def init():
log.info('init called')
sess = tf.InteractiveSession()
loaded_model = MobileNet(input_shape=(
size, size, 1), alpha=1., weights=None, classes=NCATS)
loaded_model.load_weights('./doodle_classification/model/model.h5')
loaded_model.compile(optimizer=Adam(lr=0.002),
loss='categorical_crossentropy',
metrics=[categorical_crossentropy,
categorical_accuracy, top_3_accuracy])
# log.info(loaded_model.summary())
graph = tf.get_default_graph()
return loaded_model, sess, graph
class mobilenet:
def __init__(self, maxBin, maxBin2):
"""
must have self.model, self.params, and self._name
"""
self._name = 'MobileNet-imagenet'
self.model = MobileNet(include_top=True,
weights="imagenet",
input_shape=(224, 224, 3),
classes=1000)
self.params = {
'age hidden layer': 512,
'gender hidden layer': 1024,
'ethnicity hidden layer': 1024,
}
self.model = addOutputLayers(self.model, maxBin, maxBin2, self.params)
self.model.compile(optimizer='Adam', loss=losses, metrics=['accuracy'])
def name(self):
return self._name
model.summary()
# model.trainable = False
# model.save('C:/nmb/nmb_data/h5/5s/mobilenet_rmsprop_1.h5')
# 컴파일, 훈련
op = RMSprop(lr=1e-3)
batch_size = 8
es = EarlyStopping(monitor='val_loss',
patience=20,
restore_best_weights=True,
verbose=1)
lr = ReduceLROnPlateau(monitor='val_loss', vactor=0.5, patience=10, verbose=1)
path = 'C:/nmb/nmb_data/h5/5s/mobilenet/mobilenet_rmsprop_1.h5'
mc = ModelCheckpoint(path, monitor='val_loss', verbose=1, save_best_only=True)
model.compile(optimizer=op,
loss="sparse_categorical_crossentropy",
metrics=['acc'])
history = model.fit(x_train,
y_train,
epochs=1000,
batch_size=batch_size,
validation_split=0.2,
callbacks=[es, lr, mc])
# 평가, 예측
model = load_model('C:\\nmb\\nmb_data\\h5\\5s_last\\mobilenet_rmsprop_1.h5')
# model.load_weights('C:/nmb/nmb_data/h5/5s/mobilenet/mobilenet_rmsprop_1.h5')
result = model.evaluate(x_test, y_test, batch_size=8)
print("loss : {:.5f}".format(result[0]))
print("acc : {:.5f}".format(result[1]))
model.save('C:/nmb/nmb_data/h5/5s/mobilenet/mobilenet_rmsprop_1.h5')
# 컴파일, 훈련
op = RMSprop(lr=1e-3)
batch_size = 8
es = EarlyStopping(monitor='val_loss',
patience=20,
restore_best_weights=True,
verbose=1)
lr = ReduceLROnPlateau(monitor='val_loss', vactor=0.5, patience=10, verbose=1)
path = 'C:/nmb/nmb_data/h5/5s/mobilenet/mobilenet_rmsprop_1.h5'
mc = ModelCheckpoint(path, monitor='val_loss', verbose=1, save_best_only=True)
model.compile(optimizer=op,
loss="sparse_categorical_crossentropy",
metrics=['acc', f1_m, recall_m, precision_m])
history = model.fit(x_train,
y_train,
epochs=1000,
batch_size=batch_size,
validation_split=0.2,
callbacks=[es, lr, mc])
# 평가, 예측
# model = load_model('C:/nmb/nmb_data/h5/5s/mobilenet/mobilenet_rmsprop_1.h5')
model.load_weights('C:/nmb/nmb_data/h5/5s/mobilenet/mobilenet_rmsprop_1.h5')
result = model.evaluate(x_test, y_test, batch_size=8)
print("loss : {:.5f}".format(result[0]))
print("acc : {:.5f}".format(result[1]))
print("f1_score : {:.5f}".format(result[2]))
"""
Source: https://github.com/benhamner/Metrics/blob/master/Python/ml_metrics/average_precision.py
"""
return np.mean([apk(a, p, k) for a, p in zip(actual, predicted)])
def preds2catids(predictions):
return pd.DataFrame(np.argsort(-predictions, axis=1)[:, :3], columns=['a', 'b', 'c'])
def top_3_accuracy(y_true, y_pred):
return top_k_categorical_accuracy(y_true, y_pred, k=3)
STEPS = 800
EPOCHS = 16
size = 64
batchsize = 680
model = MobileNet(input_shape=(size, size, 1), alpha=1., weights=None, classes=NCATS)
model.compile(optimizer=Adam(lr=0.002), loss='categorical_crossentropy',
metrics=[categorical_crossentropy, categorical_accuracy, top_3_accuracy])
print(model.summary())
def draw_cv2(raw_strokes, size=256, lw=6, time_color=True):
img = np.zeros((BASE_SIZE, BASE_SIZE), np.uint8)
for t, stroke in enumerate(raw_strokes):
for i in range(len(stroke[0]) - 1):
color = 255 - min(t, 10) * 13 if time_color else 255
_ = cv2.line(img, (stroke[0][i], stroke[1][i]),
(stroke[0][i + 1], stroke[1][i + 1]), color, lw)
if size != BASE_SIZE:
return cv2.resize(img, (size, size))
else:
return img
def image_generator_xd(size, batchsize, ks, lw=6, time_color=True):
while True:
def mobilenet_seg_model(pretrained_weights=None, input_size=(256, 256, 3)):
model = MobileNet(input_shape=input_size,
weights='imagenet',
include_top=False)
x = model.output
encoder = Model(inputs=model.input, outputs=x)
skip_layer_1 = encoder.get_layer('conv_pw_1_relu').output
skip_layer_2 = encoder.get_layer('conv_pw_3_relu').output
skip_layer_3 = encoder.get_layer('conv_pw_5_relu').output
skip_layer_4 = encoder.get_layer('conv_pw_11_relu').output
up6 = Conv2D(
