def myAlexNet2(weights_path=None):
inputs = Input(shape=(3, 227, 227))
conv_1 = Convolution2D(96,
11,
11,
subsample=(4, 4),
activation='relu',
name='conv_1')(inputs)
conv_2 = MaxPooling2D((3, 3), strides=(2, 2))(conv_1)
conv_2 = crosschannelnormalization(name="convpool_1")(conv_2)
conv_2 = ZeroPadding2D((2, 2))(conv_2)
conv_2 = merge([
Convolution2D(
128, 5, 5, activation="relu", name='conv_2_' + str(i + 1))(
splittensor(ratio_split=2, id_split=i)(conv_2))
for i in range(2)
],
mode='concat',
concat_axis=1,
name="conv_2")
conv_3 = MaxPooling2D((3, 3), strides=(2, 2))(conv_2)
conv_3 = crosschannelnormalization()(conv_3)
conv_3 = ZeroPadding2D((1, 1))(conv_3)
model = Model(input=inputs, output=conv_3)
if weights_path:
model.load_weights(weights_path)
return model
def AlexNet(weights_path=None, heatmap=False):
if heatmap:
inputs = Input(shape=(3,None,None))
# inputs = Input(shape=(None, None, 3))
else:
inputs = Input(shape=(3, 227, 227))
# inputs = Input(shape=(227, 227, 3))
conv_1 = Conv2D(96, 11, 11,strides=(4,4),activation='relu',
name='conv_1')(inputs)
conv_2 = MaxPooling2D((3, 3), strides=(2,2))(conv_1)
conv_2 = crosschannelnormalization(name="convpool_1")(conv_2)
conv_2 = ZeroPadding2D((2,2))(conv_2)
conv_2 = merge([
Conv2D(128,5,5,activation="relu",name='conv_2_'+str(i+1))(
splittensor(ratio_split=2,id_split=i)(conv_2)
) for i in range(2)], mode='concat',concat_axis=1,name="conv_2")
conv_3 = MaxPooling2D((3, 3), strides=(2, 2))(conv_2)
conv_3 = crosschannelnormalization()(conv_3)
conv_3 = ZeroPadding2D((1,1))(conv_3)
conv_3 = Conv2D(384,3,3,activation='relu',name='conv_3')(conv_3)
conv_4 = ZeroPadding2D((1,1))(conv_3)
conv_4 = merge([
Conv2D(192,3,3,activation="relu",name='conv_4_'+str(i+1))(
splittensor(ratio_split=2,id_split=i)(conv_4)
) for i in range(2)], mode='concat',concat_axis=1,name="conv_4")
conv_5 = ZeroPadding2D((1,1))(conv_4)
conv_5 = merge([
Conv2D(128,3,3,activation="relu",name='conv_5_'+str(i+1))(
splittensor(ratio_split=2,id_split=i)(conv_5)
) for i in range(2)], mode='concat',concat_axis=1,name="conv_5")
dense_1 = MaxPooling2D((3, 3), strides=(2,2),name="convpool_5")(conv_5)
if heatmap:
dense_1 = Conv2D(4096,6,6,activation="relu",name="dense_1")(dense_1)
dense_2 = Conv2D(4096,1,1,activation="relu",name="dense_2")(dense_1)
dense_3 = Conv2D(1000, 1,1,name="dense_3")(dense_2)
prediction = Softmax4D(axis=1,name="softmax")(dense_3)
else:
dense_1 = Flatten(name="flatten")(dense_1)
dense_1 = Dense(4096, activation='relu',name='dense_1')(dense_1)
dense_2 = Dropout(0.5)(dense_1)
dense_2 = Dense(4096, activation='relu',name='dense_2')(dense_2)
dense_3 = Dropout(0.5)(dense_2)
dense_3 = Dense(1000,name='dense_3')(dense_3)
prediction = Activation("softmax",name="softmax")(dense_3)
model = Model(input=inputs, output=prediction)
if weights_path:
model.load_weights(weights_path)
return model
def AlexNet(nb_classes=2, weights_path=None, heatmap=False):
if heatmap:
inputs = Input(shape=(3,None,None))
else:
inputs = Input(shape=(3,227,227))
conv_1 = Convolution2D(96, 11, 11,subsample=(4,4),activation='relu',
name='conv_1')(inputs)
conv_2 = MaxPooling2D((3, 3), strides=(2,2))(conv_1)
conv_2 = crosschannelnormalization(name="convpool_1")(conv_2)
conv_2 = ZeroPadding2D((2,2))(conv_2)
conv_2 = merge([
Convolution2D(128,5,5,activation="relu",name='conv_2_'+str(i+1))(
splittensor(ratio_split=2,id_split=i)(conv_2)
) for i in range(2)], mode='concat',concat_axis=1,name="conv_2")
conv_3 = MaxPooling2D((3, 3), strides=(2, 2))(conv_2)
conv_3 = crosschannelnormalization()(conv_3)
conv_3 = ZeroPadding2D((1,1))(conv_3)
conv_3 = Convolution2D(384,3,3,activation='relu',name='conv_3')(conv_3)
conv_4 = ZeroPadding2D((1,1))(conv_3)
conv_4 = merge([
Convolution2D(192,3,3,activation="relu",name='conv_4_'+str(i+1))(
splittensor(ratio_split=2,id_split=i)(conv_4)
) for i in range(2)], mode='concat',concat_axis=1,name="conv_4")
conv_5 = ZeroPadding2D((1,1))(conv_4)
conv_5 = merge([
Convolution2D(128,3,3,activation="relu",name='conv_5_'+str(i+1))(
splittensor(ratio_split=2,id_split=i)(conv_5)
) for i in range(2)], mode='concat',concat_axis=1,name="conv_5")
dense_1 = MaxPooling2D((3, 3), strides=(2,2),name="convpool_5")(conv_5)
if heatmap:
dense_1 = Convolution2D(4096,6,6,activation="relu",name="dense_1")(dense_1)
dense_2 = Convolution2D(4096,1,1,activation="relu",name="dense_2")(dense_1)
dense_3 = Convolution2D(1000, 1,1,name="dense_3")(dense_2)
prediction = Softmax4D(axis=1,name="softmax")(dense_3)
else:
dense_1 = Flatten(name="flatten")(dense_1)
dense_1 = Dense(4096, activation='relu',name='dense_1')(dense_1)
dense_2 = Dropout(0.5)(dense_1)
dense_2 = Dense(4096, activation='relu',name='dense_2')(dense_2)
dense_3 = Dropout(0.5)(dense_2)
dense_3 = Dense(output_dim=nb_classes,name='dense_3')(dense_3)
# dense_3 = Dense(1000,name='dense_3')(dense_3)
prediction = Activation("softmax",name="softmax")(dense_3)
model = Model(input=inputs, output=prediction)
if weights_path:
model.load_weights(weights_path)
return model
def load_alexnet_model_finetune67(weights_path=None, nb_class=None, top_model_weight_path=None):
inputs = Input(shape=(3,227,227))
conv_1 = Convolution2D(96, 11, 11,subsample=(4,4),
activation='relu',
name='conv_1',
W_regularizer=l2(0.0002))(inputs)
conv_2 = MaxPooling2D((3, 3), strides=(2,2))(conv_1)
conv_2 = crosschannelnormalization(name="convpool_1")(conv_2)
conv_2 = ZeroPadding2D((2,2))(conv_2)
conv_2 = merge([
Convolution2D(128,5,5,
activation="relu",
name='conv_2_'+str(i+1),
W_regularizer=l2(0.0002))(
splittensor(ratio_split=2,id_split=i)(conv_2)
) for i in range(2)], mode='concat',concat_axis=1,name="conv_2")
conv_3 = MaxPooling2D((3, 3), strides=(2, 2))(conv_2)
conv_3 = crosschannelnormalization()(conv_3)
conv_3 = ZeroPadding2D((1,1))(conv_3)
conv_3 = Convolution2D(384,3,3,activation='relu',name='conv_3', W_regularizer=l2(0.0002))(conv_3)
conv_4 = ZeroPadding2D((1,1))(conv_3)
conv_4 = merge([
Convolution2D(192,3,3,activation="relu",name='conv_4_'+str(i+1),W_regularizer=l2(0.0002))(
splittensor(ratio_split=2,id_split=i)(conv_4)
) for i in range(2)], mode='concat',concat_axis=1,name="conv_4")
