='softmax')
return network
def DenseNet(network):
# Growth Rate (12, 16, 32, ...)
k = 3
# Depth (40, 100, ...)
L = 28
nb_layers = int((L - 4) / 3)
# Building DenseNet Network
network = tflearn.conv_2d(network,
10,
4,
regularizer='L2',
weight_decay=0.0001)
network = denseblock(network, nb_layers, k, dropout=drop_prob)
network = denseblock(network, nb_layers, k, dropout=drop_prob)
network = denseblock(network, nb_layers, k, dropout=drop_prob)
network = tflearn.global_avg_pool(network)
# Regression
network = tflearn.fully_connected(network, 4, activation='softmax')
return network
def buildNetwork(n, k):
# Real-time data preprocessing
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center(per_channel=True)
img_prep.add_featurewise_stdnorm()
# Real-time data augmentation
img_aug = tflearn.ImageAugmentation()
img_aug.add_random_flip_leftright()
# Building Residual Network
net = tflearn.input_data(shape=[None, b.IMG_WIDTH, b.IMG_HEIGHT, b.CHANNELS], data_preprocessing=img_prep,
data_augmentation=img_aug)
net = tflearn.conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001)
# wideresnet part
net = wideresnet_block(net, n, 16, k, downsample=True)
net = wideresnet_block(net, 1, 32, k, downsample=True)
net = wideresnet_block(net, n - 1, 32, k, downsample=True)
net = wideresnet_block(net, 1, 64, 2, downsample=True)
net = wideresnet_block(net, n - 1, 64, k, downsample=True)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
# Regression
net = tflearn.fully_connected(net, b.CLASS_3_NUMBER, activation='softmax')
mom = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True)
net = tflearn.regression(net, optimizer=mom, loss='categorical_crossentropy')
model = tflearn.DNN(net, tensorboard_verbose=0, clip_gradients=0., tensorboard_dir=b.PATH + 'log')
return model
def _model2():
global yTest, img_aug
tf.reset_default_graph()
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
net = input_data(shape=[None, inputSize, inputSize, dim],
name='input',
data_preprocessing=img_prep,
data_augmentation=img_aug)
n = 2
j = 64
'''
net = tflearn.conv_2d(net, j, 3, regularizer='L2', weight_decay=0.0001)
net = tflearn.residual_block(net, n, j)
net = tflearn.residual_block(net, 1, j*2, downsample=True)
net = tflearn.residual_block(net, n-1, j*2)
net = tflearn.residual_block(net, 1, j*4, downsample=True)
net = tflearn.residual_block(net, n-1, j*4)
net = tflearn.residual_block(net, 1, j*8, downsample=True)
net = tflearn.residual_block(net, n-1, j*8)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
'''
net = tflearn.conv_2d(net, j, 7, strides = 2, regularizer='L2', weight_decay=0.0001)
net = max_pool_2d(net, 2, strides=2)
net = tflearn.residual_block(net, n, j)
net = tflearn.residual_block(net, 1, j*2, downsample=True)
net = tflearn.residual_block(net, n-1, j*2)
net = tflearn.residual_block(net, 1, j*4, downsample=True)
net = tflearn.residual_block(net, n-1, j*4)
net = tflearn.residual_block(net, 1, j*8, downsample=True)
net = tflearn.residual_block(net, n-1, j*8)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
net = tflearn.fully_connected(net, len(yTest[0]), activation='softmax')
mom = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True)
net = tflearn.regression(net, optimizer=mom,
loss='categorical_crossentropy')
model = tflearn.DNN(net, checkpoint_path='model2_resnet',
max_checkpoints=10, tensorboard_verbose=3, clip_gradients=0.)