512,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal')(
x) #(UpSampling2D(size = (2,2), interpolation='bilinear')(x))
up6 = Conv2DTranspose(512,
2,
activation='relu',
padding='same',
strides=2,
kernel_initializer='he_normal')(up6)
merge6 = concatenate([skip_layer_4, up6], axis=3)
conv6 = Conv2D(512,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge6)
conv6 = Conv2D(512,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv6)
up7 = Conv2D(
256,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal'
)(conv6) #(UpSampling2D(size = (2,2), interpolation='bilinear')(conv6))
up7 = Conv2DTranspose(256,
2,
activation='relu',
padding='same',
strides=2,
kernel_initializer='he_normal')(up7)
merge7 = concatenate([skip_layer_3, up7], axis=3)
conv7 = Conv2D(256,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge7)
conv7 = Conv2D(256,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv7)
up8 = Conv2D(
128,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal'
)(conv7) #(UpSampling2D(size = (2,2), interpolation='bilinear')(conv7))
up8 = Conv2DTranspose(128,
2,
activation='relu',
padding='same',
strides=2,
kernel_initializer='he_normal')(up8)
merge8 = concatenate([skip_layer_2, up8], axis=3)
conv8 = Conv2D(128,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge8)
conv8 = Conv2D(128,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv8)
up9 = Conv2D(
64,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal'
)(conv8) #(UpSampling2D(size = (2,2), interpolation='bilinear')(conv8))
up9 = Conv2DTranspose(64,
2,
activation='relu',
padding='same',
strides=2,
kernel_initializer='he_normal')(up9)
merge9 = concatenate([skip_layer_1, up9], axis=3)
conv9 = Conv2D(64,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge9)
conv9 = Conv2D(64,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv9)
up10 = Conv2D(
32,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal'
)(conv9) #(UpSampling2D(size = (2,2), interpolation='bilinear')(conv9))
up10 = Conv2DTranspose(32,
2,
activation='relu',
padding='same',
strides=2,
kernel_initializer='he_normal')(up10)
#merge9 = concatenate([conv1,up9], axis = 3)
conv10 = Conv2D(32,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(up10)
conv10 = Conv2D(32,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv10)
conv10 = Conv2D(16,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv10)
conv10 = Conv2D(1, 1, activation='sigmoid')(conv10)
model = Model(inputs=model.input, outputs=conv10)
#model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics=[my_IoU])
#model.compile(optimizer = Adam(lr = 1e-4), loss = dice_coef_loss, metrics=[my_IoU])
model.compile(optimizer=Adam(lr=1e-4),
loss=weighted_bce_dice_loss,
metrics=[my_IoU])
model.summary()
if (pretrained_weights is not None):
model.load_weights(pretrained_weights)
return model
def build_model(mode, model_name=None, model_path=None):
clear_session()
if mode == 'train':
img = Input(
shape=(96, 96,
3)) # ResNet50 minimum size (32,32) for others (128,128)
if model_name == 'DenseNet121': #Checked and Working
model = DenseNet121(include_top=False,
weights='imagenet',
input_tensor=img,
input_shape=None,
pooling='avg')
elif model_name == 'MobileNet': #checked, raised shape error, #Error Resolved, Now working
model = MobileNet(include_top=True,
weights='imagenet',
input_tensor=img,
input_shape=None,
pooling='avg')
elif model_name == 'Xception': #Checked and Working
model = Xception(include_top=False,
weights='imagenet',
input_tensor=img,
input_shape=None,
pooling='max')
elif model_name == 'ResNet50': #Image Dimesion size should be high eg 224x224, not sufficient GPU memory resource
model = ResNet50(include_top=False,
weights='imagenet',
input_tensor=img,
input_shape=None,
pooling='avg')
elif model_name == 'InceptionV3': #Checked and Working
model = InceptionV3(include_top=False,
weights='imagenet',
input_tensor=img,
input_shape=(None),
pooling='avg')
elif model_name == 'VGG19': #to be checked
model = InceptionV4(include_top=False,
weights='imagenet',
input_tensor=img,
input_shape=None,
pooling='avg')
elif model_name == 'VGG16': #Checked and Working
model = VGG16(include_top=False,
weights='imagenet',
input_tensor=img,
input_shape=(None),
pooling='max')
elif model_name == 'VGG19': #to be checked
model = VGG19(include_top=False,
weights='imagenet',
input_tensor=img,
input_shape=None,
pooling='avg')
final_layer = model.layers[-1].output
dense_layer_1 = Dense(128, activation='relu')(final_layer)
output_layer = Dense(4, activation='softmax')(dense_layer_1)
model = Model(inputs=[img], outputs=output_layer)
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
elif mode == 'inference':
model = load_model(model_path)
return model