conv_5 = ZeroPadding2D((1,1))(conv_4)
conv_5 = merge([
Convolution2D(128,3,3,activation="relu",name='conv_5_'+str(i+1),W_regularizer=l2(0.0002))(
splittensor(ratio_split=2,id_split=i)(conv_5)
) for i in range(2)], mode='concat',concat_axis=1,name="conv_5")
conv_5 = MaxPooling2D((3, 3), strides=(2,2),name="convpool_5")(conv_5)
dense_1 = Flatten(name="flatten")(conv_5)
dense_1 = Dense(4096, activation='relu',name='dense_1')(dense_1)
dense_2 = Dropout(0.5)(dense_1)
dense_2 = Dense(4096, activation='relu',name='dense_2')(dense_2)
dense_3 = Dropout(0.5)(dense_2)
dense_3 = Dense(nb_class,name='dense_3')(dense_3)
prediction = Activation("softmax",name="softmax")(dense_3)
base_model = Model(input=inputs, output=prediction)
base_model.load_weights(weights_path)
base_model = Model(input=inputs, output=conv_5)
model = get_top_model_for_alex_finetune67(
shape=base_model.output_shape[1:],
nb_class=nb_class,
weights_file_path=top_model_weight_path,
input=base_model.input,
output=base_model.output)
return model
def AlexNet(weights_path=None, heatmap=False):
if heatmap:
inputs = Input(shape=(3, None, None))
else:
inputs = Input(shape=(3, 227, 227))
conv_1 = Convolution2D(96, kernel_size=(11, 11), subsample=(4, 4), activation='relu', data_format='channels_first',
name='conv_1')(inputs)
conv_2 = MaxPooling2D((3, 3), strides=(2, 2))(conv_1)
conv_2 = crosschannelnormalization(name='convpool_1')(conv_2)
conv_2 = ZeroPadding2D((2, 2))(conv_2)
conv_2 = merge([
Convolution2D(128, kernel_size=(5, 5), activation='relu', data_format='channels_first', name='conv_2_' + str(i + 1))(
splittensor(ratio_split=2, id_split=i)(conv_2)
) for i in range(2)], mode='concat', concat_axis=1, name='conv_2')
conv_3 = MaxPooling2D((3, 3), strides=(2, 2))(conv_2)
conv_3 = crosschannelnormalization()(conv_3)
conv_3 = ZeroPadding2D((1, 1))(conv_3)
conv_3 = Convolution2D(384, kernel_size=(3, 3), activation='relu', data_format='channels_first', name='conv_3')(conv_3)
conv_4 = ZeroPadding2D((1, 1))(conv_3)
conv_4 = merge([
Convolution2D(192, kernel_size=(3, 3), activation='relu', data_format='channels_first', name='conv_4_' + str(i + 1))(
splittensor(ratio_split=2, id_split=i)(conv_4)
) for i in range(2)], mode='concat', concat_axis=1, name='conv_4')
conv_5 = ZeroPadding2D((1, 1))(conv_4)
conv_5 = merge([
Convolution2D(128, kernel_size=(3, 3), activation='relu', data_format='channels_first', name='conv_5_' + str(i + 1))(
splittensor(ratio_split=2, id_split=i)(conv_5)
) for i in range(2)], mode='concat', concat_axis=1, name='conv_5')
dense_1 = MaxPooling2D((3, 3), strides=(2, 2), name='convpool_5')(conv_5)
if heatmap:
dense_1 = Convolution2D(4096, kernel_size=(6, 6), activation='relu', data_format='channels_first', name='dense_1')(dense_1)
dense_2 = Convolution2D(4096, kernel_size=(1, 1), activation='relu', data_format='channels_first', name='dense_2')(dense_1)
dense_3 = Convolution2D(1000, kernel_size=(1, 1), data_format='channels_first', name='dense_3')(dense_2)
prediction = Softmax4D(axis=1, name='softmax')(dense_3)
else:
dense_1 = Flatten(name='flatten')(dense_1)
dense_1 = Dense(4096, activation='relu', name='dense_1')(dense_1)
dense_2 = Dropout(0.5)(dense_1)
dense_2 = Dense(4096, activation='relu', name='dense_2')(dense_2)
dense_3 = Dropout(0.5)(dense_2)
dense_3 = Dense(1000, name='dense_3')(dense_3)
prediction = Activation('softmax', name='softmax')(dense_3)
model = Model(input=inputs, output=prediction)
if weights_path:
model.load_weights(weights_path)
return model
def alexnet2(weights_path=None, nb_class=None):
inputs = Input(shape=(3,227,227))
conv_1 = Convolution2D(96, 11, 11,subsample=(4,4),activation='relu',
name='conv_1')(inputs)
conv_2 = MaxPooling2D((3, 3), strides=(2,2))(conv_1)
conv_2 = crosschannelnormalization(name="convpool_1")(conv_2)
conv_2 = ZeroPadding2D((2,2))(conv_2)
x1 = conv2D_bn(conv_2, 128, 5, 5, subsample=(1, 1), border_mode='same')
y1 = conv2D_bn(conv_2, 128, 5, 5, subsample=(1, 1), border_mode='same')
conv_2 = merge([x1,y1], mode='concat',concat_axis=1,name="conv_2")
conv_3 = MaxPooling2D((3, 3), strides=(2, 2))(conv_2)
conv_3 = crosschannelnormalization()(conv_3)
conv_3 = ZeroPadding2D((1,1))(conv_3)
conv_3 = Convolution2D(384,3,3,activation='relu',name='conv_3')(conv_3)
conv_4 = ZeroPadding2D((1,1))(conv_3)
x2 = conv2D_bn(conv_4, 192, 3, 3, subsample=(1, 1), border_mode='same')
y2 = conv2D_bn(conv_4, 192, 3, 3, subsample=(1, 1), border_mode='same')
conv_4 = merge([x2,y2], mode='concat',concat_axis=1,name="conv_4")
conv_5 = ZeroPadding2D((1,1))(conv_4)
x3 = conv2D_bn(conv_5, 192, 3, 3, subsample=(1, 1), border_mode='same')
y3 = conv2D_bn(conv_5, 192, 3, 3, subsample=(1, 1), border_mode='same')
conv_5 = merge([x3,y3], mode='concat',concat_axis=1,name="conv_5")
conv_5 = MaxPooling2D((3, 3), strides=(2,2),name="convpool_5")(conv_5)
dense_1 = Flatten(name="flatten")(conv_5)
dense_1 = Dense(4096, activation='relu',name='dense_1')(dense_1)
dense_2 = Dropout(0.5)(dense_1)
dense_2 = Dense(4096, activation='relu',name='dense_2')(dense_2)
dense_3 = Dropout(0.5)(dense_2)
dense_3 = Dense(nb_class,name='dense_3')(dense_3)
prediction = Activation("softmax",name="softmax")(dense_3)
base_model = Model(input=inputs, output=prediction)
if weights_path:
base_model.load_weights(weights_path)
return base_model
def alexnet(input_shape, nb_classes, mean_flag):
# code adapted from https://github.com/heuritech/convnets-keras
inputs = Input(shape=input_shape, name='main_input')
if mean_flag:
mean_subtraction = Lambda(mean_subtract, name='mean_subtraction')(inputs)
conv_1 = Conv2D(96, (11, 11), strides=(4,4), activation='relu',
name='conv_1', kernel_initializer='he_normal', bias_initializer='he_normal')(mean_subtraction)
else:
conv_1 = Conv2D(96, (11, 11), strides=(4,4), activation='relu',
name='conv_1', kernel_initializer='he_normal', bias_initializer='he_normal')(inputs)
conv_2 = MaxPooling2D((3, 3), strides=(2,2))(conv_1)
conv_2 = crosschannelnormalization(name="convpool_1")(conv_2)
conv_2 = ZeroPadding2D((2,2))(conv_2)
conv_2 = concatenate([
