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Colombo 2019-11-21 11:40:55 +04:00
parent 668513a507
commit 6b14741160
5 changed files with 375 additions and 31 deletions
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37
nnlib/VGGFace.py
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@ -6,43 +6,31 @@ def VGGFace():
img_input = Input(shape=(224,224,3) )
# Block 1
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='conv1_1')(
img_input)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='conv1_1')(img_input)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='conv1_2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool1')(x)
# Block 2
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='conv2_1')(
x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='conv2_2')(
x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='conv2_1')(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='conv2_2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool2')(x)
# Block 3
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='conv3_1')(
x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='conv3_2')(
x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='conv3_3')(
x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='conv3_1')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='conv3_2')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='conv3_3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool3')(x)
# Block 4
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv4_1')(
x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv4_2')(
x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv4_3')(
x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv4_1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv4_2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv4_3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool4')(x)
# Block 5
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv5_1')(
x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv5_2')(
x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv5_3')(
x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv5_1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv5_2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv5_3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool5')(x)
@ -58,7 +46,6 @@ def VGGFace():
model = Model(img_input, x, name='vggface_vgg16')
weights_path = keras.utils.data_utils.get_file('rcmalli_vggface_tf_vgg16.h5',
'https://github.com/rcmalli/keras-vggface/releases/download/v2.0/rcmalli_vggface_tf_vgg16.h5')
model.load_weights(weights_path, by_name=True)
return model