model.load(_path)
pred = model.predict(xTest)
df = pd.DataFrame(pred)
df.to_csv(_path + ".csv")
newList = pred.copy()
newList = convert2(newList)
if _CSV: makeCSV(newList)
pred = convert2(pred)
pred = convert3(pred)
yTest = convert3(yTest)
print(metrics.confusion_matrix(yTest, pred))
print(metrics.classification_report(yTest, pred))
print('Accuracy', accuracy_score(yTest, pred))
print()
if _wrFile: writeTest(pred)
def Define(): # less deep img_aug = tflearn.data_augmentation.ImageAugmentation() img_aug.add_random_flip_leftright() img_aug.add_random_crop((48, 48),6) img_aug.add_random_rotation(max_angle=30.) img_prep = tflearn.data_preprocessing.ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() n = 5 network = tflearn.input_data(shape=[None, 48, 48, 1], data_augmentation=img_aug, data_preprocessing=img_prep) #48 x 48 grayscale network = tflearn.conv_2d(network, 16, 3, regularizer='L2', weight_decay=0.0001) network = tflearn.residual_block(network, n, 16) network = tflearn.residual_block(network, 1, 32, downsample=True) network = tflearn.residual_block(network, n-1, 32) network = tflearn.residual_block(network, 1, 64, downsample=True) network = tflearn.residual_block(network, n-1, 64) network = tflearn.batch_normalization(network) network = tflearn.activation(network, 'relu') network = tflearn.global_avg_pool(network) # Regression network = tflearn.fully_connected(network, 7, activation='softmax') return network
def network(img_shape, name, LR):
# Building Residual Network
network = tflearn.input_data(shape=img_shape, name=name)
network = tflearn.conv_2d(network,
16,
3,
regularizer='L2',
weight_decay=0.0001)
network = tflearn.resnext_block(network, n, 16, 32)
network = tflearn.resnext_block(network, 1, 32, 32, downsample=True)
network = tflearn.resnext_block(network, n - 1, 32, 32)
network = tflearn.resnext_block(network, 1, 64, 32, downsample=True)
network = tflearn.resnext_block(network, n - 1, 64, 32)
network = tflearn.batch_normalization(network)
network = tflearn.activation(network, 'relu')
network = tflearn.global_avg_pool(network)
# Regression
network = tflearn.fully_connected(network, 2, activation='softmax')
opt = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True)
network = tflearn.regression(network,
optimizer=opt,
name='targets',
loss='categorical_crossentropy')
return network
def return2img():
n = 5
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center(per_channel=True)
# Real-time data augmentation
img_aug = tflearn.ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_crop([32, 32], padding=4)
# Building Residual Network
net = tflearn.input_data(shape=[None, 32, 32, 1],
data_preprocessing=img_prep,
data_augmentation=img_aug)
net = tflearn.conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001)
net = tflearn.residual_block(net, n, 16)
net = tflearn.residual_block(net, 1, 32, downsample=True)
net = tflearn.residual_block(net, n - 1, 32)
net = tflearn.residual_block(net, 1, 64, downsample=True)
net = tflearn.residual_block(net, n - 1, 64)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
# Regression
net = tflearn.fully_connected(net, 3, activation='softmax')
mom = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True)
net = tflearn.regression(net, optimizer=mom,
loss='categorical_crossentropy')
# Training
model = tflearn.DNN(net)
model.load("model_resnet_+-------cifar10-6500")
img = load_img_label('/media/bai/Elements/LiangData_Afterchoose/img1')
livermask = load_mask('/media/bai/Elements/LiangData_Afterchoose/mask1')