Conv2D(128, (5, 5), activation="relu", kernel_initializer='he_normal', bias_initializer='he_normal', name='conv_2_'+str(i+1))(
splittensor(ratio_split=2,id_split=i)(conv_2)
) for i in range(2)], axis=1, name="conv_2")
conv_3 = MaxPooling2D((3, 3), strides=(2, 2))(conv_2)
conv_3 = crosschannelnormalization()(conv_3)
conv_3 = ZeroPadding2D((1,1))(conv_3)
conv_3 = Conv2D(384, (3, 3), activation='relu', name='conv_3', kernel_initializer='he_normal', bias_initializer='he_normal')(conv_3)
conv_4 = ZeroPadding2D((1,1))(conv_3)
conv_4 = concatenate([
Conv2D(192, (3, 3), activation="relu", kernel_initializer='he_normal', bias_initializer='he_normal', name='conv_4_'+str(i+1))(
splittensor(ratio_split=2,id_split=i)(conv_4)
) for i in range(2)], axis=1, name="conv_4")
conv_5 = ZeroPadding2D((1,1))(conv_4)
conv_5 = concatenate([
Conv2D(128, (3, 3), activation="relu", kernel_initializer='he_normal', bias_initializer='he_normal', name='conv_5_'+str(i+1))(
splittensor(ratio_split=2,id_split=i)(conv_5)
) for i in range(2)], axis=1, name="conv_5")
dense_1 = MaxPooling2D((3, 3), strides=(2,2),name="convpool_5")(conv_5)
dense_1 = Flatten(name="flatten")(dense_1)
dense_1 = Dense(4096, activation='relu',name='dense_1', kernel_initializer='he_normal', bias_initializer='he_normal')(dense_1)
dense_2 = Dropout(0.5)(dense_1)
dense_2 = Dense(4096, activation='relu',name='dense__2', kernel_initializer='he_normal', bias_initializer='he_normal')(dense_2)
dense_3 = Dropout(0.5)(dense_2)
dense_3 = Dense(nb_classes,name='dense_3_new', kernel_initializer='he_normal', bias_initializer='he_normal')(dense_3)
prediction = Activation("softmax",name="softmax")(dense_3)
alexnet = Model(inputs = inputs, outputs = prediction)
return alexnet
def load_deep_features_model(nb_class, weights_path=None):
inputs = Input(shape=(3,227,227))
conv_1 = Convolution2D(96, 11, 11,subsample=(4,4),activation='relu',
name='conv_1')(inputs)
conv_2 = MaxPooling2D((3, 3), strides=(2,2))(conv_1)
conv_2 = crosschannelnormalization(name="convpool_1")(conv_2)
conv_2 = ZeroPadding2D((2,2))(conv_2)
conv_2 = merge([
Convolution2D(128,5,5,activation="relu",name='conv_2_'+str(i+1))(
splittensor(ratio_split=2,id_split=i)(conv_2)
) for i in range(2)], mode='concat',concat_axis=1,name="conv_2")
conv_3 = MaxPooling2D((3, 3), strides=(2, 2))(conv_2)
conv_3 = crosschannelnormalization()(conv_3)
conv_3 = ZeroPadding2D((1,1))(conv_3)
conv_3 = Convolution2D(384,3,3,activation='relu',name='conv_3')(conv_3)
conv_4 = ZeroPadding2D((1,1))(conv_3)
conv_4 = merge([
Convolution2D(192,3,3,activation="relu",name='conv_4_'+str(i+1))(
splittensor(ratio_split=2,id_split=i)(conv_4)
) for i in range(2)], mode='concat',concat_axis=1,name="conv_4")
conv_5 = ZeroPadding2D((1,1))(conv_4)
conv_5 = merge([
Convolution2D(128,3,3,activation="relu",name='conv_5_'+str(i+1))(
splittensor(ratio_split=2,id_split=i)(conv_5)
) for i in range(2)], mode='concat',concat_axis=1,name="conv_5")
conv_5 = MaxPooling2D((3, 3), strides=(2,2),name="convpool_5")(conv_5)
dense_1 = Flatten(name="flatten")(conv_5)
dense_1 = Dense(4096, activation='relu',name='dense_1')(dense_1)
dense_2 = Dropout(0.5)(dense_1)
dense_2 = Dense(4096, activation='relu',name='dense_2')(dense_2)
dense_3 = Dropout(0.5)(dense_2)
dense_3 = Dense(nb_class,name='dense_3')(dense_3)
prediction = Activation("softmax",name="softmax")(dense_3)
base_model = Model(input=inputs, output=prediction)
if weights_path:
base_model.load_weights(weights_path)
base_model = Model(input=inputs, output=conv_5)
return base_model
def get_alexnet(input_shape,nb_classes,mean_flag):
inputs = Input(shape=input_shape)
if mean_flag:
mean_subtraction = Lambda(mean_subtract, name='mean_subtraction')(inputs)
conv_1 = Convolution2D(96, 11, 11,subsample=(4,4),activation='relu',
name='conv_1', init='he_normal')(mean_subtraction)
else:
conv_1 = Convolution2D(96, 11, 11,subsample=(4,4),activation='relu',
name='conv_1', init='he_normal')(inputs)
conv_2 = MaxPooling2D((3, 3), strides=(2,2))(conv_1)
conv_2 = crosschannelnormalization(name="convpool_1")(conv_2)
conv_2 = ZeroPadding2D((2,2))(conv_2)
conv_2 = merge([
Convolution2D(128,5,5,activation="relu",init='he_normal', name='conv_2_'+str(i+1))(
splittensor(ratio_split=2,id_split=i)(conv_2)
) for i in range(2)], mode='concat',concat_axis=1,name="conv_2")
conv_3 = MaxPooling2D((3, 3), strides=(2, 2))(conv_2)
conv_3 = crosschannelnormalization()(conv_3)
conv_3 = ZeroPadding2D((1,1))(conv_3)
conv_3 = Convolution2D(384,3,3,activation='relu',name='conv_3',init='he_normal')(conv_3)
conv_4 = ZeroPadding2D((1,1))(conv_3)
conv_4 = merge([
Convolution2D(192,3,3,activation="relu", init='he_normal', name='conv_4_'+str(i+1))(
splittensor(ratio_split=2,id_split=i)(conv_4)
) for i in range(2)], mode='concat',concat_axis=1,name="conv_4")
conv_5 = ZeroPadding2D((1,1))(conv_4)
conv_5 = merge([
Convolution2D(128,3,3,activation="relu",init='he_normal', name='conv_5_'+str(i+1))(
splittensor(ratio_split=2,id_split=i)(conv_5)
) for i in range(2)], mode='concat',concat_axis=1,name="conv_5")
dense_1 = MaxPooling2D((3, 3), strides=(2,2),name="convpool_5")(conv_5)
dense_1 = Flatten(name="flatten")(dense_1)
dense_1 = Dense(4096, activation='relu',name='dense_1',init='he_normal')(dense_1)
dense_2 = Dropout(0.5)(dense_1)
dense_2 = Dense(4096, activation='relu',name='dense_2',init='he_normal')(dense_2)
dense_3 = Dropout(0.5)(dense_2)
dense_3 = Dense(nb_classes,name='dense_3_new',init='he_normal')(dense_3)
prediction = Activation("softmax",name="softmax")(dense_3)
alexnet = Model(input=inputs, output=prediction)
return alexnet
def alexnet(input_shape, nb_classes):
# https://github.com/duggalrahul/AlexNet-Experiments-Keras/blob/master/Code/alexnet_base.py
# code adapted from https://github.com/heuritech/convnets-keras
inputs = Input(shape=input_shape)
conv_1 = Convolution2D(96, 11, 11, subsample=(4, 4), activation='relu',
name='conv_1', init='he_normal')(inputs)
conv_2 = MaxPooling2D((3, 3), strides=(2, 2))(conv_1)
conv_2 = crosschannelnormalization(name="convpool_1")(conv_2)
conv_2 = ZeroPadding2D((2, 2))(conv_2)
conv_2 = merge([
Convolution2D(128, 5, 5, activation="relu", init='he_normal', name='conv_2_' + str(i + 1))(
