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Commit ae6f8ec4 authored by Luc Giffon's avatar Luc Giffon
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add steph model + vgg19 avec initialisation glorot

parent 71a6ff64
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......@@ -14,6 +14,21 @@ def build_lenet_model(input_shape):
model.add(Flatten())
return model
def build_steph_model(input_shape):
model = Sequential()
model.add(Conv2D(64, kernel_size=(4, 4), strides=(2, 2), padding='same', activation=None, input_shape=input_shape))
model.add(Activation('relu'))
model.add(Conv2D(128, kernel_size=(4, 4), strides=(2, 2), padding='same', activation=None))
model.add(Activation('relu'))
model.add(Conv2D(256, kernel_size=(4, 4), strides=(2, 2), padding='same', activation=None))
model.add(Activation('relu'))
model.add(Conv2D(512, kernel_size=(3, 3), strides=(1, 1), padding='same', activation=None))
model.add(BatchNormalization())
model.add(MaxPooling2D((2, 2)))
model.add(Activation('relu'))
model.add(Flatten())
return model
def build_vgg19_model(input_shape, weight_decay=0.0001):
model = Sequential()
......@@ -100,3 +115,90 @@ def build_vgg19_model(input_shape, weight_decay=0.0001):
model.add(Flatten(name='flatten'))
return model
def build_vgg19_model_glorot(input_shape, weight_decay=0.0001):
model = Sequential()
# Block 1
model.add(Conv2D(64, (3, 3), padding='same', kernel_regularizer=l2(weight_decay),
name='block1_conv1', input_shape=input_shape))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3), padding='same', kernel_regularizer=l2(weight_decay),
name='block1_conv2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool'))
# Block 2
model.add(Conv2D(128, (3, 3), padding='same', kernel_regularizer=l2(weight_decay),
name='block2_conv1'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(128, (3, 3), padding='same', kernel_regularizer=l2(weight_decay),
name='block2_conv2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool'))
# Block 3
model.add(Conv2D(256, (3, 3), padding='same', kernel_regularizer=l2(weight_decay),
name='block3_conv1'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(256, (3, 3), padding='same', kernel_regularizer=l2(weight_decay),
name='block3_conv2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(256, (3, 3), padding='same', kernel_regularizer=l2(weight_decay),
name='block3_conv3'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(256, (3, 3), padding='same', kernel_regularizer=l2(weight_decay),
name='block3_conv4'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool'))
# Block 4
model.add(Conv2D(512, (3, 3), padding='same', kernel_regularizer=l2(weight_decay),
name='block4_conv1'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(512, (3, 3), padding='same', kernel_regularizer=l2(weight_decay),
name='block4_conv2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(512, (3, 3), padding='same', kernel_regularizer=l2(weight_decay),
name='block4_conv3'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(512, (3, 3), padding='same', kernel_regularizer=l2(weight_decay),
name='block4_conv4'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool'))
# Block 5
model.add(Conv2D(512, (3, 3), padding='same', kernel_regularizer=l2(weight_decay),
name='block5_conv1'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(512, (3, 3), padding='same', kernel_regularizer=l2(weight_decay),
name='block5_conv2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(512, (3, 3), padding='same', kernel_regularizer=l2(weight_decay),
name='block5_conv3'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(512, (3, 3), padding='same', kernel_regularizer=l2(weight_decay),
name='block5_conv4'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool'))
model.add(Flatten(name='flatten'))
return model
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