abc=[]
np.array(abc)
for i in range(img.shape[0]):
a = np.zeros((512,512,3,3),dtype=np.float32)#最后一项表示第几个分割方式，倒数第二项表示预测的三个结果
for j in range(0,3):
this_mask = livermask[i, :, :, j]
this_mask_num = int(np.max(this_mask))
for k in range(1,this_mask_num+1):
if os.path.exists('/media/bai/Elements/LiangData_Afterchoose/superpixel/'+str(i)+'_'+str(j)+'_'+str(k)+'.jpg'):
this_img = io.imread('/media/bai/Elements/LiangData_Afterchoose/superpixel/'+str(i)+'_'+str(j)+'_'+str(k)+'.jpg')
this_img = np.reshape(this_img,(1,32,32,1))
this_img = this_img.astype(np.float32)
result = model.predict(this_img)
# print(result)
abc.append(result)
thisPartLoc=np.where(this_mask==k)
for num in range(len(thisPartLoc[1])):
a[thisPartLoc[0][num],thisPartLoc[1][num],0,j] = result[0,0]
a[thisPartLoc[0][num],thisPartLoc[1][num], 1, j] = result[0,1]
a[thisPartLoc[0][num],thisPartLoc[1][num], 2, j] = result[0,2]
b = np.max(a,axis=3)
final = np.argmax(b,axis=2)
#添加3D CRF
misc.imsave('/media/bai/Elements/LiangData_Afterchoose/result/' + str(i) + '.jpg', final)
def tflearn_imdb():
"""
文本情感分析
:return:
"""
(X_train, Y_train), (X_test, Y_test) = imdb.load_data()
X_train, Y_train = pad_sequences(Y_train,
maxlen=100), to_categorical(Y_train,
nb_classes=2)
X_test, Y_test = pad_sequences(Y_test,
maxlen=100), to_categorical(Y_test,
nb_classes=2)
network = input_data([None, 100], name="input")
tflearn.embedding(network, input_dim=10000, output_dim=128)
branch1 = tflearn.conv_1d(network,
128,
3,
padding="valid",
activation="relu",
regularizer="L2")
branch2 = tflearn.conv_1d(network,
128,
4,
padding="valid",
activation="relu",
regularizer="L2")
branch3 = tflearn.conv_1d(network,
128,
5,
padding="valid",
activation="relu",
regularizer="L2")
network = tflearn.merge([branch1, branch2, branch3], mode="concat", axis=1)
network = tf.expand_dims(network, 2)
network = tflearn.global_avg_pool(network)
network = tflearn.dropout(network, 0.5)
network = tflearn.fully_connected(network, 2, activation="softmax")
network = tflearn.regression(network,
optimizer="adam",
learning_rate=0.001,
loss="categorical_crossentropy",
name="target")
model = tflearn.DNN(network, tensorboard_verbose=0)
model.fit(X_train,
Y_train,
n_epoch=5,
shuffle=True,
validation_set=(X_test, Y_test),
show_metric=True,
batch_size=32)
def run(self):
# Real-time pre-processing of the image data
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
# Real-time data augmentation
img_aug = tflearn.ImageAugmentation()
img_aug.add_random_flip_leftright()
# Resnet model below: Adapted from tflearn website
self.n = 5 #32 layer resnet
# Building Residual Network
net = tflearn.input_data(shape=[None, 48, 48, 1], data_preprocessing=img_prep, data_augmentation=img_aug)
net = tflearn.conv_2d(net, nb_filter=16, filter_size=3, regularizer='L2', weight_decay=0.0001)
net = tflearn.residual_block(net, self.n, 16)
net = tflearn.residual_block(net, 1, 32, downsample=True)
net = tflearn.residual_block(net, self.n - 1, 32)
net = tflearn.residual_block(net, 1, 64, downsample=True)
net = tflearn.residual_block(net, self.n - 1, 64)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
# Regression
net = tflearn.fully_connected(net, 7, activation='softmax')
mom = tflearn.Momentum(learning_rate=0.1, lr_decay=0.0001, decay_step=32000, staircase=True, momentum=0.9)
net = tflearn.regression(net, optimizer=mom,
loss='categorical_crossentropy')
self.model = tflearn.DNN(net, checkpoint_path='models/model_resnet_emotion',
max_checkpoints=10, tensorboard_verbose=0,
clip_gradients=0.)