splittensor(ratio_split=2, id_split=i)(conv_2)
) for i in range(2)], mode='concat', concat_axis=1, name="conv_2")
conv_3 = MaxPooling2D((3, 3), strides=(2, 2))(conv_2)
conv_3 = crosschannelnormalization()(conv_3)
conv_3 = ZeroPadding2D((1, 1))(conv_3)
conv_3 = Convolution2D(384, 3, 3, activation='relu', name='conv_3', init='he_normal')(conv_3)
conv_4 = ZeroPadding2D((1, 1))(conv_3)
conv_4 = merge([
Convolution2D(192, 3, 3, activation="relu", init='he_normal', name='conv_4_' + str(i + 1))(
splittensor(ratio_split=2, id_split=i)(conv_4)
) for i in range(2)], mode='concat', concat_axis=1, name="conv_4")
conv_5 = ZeroPadding2D((1, 1))(conv_4)
conv_5 = merge([
Convolution2D(128, 3, 3, activation="relu", init='he_normal', name='conv_5_' + str(i + 1))(
splittensor(ratio_split=2, id_split=i)(conv_5)
) for i in range(2)], mode='concat', concat_axis=1, name="conv_5")
dense_1 = MaxPooling2D((3, 3), strides=(2, 2), name="convpool_5")(conv_5)
dense_1 = Flatten(name="flatten")(dense_1)
dense_1 = Dense(4096, activation='relu', name='dense_1', init='he_normal')(dense_1)
dense_2 = Dropout(0.5)(dense_1)
dense_2 = Dense(4096, activation='relu', name='dense_2', init='he_normal')(dense_2)
dense_3 = Dropout(0.5)(dense_2)
dense_3 = Dense(nb_classes, name='dense_3_new', init='he_normal')(dense_3)
prediction = Activation("softmax", name="softmax")(dense_3)
alexnet = Model(input=inputs, output=prediction)
return alexnet
def train_shallow_alexnet_imagenet_FCN(classes=5, freeze_flag=None):
model = alexnet(input_shape=(3, 227, 227), nb_classes=1000, mean_flag=True)
model.load_weights('alexnet_weights.h5')
# modify architecture
last_conv_1 = model.layers[5].output
conv_2 = Conv2D(256, (5, 5),
strides=(1, 1),
activation='relu',
name='conv_2',
kernel_initializer='he_normal',
bias_initializer='he_normal')(last_conv_1)
conv_2 = MaxPooling2D((3, 3), strides=(2, 2))(conv_2)
conv_2 = crosschannelnormalization(name="convpool_2")(conv_2)
conv_2 = ZeroPadding2D((2, 2))(conv_2)
conv_2 = Dropout(0.5)(conv_2)
conv_activate = Conv2D(classes,
kernel_size=(1, 1),
strides=(1, 1),
activation='relu',
kernel_initializer='he_normal',
bias_initializer='he_normal',
name='conv_activate')(conv_2)
conv_activate = GlobalAveragePooling2D(
data_format='channels_first')(conv_activate)
model = Model(inputs=model.input, outputs=conv_activate)
plot_model(model, to_file='shallowalex_fcn', show_shapes=True)
print(model.summary())
return model
def alexnet_correct(weights_path=None,
out_dim=1000,
dist_type='blur',
corr_arch='CW',
num_ly_corr=5):
filter_sz = [96, 256, 384, 384, 256]
tr_wts = np.load(weights_path)
inp_img = Input((3, IN_dat.img_crop, IN_dat.img_crop), name='input_img')
set_trainable = False
correction_perc = 0.75
def split_tensor_lower(x):
inp_shape = K.int_shape(x)
chn = corrected_chn
return x[:, chn:, :, :]
def split_tensor_upper(x):
# corr_per = 0.25
inp_shape = K.int_shape(x)
chn = corrected_chn
return x[:, :chn, :, :]
def split_tensor_uppershape(input_shape):
inp_shape = input_shape
chn = corrected_chn
shape = (chn, ) + input_shape[2:]
shape = (input_shape[0], ) + shape
return shape
def split_tensor_lowershape(input_shape):
inp_shape = input_shape
chn = inp_shape[1] - corrected_chn
shape = (chn, ) + input_shape[2:]
shape = (input_shape[0], ) + shape
return shape
# else:
# def split_tensor_lower(x):
#
# inp_shape = K.int_shape(x)
# chn = int(inp_shape[1] * correction_perc)
#
# return x[:, chn:, :, :]
#
# def split_tensor_upper(x):
# # corr_per = 0.25
# inp_shape = K.int_shape(x)
# chn = int(inp_shape[1] * correction_perc)
# return x[:, :chn, :, :]
#
# def split_tensor_uppershape(input_shape):
# inp_shape = input_shape
# chn = int(inp_shape[1] * correction_perc)
# shape = (chn,) + input_shape[2:]
# shape = (input_shape[0],) + shape
# return shape
#
# def split_tensor_lowershape(input_shape):
# inp_shape = input_shape
# chn = inp_shape[1] - int(inp_shape[1] * correction_perc)
# shape = (chn,) + input_shape[2:]
# shape = (input_shape[0],) + shape
# return shape
wts_l1 = tr_wts[0].copy()
b_l1 = tr_wts[1].copy()
ranked_fltr = h5py.File('imagenet_alexnet_ranked_filters.h5', 'r')
if dist_type == 'blur':
corr_id = ranked_fltr['alexnet_blur/layer_' + str(1)][:]
elif dist_type == 'awgn':
corr_id = ranked_fltr['alexnet_awgn/layer_' + str(1)][:]
corrected_chn = int(correction_perc * filter_sz[0])
print corrected_chn
# print corr_id
wts_l1 = wts_l1[corr_id, :, :, :]
b_l1 = b_l1[corr_id]
wts_remap_l1 = np.zeros((filter_sz[0], filter_sz[0], 1, 1), np.float32)
b_remap_l1 = np.zeros(filter_sz[0], np.float32)
for filt_id in range(filter_sz[0]):
wts_remap_l1[corr_id[filt_id], filt_id, :, :] = 1.0
# inp_img1 = ZeroPadding2D((2,2))(inp_img)
conv_1 = Convolution2D(96,
11,
11,
subsample=(4, 4),
activation='linear',
weights=[wts_l1, b_l1],
name='conv_1',
trainable=set_trainable)(inp_img)
conv1_lower = Lambda(split_tensor_lower,
output_shape=split_tensor_lowershape)(conv_1)
conv1_upper = Lambda(split_tensor_upper,
output_shape=split_tensor_uppershape)(conv_1)
if corr_arch == 'CW':
conv1_correct = get_correct_unit_CW(conv1_upper, corrected_chn, 5, 2)
elif corr_arch == 'fixed':
conv1_correct = get_correct_unit_bottleneck(conv1_upper, 128,
corrected_chn, [1, 2], 2)
else:
conv1_correct = get_correct_unit_bottleneck(conv1_upper,
int(0.5 * corrected_chn),
corrected_chn, [1, 1, 2],
3)
conv1_sum_merge1 = merge([conv1_correct, conv1_upper], mode='sum')
conv1_1_merged = merge([conv1_sum_merge1, conv1_lower],
mode='concat',
concat_axis=1)
conv_1_remap = Convolution2D(filter_sz[0],
1,
1,
activation='linear',
name='conv_1_corr',
trainable=False,
weights=[wts_remap_l1,
b_remap_l1])(conv1_1_merged)
conv_1_out = Activation('relu', name='conv_1_relu')(conv_1_remap)
conv_2 = MaxPooling2D((3, 3), strides=(2, 2))(conv_1_out)
conv_2 = crosschannelnormalization(name="convpool_1")(conv_2)
conv_2 = ZeroPadding2D((2, 2))(conv_2)
conv2_w_t = tr_wts[2]
conv2_w = np.empty((2, conv2_w_t.shape[0], conv2_w_t.shape[1],
conv2_w_t.shape[2], conv2_w_t.shape[3]), np.float32)
conv2_w[0, :] = conv2_w_t.copy()
del conv2_w_t
conv2_w[1, :] = tr_wts[4]