self.model.load('model.tfl')
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
cap = cv2.VideoCapture(0)
#Main Loop where we will be capturing live webcam feed, crop image and process the image for emotion recognition on trained model
while True:
ret, img = cap.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
roi_gray = gray[y:y + h, x:x + w]
roi_color = img[y:y + h, x:x + w]
self.image_processing(roi_gray, img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
def build_net():
n = 5
tflearn.config.init_training_mode()
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
# Real-time data augmentation
img_aug = tflearn.ImageAugmentation()
img_aug.add_random_flip_leftright()
# img_aug.add_random_crop([48, 48], padding=8)
# Building Residual Network
net = tflearn.input_data(shape=[None, 48, 48, 1],
data_preprocessing=img_prep,
data_augmentation=img_aug)
net = tflearn.conv_2d(net,
nb_filter=16,
filter_size=3,
regularizer='L2',
weight_decay=0.0001)
net = tflearn.residual_block(net, n, 16)
net = tflearn.residual_block(net, 1, 32, downsample=True)
net = tflearn.residual_block(net, n - 1, 32)
net = tflearn.residual_block(net, 1, 64, downsample=True)
net = tflearn.residual_block(net, n - 1, 64)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
# Regression
net = tflearn.fully_connected(net, 7, activation='softmax')
mom = tflearn.Momentum(learning_rate=0.1,
lr_decay=0.0001,
decay_step=32000,
staircase=True,
momentum=0.9)
net = tflearn.regression(net,
optimizer=mom,
loss='categorical_crossentropy')
print("make model")
model = tflearn.DNN(
net,
checkpoint_path='upload/Resmodels/model_resnet_emotion',
max_checkpoints=10,
tensorboard_verbose=0,
clip_gradients=0.)
print("load model start")
model.load('upload/Resmodels/model_resnet_emotion-10500')
print("load model success")
return model
def inference(input_data, n_classes):
#stem
net = conv_2d(input_data,
32,
3,
strides=2,
activation='softplus',
name='conv1')
net = conv_2d(net, 32, 3, activation='softplus', name='conv2')
net = conv_2d(net, 64, 3, activation='softplus', name='conv3')
net1 = max_pool_2d(net, 3, strides=2, name='pool4')
net2 = conv_2d(net, 96, 3, strides=2, activation='softplus', name='conv4')
merge5 = tflearn.layers.merge_ops.merge([net1, net2],
mode='concat',
axis=3)
net6_1 = conv_2d(merge5, 64, 1, activation='softplus', name='net6_1')
net7_1 = conv_2d(net6_1, 96, 3, activation='softplus', name='net6_1')
net6_2 = conv_2d(merge5, 64, 1, activation='softplus', name='net6_2')
net7_2 = conv_2d(net6_2,
64,
filter_size=[1, 7],
activation='softplus',
name='net7_2')
net8_2 = conv_2d(net7_2,
64,
filter_size=[7, 1],
activation='softplus',
name='net8_2')
net10_2 = conv_2d(net8_2, 96, 3, activation='softplus', name='net10_2')
merge = tflearn.layers.merge_ops.merge([net7_1, net10_2],
mode='concat',
axis=3)
net11_1 = conv_2d(merge, 192, 3, activation='softplus', name='net11_1')
net11_2 = max_pool_2d(merge, 1, name='net11_2')
x = tflearn.layers.merge_ops.merge([net11_1, net11_2],
mode='concat',
axis=3)
for i in range(3):
x = inception_A(x)
x = reduction_A(x)
for i in range(5):
x = inception_B(x)
x = reduction_B(x)
for i in range(3):
x = inception_C(x)
pool = tflearn.global_avg_pool(x)
# softmax
drop = tf.nn.dropout(pool, 0.8)
soft = fully_connected(drop, n_classes, activation=None)
soft = tf.nn.softmax(soft)
return soft
def resnet1(x, n=5):
net = tflearn.conv_2d(x, 16, 3, regularizer='L2', weight_decay=0.0001)
net = tflearn.residual_block(net, n, 16)
net = tflearn.residual_block(net, 1, 32, downsample=True)
net = tflearn.residual_block(net, n - 1, 32)
net = tflearn.residual_block(net, 1, 64, downsample=True)
net = tflearn.residual_block(net, n - 1, 64)
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')
return net
def resnet(inputs, prob_fc, prob_conv, wd, training_phase=True):
n = 5
net = tflearn.conv_2d(inputs, 16, 3, regularizer='L2', weight_decay=0.0001)
net = tflearn.residual_block(net, n, 16)
net = tflearn.residual_block(net, 1, 32, downsample=True)
net = tflearn.residual_block(net, n - 1, 32)
net = tflearn.residual_block(net, 1, 64, downsample=True)
net = tflearn.residual_block(net, n - 1, 64)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
# Regression
net = tflearn.fully_connected(net, 10, activation='linear')
return net
def get_model(model_name):
# First we load the network
print("Setting up neural networks...")
n = 18
# Real-time data preprocessing
print("Doing preprocessing...")
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center(
per_channel=True, mean=[0.573364, 0.44924123, 0.39455055])
# Real-time data augmentation
print("Building augmentation...")
img_aug = tflearn.ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_crop([32, 32], padding=4)
#Build the model (for 32 x 32)
print("Shaping input data...")
net = tflearn.input_data(shape=[None, 32, 32, 3],
data_preprocessing=img_prep,
data_augmentation=img_aug)
net = tflearn.conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001)
print("Carving Resnext blocks...")
net = tflearn.resnext_block(net, n, 16, 32)
net = tflearn.resnext_block(net, 1, 32, 32, downsample=True)
net = tflearn.resnext_block(net, n - 1, 32, 32)
net = tflearn.resnext_block(net, 1, 64, 32, downsample=True)
net = tflearn.resnext_block(net, n - 1, 64, 32)
print("Erroding Gradient...")
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
net = tflearn.fully_connected(net, 8, activation='softmax')
opt = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True)
net = tflearn.regression(net,
optimizer=opt,
loss='categorical_crossentropy')
print("Structuring model...")
model = tflearn.DNN(net, tensorboard_verbose=0, clip_gradients=0.)
# Load the model from checkpoint
print("Loading the model...")
model.load(model_name)
return model
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 BReG_NeXt(_X):
"""BReG_NeXt implementation. Returns feature map before softmax.
"""
net = tflearn.conv_2d(_X, 32, 3, regularizer='L2', weight_decay=0.0001)
net = residual_block(net, 7, 32, activation='elu')
net = residual_block(net, 1, 64, downsample=True, activation='elu')
net = residual_block(net, 8, 64, activation='elu')
net = residual_block(net, 1, 128, downsample=True, activation='elu')
net = residual_block(net, 7, 128, activation='elu')
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'elu')
net = tflearn.global_avg_pool(net)
# Regression
logits = tflearn.fully_connected(net, n_classes, activation='linear')
return logits
def build_network():
# Real-time data preprocessing
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center(per_channel=True)
# Real-time data augmentation
img_aug = ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_crop([IMG_WIDTH, IMG_HEIGHT], padding=4)
#Ashis: transition layer didn't use here
#Hence, each densenet_block used same nb_layers and growth (k)
#transition layer needs to balance two adjacent densenet_block
#by default, dropout is set as false. Downsample is used as True
# Building Residual Network
net = input_data(shape=[None, IMG_WIDTH, IMG_HEIGHT, CHANNELS],
data_preprocessing=img_prep,
data_augmentation=img_aug)
net = conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001)
net = densenet_block(net, nb_layers, k)
#no transition layer
net = densenet_block(net, nb_layers, k)
#no transition layer
#net = densenet_block(net, nb_layers, k) #Ignore one
#no transition layer
net = tflearn.global_avg_pool(net)
# Regression
net = fully_connected(net, CLASS_NUMBER, activation='softmax')
#opt = tflearn.optimizers.Nesterov(0.1, lr_decay=0.1, decay_step=32000, staircase=True)
#opt = tflearn.optimizers.AdaGrad (learning_rate=0.01, initial_accumulator_value=0.01)
net = regression(net,
optimizer="adam",
loss='categorical_crossentropy',
learning_rate=0.001)
# Training
model = tflearn.DNN(net,
checkpoint_path='model_densenet',
max_checkpoints=10,
tensorboard_verbose=0,
clip_gradients=0.)