conv2_b_t = tr_wts[3]
conv2_b = np.empty((2, conv2_b_t.shape[0]), np.float32)
conv2_b[0, :] = conv2_b_t.copy()
del conv2_b_t
conv2_b[1, :] = tr_wts[5]
conv_2 = merge([
Convolution2D(128,
5,
5,
activation="linear",
name='conv_2_' + str(i + 1),
weights=[conv2_w[i, :], conv2_b[i, :]],
trainable=set_trainable)(splittensor(ratio_split=2,
id_split=i)(conv_2))
for i in range(2)
],
mode='concat',
concat_axis=1,
name="conv_2")
if num_ly_corr > 1:
if dist_type == 'blur':
corr_id = ranked_fltr['alexnet_blur/layer_' + str(2)][:]
elif dist_type == 'awgn':
corr_id = ranked_fltr['alexnet_awgn/layer_' + str(2)][:]
corrected_chn = int(correction_perc * filter_sz[1])
print corrected_chn
# print corr_id
wts_remap_l2_inp = np.zeros((filter_sz[1], filter_sz[1], 1, 1),
np.float32)
b_remap_l2_inp = np.zeros(filter_sz[1], np.float32)
wts_remap_l2 = np.zeros((filter_sz[1], filter_sz[1], 1, 1), np.float32)
b_remap_l2 = np.zeros(filter_sz[1], np.float32)
for filt_id in range(filter_sz[1]):
wts_remap_l2_inp[filt_id, corr_id[filt_id], :, :] = 1.0
wts_remap_l2[corr_id[filt_id], filt_id, :, :] = 1.0
conv_2_inp_remap = Convolution2D(
filter_sz[1],
1,
1,
activation='linear',
trainable=False,
weights=[wts_remap_l2_inp, b_remap_l2_inp])(conv_2)
conv2_lower = Lambda(
split_tensor_lower,
output_shape=split_tensor_lowershape)(conv_2_inp_remap)
conv2_upper = Lambda(
split_tensor_upper,
output_shape=split_tensor_uppershape)(conv_2_inp_remap)
if corr_arch == 'CW':
conv2_correct = get_correct_unit_CW(conv2_upper, corrected_chn, 3,
2)
elif corr_arch == 'fixed':
conv2_correct = get_correct_unit_bottleneck(
conv2_upper, 128, corrected_chn, [1, 2], 2)
else:
conv2_correct = get_correct_unit_bottleneck(
conv2_upper, int(0.5 * corrected_chn), corrected_chn,
[1, 1, 1], 3)
conv2_sum_merge1 = merge([conv2_correct, conv2_upper], mode='sum')
conv2_1_merged = merge([conv2_sum_merge1, conv2_lower],
mode='concat',
concat_axis=1)
conv_2_remap = Convolution2D(filter_sz[1],
1,
1,
activation='linear',
name='conv_2_corr',
trainable=False,
weights=[wts_remap_l2,
b_remap_l2])(conv2_1_merged)
conv_2_out = Activation('relu', name='conv_2_relu')(conv_2_remap)
else:
conv_2_out = Activation('relu', name='conv_2_relu')(conv_2)
conv_3 = MaxPooling2D((3, 3), strides=(2, 2))(conv_2_out)
conv_3 = crosschannelnormalization()(conv_3)
conv_3 = ZeroPadding2D((1, 1))(conv_3)
wts_l3 = tr_wts[6].copy()
b_l3 = tr_wts[7].copy()
#
if num_ly_corr > 2:
correction_perc = 0.5
if dist_type == 'blur':
corr_id = ranked_fltr['alexnet_blur/layer_' + str(3)][:]
elif dist_type == 'awgn':
corr_id = ranked_fltr['alexnet_awgn/layer_' + str(3)][:]
corrected_chn = int(correction_perc * filter_sz[2])
print corrected_chn
# print corr_id
wts_l3 = wts_l3[corr_id, :, :, :]
b_l3 = b_l3[corr_id]
wts_remap_l3 = np.zeros((filter_sz[2], filter_sz[2], 1, 1), np.float32)
b_remap_l3 = np.zeros(filter_sz[2], np.float32)
for filt_id in range(filter_sz[2]):
wts_remap_l3[corr_id[filt_id], filt_id, :, :] = 1.0
conv_3 = Convolution2D(384,
3,
3,
weights=[wts_l3, b_l3],
activation='linear',
name='conv_3',
trainable=set_trainable)(conv_3)
#
conv3_lower = Lambda(split_tensor_lower,
output_shape=split_tensor_lowershape)(conv_3)
conv3_upper = Lambda(split_tensor_upper,
output_shape=split_tensor_uppershape)(conv_3)
if corr_arch == 'CW':
conv3_correct = get_correct_unit_CW(conv3_upper, corrected_chn, 3,
2)
elif corr_arch == 'fixed':
conv3_correct = get_correct_unit_bottleneck(
conv3_upper, 128, corrected_chn, [1, 1], 2)
else:
conv3_correct = get_correct_unit_bottleneck(
conv3_upper, int(0.5 * corrected_chn), corrected_chn,
[1, 1, 1], 3)
conv3_sum_merge1 = merge([conv3_correct, conv3_upper], mode='sum')
conv3_1_merged = merge([conv3_sum_merge1, conv3_lower],
mode='concat',
concat_axis=1)
conv_3_remap = Convolution2D(filter_sz[2],
1,
1,
activation='linear',
name='conv_3_corr',
trainable=False,
weights=[wts_remap_l3,
b_remap_l3])(conv3_1_merged)
conv_3_out = Activation('relu', name='conv_3_relu')(conv_3_remap)
else:
conv_3 = Convolution2D(384,
3,
3,
weights=[wts_l3, b_l3],
activation='linear',
name='conv_3',
trainable=set_trainable)(conv_3)
conv_3_out = Activation('relu', name='conv_3_relu')(conv_3)
conv_4 = ZeroPadding2D((1, 1))(conv_3_out)
conv4_w_t = tr_wts[8].copy()
conv4_w = np.empty((2, conv4_w_t.shape[0], conv4_w_t.shape[1],
conv4_w_t.shape[2], conv4_w_t.shape[3]), np.float32)
conv4_w[0, :] = conv4_w_t.copy()
del conv4_w_t
conv4_w[1, :] = tr_wts[10].copy()
conv4_b_t = tr_wts[9].copy()
conv4_b = np.empty((2, conv4_b_t.shape[0]), np.float32)
conv4_b[0, :] = conv4_b_t.copy()
del conv4_b_t
conv4_b[1, :] = tr_wts[11].copy()
conv_4 = merge([
Convolution2D(192,
3,
3,
activation="linear",
name='conv_4_' + str(i + 1),
weights=[conv4_w[i, :], conv4_b[i, :]],
trainable=set_trainable)(splittensor(ratio_split=2,
id_split=i)(conv_4))
for i in range(2)
],
mode='concat',
concat_axis=1,
name="conv_4")
#
if num_ly_corr > 3:
correction_perc = 0.5
if dist_type == 'blur':
corr_id = ranked_fltr['alexnet_blur/layer_' + str(4)][:]
elif dist_type == 'awgn':
corr_id = ranked_fltr['alexnet_awgn/layer_' + str(4)][:]
corrected_chn = int(correction_perc * filter_sz[3])
# corrected_chn = corr_chn
print corrected_chn
# print corr_id
wts_remap_l4_inp = np.zeros((filter_sz[3], filter_sz[3], 1, 1),
np.float32)
b_remap_l4_inp = np.zeros(filter_sz[3], np.float32)
wts_remap_l4 = np.zeros((filter_sz[3], filter_sz[3], 1, 1), np.float32)
b_remap_l4 = np.zeros(filter_sz[3], np.float32)
for filt_id in range(filter_sz[3]):
wts_remap_l4_inp[filt_id, corr_id[filt_id], :, :] = 1.0
wts_remap_l4[corr_id[filt_id], filt_id, :, :] = 1.0
conv_4_inp_remap = Convolution2D(
filter_sz[3],
1,
1,
activation='linear',
trainable=False,
weights=[wts_remap_l4_inp, b_remap_l4_inp])(conv_4)
conv4_lower = Lambda(
split_tensor_lower,
output_shape=split_tensor_lowershape)(conv_4_inp_remap)
conv4_upper = Lambda(
split_tensor_upper,
output_shape=split_tensor_uppershape)(conv_4_inp_remap)
if corr_arch == 'CW':
conv4_correct = get_correct_unit_CW(conv4_upper, corrected_chn, 3,
2)
elif corr_arch == 'fixed':
conv4_correct = get_correct_unit_bottleneck(
conv4_upper, 128, corrected_chn, [1, 1], 2)
else:
conv4_correct = get_correct_unit_bottleneck(
conv4_upper, int(0.5 * corrected_chn), corrected_chn,
[1, 1, 1], 3)
conv4_sum_merge1 = merge([conv4_correct, conv4_upper], mode='sum')
conv4_1_merged = merge([conv4_sum_merge1, conv4_lower],
mode='concat',
concat_axis=1)
conv_4_remap = Convolution2D(filter_sz[3],
1,
1,
activation='linear',
name='conv_4_corr',
trainable=False,
weights=[wts_remap_l4,
b_remap_l4])(conv4_1_merged)
conv_4_out = Activation('relu', name='conv_4_relu')(conv_4_remap)
else:
conv_4_out = Activation('relu', name='conv_4_relu')(conv_4)
conv_5 = ZeroPadding2D((1, 1))(conv_4_out)
conv5_w_t = tr_wts[12].copy()
conv5_w = np.empty((2, conv5_w_t.shape[0], conv5_w_t.shape[1],
conv5_w_t.shape[2], conv5_w_t.shape[3]), np.float32)
conv5_w[0, :] = conv5_w_t.copy()
del conv5_w_t
conv5_w[1, :] = tr_wts[14].copy()
conv5_b_t = tr_wts[13].copy()
conv5_b = np.empty((2, conv5_b_t.shape[0]), np.float32)
conv5_b[0, :] = conv5_b_t.copy()
del conv5_b_t
conv5_b[1, :] = tr_wts[15].copy()
conv_5 = merge([
Convolution2D(128,
3,
3,
activation="linear",
name='conv_5_' + str(i + 1),
weights=[conv5_w[i, :], conv5_b[i, :]],
trainable=set_trainable)(splittensor(ratio_split=2,
id_split=i)(conv_5))
for i in range(2)
],
mode='concat',
concat_axis=1,
name="conv_5")
#
#
#
if num_ly_corr > 4:
correction_perc = 0.5
if dist_type == 'blur':
corr_id = ranked_fltr['alexnet_blur/layer_' + str(5)][:]
elif dist_type == 'awgn':
corr_id = ranked_fltr['alexnet_awgn/layer_' + str(5)][:]
corrected_chn = int(correction_perc * filter_sz[4])
print corrected_chn
# print corr_id
wts_remap_l5_inp = np.zeros((filter_sz[4], filter_sz[4], 1, 1),
np.float32)
b_remap_l5_inp = np.zeros(filter_sz[4], np.float32)
wts_remap_l5 = np.zeros((filter_sz[4], filter_sz[4], 1, 1), np.float32)
b_remap_l5 = np.zeros(filter_sz[4], np.float32)
for filt_id in range(filter_sz[4]):
wts_remap_l5_inp[filt_id, corr_id[filt_id], :, :] = 1.0
wts_remap_l5[corr_id[filt_id], filt_id, :, :] = 1.0
conv_5_inp_remap = Convolution2D(
filter_sz[4],
1,
1,
activation='linear',
trainable=False,
weights=[wts_remap_l5_inp, b_remap_l5_inp])(conv_5)
conv5_lower = Lambda(
split_tensor_lower,
output_shape=split_tensor_lowershape)(conv_5_inp_remap)
conv5_upper = Lambda(
split_tensor_upper,
output_shape=split_tensor_uppershape)(conv_5_inp_remap)
if corr_arch == 'CW':
conv5_correct = get_correct_unit_CW(conv5_upper, corrected_chn, 3,
2)
elif corr_arch == 'fixed':
conv5_correct = get_correct_unit_bottleneck(
conv5_upper, 128, corrected_chn, [1, 1], 2)
else:
conv5_correct = get_correct_unit_bottleneck(
conv5_upper, int(0.5 * corrected_chn), corrected_chn,
[1, 1, 1], 3)
conv5_sum_merge1 = merge([conv5_correct, conv5_upper], mode='sum')
conv5_1_merged = merge([conv5_sum_merge1, conv5_lower],
mode='concat',
concat_axis=1)
conv_5_remap = Convolution2D(filter_sz[4],
1,
1,
activation='linear',
name='conv_5_corr',
trainable=False,
weights=[wts_remap_l5,
b_remap_l5])(conv5_1_merged)
conv_5_out = Activation('relu', name='conv_5_relu')(conv_5_remap)
else:
conv_5_out = Activation('relu', name='conv_5_relu')(conv_5)
dense_1 = MaxPooling2D((3, 3), strides=(2, 2),
name="convpool_5")(conv_5_out)
dense_1_w = tr_wts[16]
dense_1_b = tr_wts[17]
dense_2_w = tr_wts[18]
dense_2_b = tr_wts[19]
dense_1 = Flatten(name="flatten")(dense_1)
dense_1 = Dense(4096,
activation='relu',
name='dense_1',
weights=[dense_1_w, dense_1_b],
trainable=False)(dense_1)
dense_2 = Dropout(0.5)(dense_1)
dense_2 = Dense(4096,
activation='relu',
name='dense_2',
weights=[dense_2_w, dense_2_b],
trainable=False)(dense_2)
dense_3 = Dropout(0.5)(dense_2)
dense_3 = Dense(out_dim,
name='dense_3',
weights=[tr_wts[20][:, :out_dim], tr_wts[21][:out_dim]],
trainable=False,
init='he_normal')(dense_3)
prediction = Activation("softmax", name="softmax")(dense_3)
print '\n'
print tr_wts[20].shape
print '\n'
print tr_wts[21].shape
model = Model(input=inp_img, output=prediction)
layer_dict = dict([(layer.name, layer) for layer in model.layers])
return model, layer_dict
def AlexNetDNN(weights_path=None,
heatmap=False,
trainable=False,
out_dim=1000):
if heatmap:
inputs = Input(shape=(3, None, None))
else:
inputs = Input(shape=(3, 227, 227))
inp_file = h5py.File(weights_path)
conv1_w = inp_file['conv_1/conv_1_W'][:]
conv1_b = inp_file['conv_1/conv_1_b'][:]
conv_1 = Convolution2D(96,
11,
11,
subsample=(4, 4),
activation='relu',
weights=[conv1_w, conv1_b],
name='conv_1',
trainable=trainable)(inputs)
conv_2 = MaxPooling2D((3, 3), strides=(2, 2))(conv_1)
conv_2 = crosschannelnormalization(name="convpool_1")(conv_2)
conv_2 = ZeroPadding2D((2, 2))(conv_2)
conv2_w_t = inp_file['conv_2_1/conv_2_1_W'][:]
conv2_w = np.empty((2, conv2_w_t.shape[0], conv2_w_t.shape[1],
conv2_w_t.shape[2], conv2_w_t.shape[3]), np.float32)
conv2_w[0, :] = conv2_w_t.copy()
del conv2_w_t
conv2_w[1, :] = inp_file['conv_2_2/conv_2_2_W'][:]
conv2_b_t = inp_file['conv_2_1/conv_2_1_b'][:]
conv2_b = np.empty((2, conv2_b_t.shape[0]), np.float32)
conv2_b[0, :] = conv2_b_t.copy()
del conv2_b_t
conv2_b[1, :] = inp_file['conv_2_2/conv_2_2_b'][:]
conv_2 = merge([
Convolution2D(128,
5,
5,
activation="relu",
name='conv_2_' + str(i + 1),
weights=[conv2_w[i, :], conv2_b[i, :]],
trainable=trainable)(splittensor(ratio_split=2,
id_split=i)(conv_2))
for i in range(2)
],
mode='concat',
concat_axis=1,
name="conv_2")
conv_3 = MaxPooling2D((3, 3), strides=(2, 2))(conv_2)
conv_3 = crosschannelnormalization()(conv_3)
conv_3 = ZeroPadding2D((1, 1))(conv_3)
conv3_w = inp_file['conv_3/conv_3_W'][:]
conv3_b = inp_file['conv_3/conv_3_b'][:]
conv_3 = Convolution2D(384,
3,
3,
weights=[conv3_w, conv3_b],
activation='relu',
name='conv_3',
trainable=trainable)(conv_3)
conv_4 = ZeroPadding2D((1, 1))(conv_3)
conv4_w_t = inp_file['conv_4_1/conv_4_1_W'][:]
conv4_w = np.empty((2, conv4_w_t.shape[0], conv4_w_t.shape[1],
conv4_w_t.shape[2], conv4_w_t.shape[3]), np.float32)
conv4_w[0, :] = conv4_w_t.copy()
del conv4_w_t
conv4_w[1, :] = inp_file['conv_4_2/conv_4_2_W'][:]
conv4_b_t = inp_file['conv_4_1/conv_4_1_b'][:]
conv4_b = np.empty((2, conv4_b_t.shape[0]), np.float32)