return model
def resnet(dataset, img_prep, img_aug, X, Y, testX, testY, width, height,
channel, class_num, filt, depth, epoch):
# Building Residual Network
layer = 1
net = tflearn.input_data(shape=[None, width, height, channel],
data_preprocessing=img_prep,
data_augmentation=img_aug)
net = tflearn.conv_2d(net, filt, 3, regularizer='L2', weight_decay=0.0001)
while (depth != 0):
d_num = depth - (int)(depth / 100) * 100
depth = (int)(depth / 100)
if (layer == 1):
net = tflearn.residual_block(net, d_num, filt)
else:
net = tflearn.residual_block(net, 1, filt, downsample=True)
net = tflearn.residual_block(net, d_num - 1, filt)
layer = layer + 1
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
# Regression
net = tflearn.fully_connected(net, class_num, activation='softmax')
mom = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True)
net = tflearn.regression(net,
optimizer=mom,
loss='categorical_crossentropy')
# Training
model = tflearn.DNN(net,
checkpoint_path=('model_resnet_' + dataset),
max_checkpoints=10,
tensorboard_verbose=0,
clip_gradients=0.)
model.fit(X,
Y,
n_epoch=epoch,
validation_set=(testX, testY),
snapshot_epoch=False,
snapshot_step=500,
show_metric=True,
batch_size=128,
shuffle=True,
run_id=('resnet_' + dataset))
aa = model.predict(testX)
correct = 0
for i in range(len(aa)):
if (aa[i].index(max(aa[i])) == np.argmax(testY[i])):
correct = correct + 1
return correct / len(aa)
def inference(x):
n = 5
net = tflearn.conv_2d(x, 16, 3, regularizer='L2', weight_decay=0.0001)
net = tflearn.resnext_block(net, n, 16, 32)
net = tflearn.resnext_block(net, 1, 32, 32, downsample=True)
net = tflearn.resnext_block(net, n - 1, 32, 32)
net = tflearn.resnext_block(net, 1, 64, 32, downsample=True)
net = tflearn.resnext_block(net, n - 1, 64, 32)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
# Regression
net = tflearn.fully_connected(net, 5, activation=None)
logits = net
net = tflearn.activations.softmax(net)
return logits, net
def ResNext1(network):
# Residual blocks
# 32 layers: n=5, 56 layers: n=9, 110 layers: n=18
n = 5
network = conv_2d(network, 16, 3, regularizer='L2', weight_decay=0.0001)
network = resnext_block(network, n, 16, 32)
network = resnext_block(network, 1, 32, 32, downsample=True)
network = resnext_block(network, n - 1, 32, 32)
network = resnext_block(network, 1, 32, 32, downsample=True)
network = resnext_block(network, n - 1, 32, 32)
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 inference(inputs, n_class=1000, finetuning=False):
block_list = [10, 20, 9]
scale_list = [0.17, 0.10, 0.20]
print(inputs)
net = stem(inputs, scope='stem')
print(net)
# bloack A
for i in range(block_list[0]):
net = blockA(net, scale=scale_list[0], scope='A_' + str(i))
print(net)
# reduction A
net = reductionA(net)
# bloack B
for i in range(block_list[1]):
net = blockB(net, scale=scale_list[1], scope='B_' + str(i))
print(net)
# reduction B
net = reductionB(net)
print(net)
# bloack C
for i in range(block_list[2]):
net = blockB(net, scale=scale_list[2], scope='C_' + str(i))
print(net)
net = tflearn.global_avg_pool(net)
print(net)
keep_prob = tf.cond(tflearn.get_training_mode(), lambda: 1.0, lambda: 0.8)
net = tflearn.dropout(net, keep_prob=keep_prob)
with tf.variable_scope('FC', reuse=tf.AUTO_REUSE):
net = tflearn.fully_connected(net,
n_class,
weights_init='uniform_scaling',
regularizer='L2',
weight_decay=0.0001,
restore=(not finetuning))
print(net)
return net
def _build_network(self, n, image_size):