conv4_b[0, :] = conv4_b_t.copy()
del conv4_b_t
conv4_b[1, :] = inp_file['conv_4_2/conv_4_2_b'][:]
conv_4 = merge([
Convolution2D(192,
3,
3,
activation="relu",
name='conv_4_' + str(i + 1),
weights=[conv4_w[i, :], conv4_b[i, :]],
trainable=trainable)(splittensor(ratio_split=2,
id_split=i)(conv_4))
for i in range(2)
],
mode='concat',
concat_axis=1,
name="conv_4")
conv_5 = ZeroPadding2D((1, 1))(conv_4)
conv5_w_t = inp_file['conv_5_1/conv_5_1_W'][:]
conv5_w = np.empty((2, conv5_w_t.shape[0], conv5_w_t.shape[1],
conv5_w_t.shape[2], conv5_w_t.shape[3]), np.float32)
conv5_w[0, :] = conv5_w_t.copy()
del conv5_w_t
conv5_w[1, :] = inp_file['conv_5_2/conv_5_2_W'][:]
conv5_b_t = inp_file['conv_5_1/conv_5_1_b'][:]
conv5_b = np.empty((2, conv5_b_t.shape[0]), np.float32)
conv5_b[0, :] = conv5_b_t.copy()
del conv5_b_t
conv5_b[1, :] = inp_file['conv_5_2/conv_5_2_b'][:]
conv_5 = merge([
Convolution2D(128,
3,
3,
activation="relu",
name='conv_5_' + str(i + 1),
weights=[conv5_w[i, :], conv5_b[i, :]],
trainable=trainable)(splittensor(ratio_split=2,
id_split=i)(conv_5))
for i in range(2)
],
mode='concat',
concat_axis=1,
name="conv_5")
dense_1 = MaxPooling2D((3, 3), strides=(2, 2), name="convpool_5")(conv_5)
dense_1_w = inp_file['dense_1/dense_1_W'][:]
dense_1_b = inp_file['dense_1/dense_1_b'][:]
dense_2_w = inp_file['dense_2/dense_2_W'][:]
dense_2_b = inp_file['dense_2/dense_2_b'][:]
dense_3_w = inp_file['dense_3/dense_3_W'][:]
dense_3_b = inp_file['dense_3/dense_3_b'][:]
dense_1 = Flatten(name="flatten")(dense_1)
dense_1 = Dense(4096,
activation='relu',
name='dense_1',
weights=[dense_1_w, dense_1_b],
trainable=trainable)(dense_1)
dense_2 = Dropout(0.5)(dense_1)
dense_2 = Dense(4096,
activation='relu',
name='dense_2',
weights=[dense_2_w, dense_2_b],
trainable=trainable)(dense_2)
dense_3 = Dropout(0.5)(dense_2)
if out_dim == 1000:
dense_3 = Dense(out_dim,
name='dense_3',
weights=[dense_3_w, dense_3_b],
trainable=trainable)(dense_3)
else:
# change trainable from true to trainable
dense_3 = Dense(out_dim,
name='dense_3',
trainable=True,
init='he_normal')(dense_3)
prediction = Activation("softmax", name="softmax")(dense_3)
model = Model(input=inputs, output=prediction)
layer_dict = dict([(layer.name, layer) for layer in model.layers])
return model, layer_dict
def AlexNetDNN_layers(weights_path=None,
layer_id=0,
trainable=False,
out_dim=1000):
learning_rate_multiplier = 1.0
tr_wts = np.load(weights_path)
if layer_id == 0:
inputs = Input(shape=(96, 55, 55))
conv_2 = MaxPooling2D((3, 3), strides=(2, 2))(inputs)
conv_2 = crosschannelnormalization(name="convpool_1")(conv_2)
conv_2 = ZeroPadding2D((2, 2))(conv_2)
conv2_w_t = tr_wts[2]
conv2_w = np.empty((2, conv2_w_t.shape[0], conv2_w_t.shape[1],
conv2_w_t.shape[2], conv2_w_t.shape[3]),
np.float32)
conv2_w[0, :] = conv2_w_t.copy()
del conv2_w_t
conv2_w[1, :] = tr_wts[4]
conv2_b_t = tr_wts[3]
conv2_b = np.empty((2, conv2_b_t.shape[0]), np.float32)
conv2_b[0, :] = conv2_b_t.copy()
del conv2_b_t
conv2_b[1, :] = tr_wts[5]
conv_2 = merge([
Convolution2D(128,
5,
5,
activation="relu",
name='conv_2_' + str(i + 1),
weights=[conv2_w[i, :], conv2_b[i, :]],
trainable=trainable)(splittensor(ratio_split=2,
id_split=i)(conv_2))
for i in range(2)
],
mode='concat',
concat_axis=1,
name="conv_2")
conv_3 = MaxPooling2D((3, 3), strides=(2, 2))(conv_2)
conv_3 = crosschannelnormalization()(conv_3)
conv_3 = ZeroPadding2D((1, 1))(conv_3)
conv3_w = tr_wts[6]
conv3_b = tr_wts[7]
conv_3 = Convolution2D(384,
3,
3,
weights=[conv3_w, conv3_b],
activation='relu',
name='conv_3',
trainable=trainable)(conv_3)
conv_4 = ZeroPadding2D((1, 1))(conv_3)
conv4_w_t = tr_wts[8]
conv4_w = np.empty((2, conv4_w_t.shape[0], conv4_w_t.shape[1],
conv4_w_t.shape[2], conv4_w_t.shape[3]),
np.float32)
conv4_w[0, :] = conv4_w_t.copy()
del conv4_w_t
conv4_w[1, :] = tr_wts[10]
conv4_b_t = tr_wts[9]
conv4_b = np.empty((2, conv4_b_t.shape[0]), np.float32)
conv4_b[0, :] = conv4_b_t.copy()
del conv4_b_t
conv4_b[1, :] = tr_wts[11]
conv_4 = merge([
Convolution2D(192,
3,
3,
activation="relu",
name='conv_4_' + str(i + 1),
weights=[conv4_w[i, :], conv4_b[i, :]],
trainable=trainable)(splittensor(ratio_split=2,
id_split=i)(conv_4))
for i in range(2)
],
mode='concat',
concat_axis=1,
name="conv_4")
conv_5 = ZeroPadding2D((1, 1))(conv_4)
conv5_w_t = tr_wts[12]
conv5_w = np.empty((2, conv5_w_t.shape[0], conv5_w_t.shape[1],
conv5_w_t.shape[2], conv5_w_t.shape[3]),
np.float32)
conv5_w[0, :] = conv5_w_t.copy()
del conv5_w_t
conv5_w[1, :] = tr_wts[14]
conv5_b_t = tr_wts[13]
conv5_b = np.empty((2, conv5_b_t.shape[0]), np.float32)
conv5_b[0, :] = conv5_b_t.copy()
del conv5_b_t
conv5_b[1, :] = tr_wts[15]
conv_5 = merge([
Convolution2D(128,
3,
3,
activation="relu",
name='conv_5_' + str(i + 1),
weights=[conv5_w[i, :], conv5_b[i, :]],
trainable=trainable)(splittensor(ratio_split=2,
id_split=i)(conv_5))
for i in range(2)
],
mode='concat',
concat_axis=1,
name="conv_5")
dense_1 = MaxPooling2D((3, 3), strides=(2, 2),
name="convpool_5")(conv_5)
dense_1_w = tr_wts[16]
dense_1_b = tr_wts[17]
dense_2_w = tr_wts[18]
dense_2_b = tr_wts[19]
dense_1 = Flatten(name="flatten")(dense_1)
dense_1 = Dense(4096,
activation='relu',
name='dense_1',
weights=[dense_1_w, dense_1_b],
trainable=trainable)(dense_1)
dense_2 = Dropout(0.5)(dense_1)
dense_2 = Dense(4096,
activation='relu',
name='dense_2',
weights=[dense_2_w, dense_2_b],
trainable=trainable)(dense_2)
dense_3 = Dropout(0.5)(dense_2)
dense_3 = Dense(out_dim,
name='dense_3',
weights=[tr_wts[20], tr_wts[21]],
trainable=trainable,
init='he_normal')(dense_3)
prediction = Activation("softmax", name="softmax")(dense_3)
elif layer_id == 1:
inputs = Input(shape=(256, 27, 27))
conv_3 = MaxPooling2D((3, 3), strides=(2, 2))(inputs)
conv_3 = crosschannelnormalization()(conv_3)
conv_3 = ZeroPadding2D((1, 1))(conv_3)
conv3_w = tr_wts[6]
conv3_b = tr_wts[7]
conv_3 = Convolution2D(384,
3,
3,
weights=[conv3_w, conv3_b],
activation='relu',
name='conv_3',
trainable=trainable)(conv_3)
conv_4 = ZeroPadding2D((1, 1))(conv_3)
conv4_w_t = tr_wts[8]
conv4_w = np.empty((2, conv4_w_t.shape[0], conv4_w_t.shape[1],