# Define the input to the network.
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center(per_channel=True)
# Real-time data augmentation.
img_aug = tflearn.ImageAugmentation()
img_aug.add_random_flip_leftright()
net = tflearn.input_data(shape=[None, image_size[0], image_size[1], 3],
data_preprocessing=img_prep,
data_augmentation=img_aug)
# Start with a normal convolutional layer.
net = tflearn.conv_2d(net,
64,
3,
regularizer='L2',
weight_decay=0.0001)
# Since this is a ResNet with <50 layers, we'll use regular residual blocks;
# otherwise, we'd use residual bottleneck blocks instead.
net = tflearn.residual_block(net, n, 64)
net = tflearn.residual_block(net, 1, 128, downsample=True)
net = tflearn.residual_block(net, n - 1, 128)
net = tflearn.residual_block(net, 1, 256, downsample=True)
net = tflearn.residual_block(net, n - 1, 256)
# Perform batch normalization.
net = tflearn.batch_normalization(net)
# Activation at the end of the network pre-FC.
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
net = tflearn.fully_connected(net, 2, activation='softmax')
mom = tflearn.Momentum(0.1,
lr_decay=0.1,
decay_step=32000,
staircase=True)
net = tflearn.regression(net,
optimizer=mom,
loss='categorical_crossentropy')
return net
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 _model2():
global yTest, img_aug
tf.reset_default_graph()
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
net = input_data(shape=[None, inputSize, inputSize, dim],
name='input',
data_preprocessing=img_prep,
data_augmentation=img_aug)
n = 3
j = 64
'''
net = tflearn.conv_2d(net, j, 3, regularizer='L2', weight_decay=0.0001)
net = tflearn.residual_block(net, n, j)
net = tflearn.residual_block(net, 1, j*2, downsample=True)
net = tflearn.residual_block(net, n-1, j*2)
net = tflearn.residual_block(net, 1, j*4, downsample=True)
net = tflearn.residual_block(net, n-1, j*4)
net = tflearn.residual_block(net, 1, j*8, downsample=True)
net = tflearn.residual_block(net, n-1, j*8)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
'''
net = tflearn.conv_2d(net, j, 7, strides = 2, regularizer='L2', weight_decay=0.0001)
net = max_pool_2d(net, 2, strides=2)
net = tflearn.residual_block(net, n, j)
net = tflearn.residual_block(net, 1, j*2, downsample=True)
net = tflearn.residual_block(net, n-1, j*2)
net = tflearn.residual_block(net, 1, j*4, downsample=True)
net = tflearn.residual_block(net, n-1, j*4)
net = tflearn.residual_block(net, 1, j*8, downsample=True)
net = tflearn.residual_block(net, n-1, j*8)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
net = tflearn.fully_connected(net, len(Y[0]), activation='softmax')
mom = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True)
net = tflearn.regression(net, optimizer=mom,
loss='categorical_crossentropy')
model = tflearn.DNN(net, checkpoint_path='model_resnet_cifar10',
max_checkpoints=10, tensorboard_verbose=3, clip_gradients=0.)
model.fit(X, Y, n_epoch=epochNum, validation_set=(xTest, yTest),snapshot_epoch=False,
snapshot_step=500, show_metric=True, batch_size=batchNum, shuffle=True, run_id= _id + 'artClassification')
if modelStore: model.save(_id + '-model.tflearn')
def build_residual_network(self, network, res_n=5):
# data_augmentation=self.generate_image_augumentation())
network = tflearn.conv_2d(network, 16, 3, regularizer='L2',
weight_decay=0.0001)
network = tflearn.residual_block(network, res_n, 16)
network = tflearn.residual_block(network, 1, 32, downsample=True)
network = tflearn.residual_block(network, res_n - 1, 32)
network = tflearn.residual_block(network, 1, 64, downsample=True)
network = tflearn.residual_block(network, res_n - 1, 64)
network = tflearn.batch_normalization(network)
network = tflearn.activation(network, 'relu')
network = tflearn.global_avg_pool(network)
# Regression
network = tflearn.fully_connected(network, 2, activation='softmax')
mom = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000,
staircase=True)
network = tflearn.regression(network, optimizer=mom,
loss='categorical_crossentropy')
return network
def resnext(width, height, frame_count, lr, output=9, model_name = 'sentnet_color.model'):
net = input_data(shape=[None, width, height, 3], name='input')
net = tflearn.conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001)
net = tflearn.layers.conv.resnext_block(net, n, 16, 32)
net = tflearn.resnext_block(net, 1, 32, 32, downsample=True)
net = tflearn.resnext_block(net, n-1, 32, 32)
net = tflearn.resnext_block(net, 1, 64, 32, downsample=True)
net = tflearn.resnext_block(net, n-1, 64, 32)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
# Regression
net = tflearn.fully_connected(net, output, activation='softmax')
opt = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True)
net = tflearn.regression(net, optimizer=opt,
loss='categorical_crossentropy')
model = tflearn.DNN(net,
max_checkpoints=0, tensorboard_verbose=0, tensorboard_dir='log')
return model
testX = du.featurewise_zero_center(testX, mean)
testX = du.featurewise_std_normalization(testX, std)
Y = du.to_categorical(Y, 10)
testY = du.to_categorical(testY, 10)
# Building Residual Network
net = tflearn.input_data(shape=[None, 32, 32, 3])
net = tflearn.conv_2d(net, 32, 3)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.shallow_residual_block(net, 4, 32, regularizer='L2')
net = tflearn.shallow_residual_block(net, 1, 32, downsample=True,
regularizer='L2')
net = tflearn.shallow_residual_block(net, 4, 64, regularizer='L2')
net = tflearn.shallow_residual_block(net, 1, 64, downsample=True,
regularizer='L2')
net = tflearn.shallow_residual_block(net, 5, 128, regularizer='L2')
net = tflearn.global_avg_pool(net)
# Regression
net = tflearn.fully_connected(net, 10, activation='softmax')
mom = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=16000, staircase=True)
net = tflearn.regression(net, optimizer=mom,
loss='categorical_crossentropy')
# Training
model = tflearn.DNN(net, checkpoint_path='model_resnet_cifar10',
max_checkpoints=10, tensorboard_verbose=0,
clip_gradients=1.0)
model.fit(X, Y, n_epoch=200, validation_set=(testX, testY),
show_metric=True, batch_size=128, shuffle=True,
run_id='resnet_cifar10')
#Load Test data
image_count = (3,6)
patch_count = 20
X = generate_patches(img2numpy_arr(config.test_path), image_count, patch_count)
# Building Residual Network
net = tl.input_data(shape=[None, 42, 42, 3])
net = tl.conv_2d(net, 32, 3)
net = tl.batch_normalization(net)
net = tl.activation(net, 'relu')
net = tl.shallow_residual_block(net, 4, 32, regularizer='L2')
net = tl.shallow_residual_block(net, 1, 32, downsample=True,
regularizer='L2')
net = tl.shallow_residual_block(net, 4, 64, regularizer='L2')
net = tl.shallow_residual_block(net, 1, 64, downsample=True,
regularizer='L2')
net = tl.shallow_residual_block(net, 5, 64, regularizer='L2')
net = tl.global_avg_pool(net)
# Regression
net = tl.fully_connected(net, 9, activation='softmax')
mom = tl.Momentum(0.1, lr_decay=0.1, decay_step=16000, staircase=True)
net = tl.regression(net, optimizer=mom,
loss='categorical_crossentropy')
# Training
model = tl.DNN(net)
model.load('resnet_analysis-172500')
pred_y = model.predict(X)
print(pred_y)