conv4_w_t.shape[2], conv4_w_t.shape[3]),
np.float32)
conv4_w[0, :] = conv4_w_t.copy()
del conv4_w_t
conv4_w[1, :] = tr_wts[10]
conv4_b_t = tr_wts[9]
conv4_b = np.empty((2, conv4_b_t.shape[0]), np.float32)
conv4_b[0, :] = conv4_b_t.copy()
del conv4_b_t
conv4_b[1, :] = tr_wts[11]
conv_4 = merge([
Convolution2D(192,
3,
3,
activation="relu",
name='conv_4_' + str(i + 1),
weights=[conv4_w[i, :], conv4_b[i, :]],
trainable=trainable)(splittensor(ratio_split=2,
id_split=i)(conv_4))
for i in range(2)
],
mode='concat',
concat_axis=1,
name="conv_4")
conv_5 = ZeroPadding2D((1, 1))(conv_4)
conv5_w_t = tr_wts[12]
conv5_w = np.empty((2, conv5_w_t.shape[0], conv5_w_t.shape[1],
conv5_w_t.shape[2], conv5_w_t.shape[3]),
np.float32)
conv5_w[0, :] = conv5_w_t.copy()
del conv5_w_t
conv5_w[1, :] = tr_wts[14]
conv5_b_t = tr_wts[13]
conv5_b = np.empty((2, conv5_b_t.shape[0]), np.float32)
conv5_b[0, :] = conv5_b_t.copy()
del conv5_b_t
conv5_b[1, :] = tr_wts[15]
conv_5 = merge([
Convolution2D(128,
3,
3,
activation="relu",
name='conv_5_' + str(i + 1),
weights=[conv5_w[i, :], conv5_b[i, :]],
trainable=trainable)(splittensor(ratio_split=2,
id_split=i)(conv_5))
for i in range(2)
],
mode='concat',
concat_axis=1,
name="conv_5")
dense_1 = MaxPooling2D((3, 3), strides=(2, 2),
name="convpool_5")(conv_5)
dense_1_w = tr_wts[16]
dense_1_b = tr_wts[17]
dense_2_w = tr_wts[18]
dense_2_b = tr_wts[19]
dense_1 = Flatten(name="flatten")(dense_1)
dense_1 = Dense(4096,
activation='relu',
name='dense_1',
weights=[dense_1_w, dense_1_b],
trainable=trainable)(dense_1)
dense_2 = Dropout(0.5)(dense_1)
dense_2 = Dense(4096,
activation='relu',
name='dense_2',
weights=[dense_2_w, dense_2_b],
trainable=trainable)(dense_2)
dense_3 = Dropout(0.5)(dense_2)
dense_3 = Dense(out_dim,
name='dense_3',
weights=[tr_wts[20], tr_wts[21]],
trainable=trainable,
init='he_normal')(dense_3)
prediction = Activation("softmax", name="softmax")(dense_3)
elif layer_id == 2:
inputs = Input(shape=(384, 13, 13))
conv_4 = ZeroPadding2D((1, 1))(inputs)
conv4_w_t = tr_wts[8]
conv4_w = np.empty((2, conv4_w_t.shape[0], conv4_w_t.shape[1],
conv4_w_t.shape[2], conv4_w_t.shape[3]),
np.float32)
conv4_w[0, :] = conv4_w_t.copy()
del conv4_w_t
conv4_w[1, :] = tr_wts[10]
conv4_b_t = tr_wts[9]
conv4_b = np.empty((2, conv4_b_t.shape[0]), np.float32)
conv4_b[0, :] = conv4_b_t.copy()
del conv4_b_t
conv4_b[1, :] = tr_wts[11]
conv_4 = merge([
Convolution2D(192,
3,
3,
activation="relu",
name='conv_4_' + str(i + 1),
weights=[conv4_w[i, :], conv4_b[i, :]],
trainable=trainable)(splittensor(ratio_split=2,
id_split=i)(conv_4))
for i in range(2)
],
mode='concat',
concat_axis=1,
name="conv_4")
conv_5 = ZeroPadding2D((1, 1))(conv_4)
conv5_w_t = tr_wts[12]
conv5_w = np.empty((2, conv5_w_t.shape[0], conv5_w_t.shape[1],
conv5_w_t.shape[2], conv5_w_t.shape[3]),
np.float32)
conv5_w[0, :] = conv5_w_t.copy()
del conv5_w_t
conv5_w[1, :] = tr_wts[14]
conv5_b_t = tr_wts[13]
conv5_b = np.empty((2, conv5_b_t.shape[0]), np.float32)
conv5_b[0, :] = conv5_b_t.copy()
del conv5_b_t
conv5_b[1, :] = tr_wts[15]
conv_5 = merge([
Convolution2D(128,
3,
3,
activation="relu",
name='conv_5_' + str(i + 1),
weights=[conv5_w[i, :], conv5_b[i, :]],
trainable=trainable)(splittensor(ratio_split=2,
id_split=i)(conv_5))
for i in range(2)
],
mode='concat',
concat_axis=1,
name="conv_5")
dense_1 = MaxPooling2D((3, 3), strides=(2, 2),
name="convpool_5")(conv_5)
dense_1_w = tr_wts[16]
dense_1_b = tr_wts[17]
dense_2_w = tr_wts[18]
dense_2_b = tr_wts[19]
dense_1 = Flatten(name="flatten")(dense_1)
dense_1 = Dense(4096,
activation='relu',
name='dense_1',
weights=[dense_1_w, dense_1_b],
trainable=trainable)(dense_1)
dense_2 = Dropout(0.5)(dense_1)
dense_2 = Dense(4096,
activation='relu',
name='dense_2',
weights=[dense_2_w, dense_2_b],
trainable=trainable)(dense_2)
dense_3 = Dropout(0.5)(dense_2)
dense_3 = Dense(out_dim,
name='dense_3',
weights=[tr_wts[20], tr_wts[21]],
trainable=trainable,
init='he_normal')(dense_3)
prediction = Activation("softmax", name="softmax")(dense_3)
elif layer_id == 3:
inputs = Input(shape=(384, 13, 13))
conv_5 = ZeroPadding2D((1, 1))(inputs)
conv5_w_t = tr_wts[12]
conv5_w = np.empty((2, conv5_w_t.shape[0], conv5_w_t.shape[1],
conv5_w_t.shape[2], conv5_w_t.shape[3]),
np.float32)
conv5_w[0, :] = conv5_w_t.copy()
del conv5_w_t
conv5_w[1, :] = tr_wts[14]
conv5_b_t = tr_wts[13]
conv5_b = np.empty((2, conv5_b_t.shape[0]), np.float32)
conv5_b[0, :] = conv5_b_t.copy()
del conv5_b_t
conv5_b[1, :] = tr_wts[15]
conv_5 = merge([
Convolution2D(128,
3,
3,
activation="relu",
name='conv_5_' + str(i + 1),
weights=[conv5_w[i, :], conv5_b[i, :]],
trainable=trainable)(splittensor(ratio_split=2,
id_split=i)(conv_5))
for i in range(2)
],
mode='concat',
concat_axis=1,
name="conv_5")
dense_1 = MaxPooling2D((3, 3), strides=(2, 2),
name="convpool_5")(conv_5)
dense_1_w = tr_wts[16]
dense_1_b = tr_wts[17]
dense_2_w = tr_wts[18]
dense_2_b = tr_wts[19]
dense_1 = Flatten(name="flatten")(dense_1)
dense_1 = Dense(4096,
activation='relu',
name='dense_1',
weights=[dense_1_w, dense_1_b],
trainable=trainable)(dense_1)
dense_2 = Dropout(0.5)(dense_1)
dense_2 = Dense(4096,
activation='relu',
name='dense_2',
weights=[dense_2_w, dense_2_b],
trainable=trainable)(dense_2)
dense_3 = Dropout(0.5)(dense_2)
dense_3 = Dense(out_dim,
name='dense_3',
weights=[tr_wts[20], tr_wts[21]],
trainable=trainable,
init='he_normal')(dense_3)
prediction = Activation("softmax", name="softmax")(dense_3)
elif layer_id == 4:
inputs = Input(shape=(256, 13, 13))
dense_1 = MaxPooling2D((3, 3), strides=(2, 2),
name="convpool_5")(inputs)
dense_1_w = tr_wts[16]
dense_1_b = tr_wts[17]
dense_2_w = tr_wts[18]
dense_2_b = tr_wts[19]
dense_1 = Flatten(name="flatten")(dense_1)
dense_1 = Dense(4096,
activation='relu',
name='dense_1',
weights=[dense_1_w, dense_1_b],
trainable=trainable)(dense_1)
dense_2 = Dropout(0.5)(dense_1)
dense_2 = Dense(4096,
activation='relu',
name='dense_2',
weights=[dense_2_w, dense_2_b],
trainable=trainable)(dense_2)
dense_3 = Dropout(0.5)(dense_2)
dense_3 = Dense(out_dim,
name='dense_3',
weights=[tr_wts[20], tr_wts[21]],
trainable=trainable,
init='he_normal')(dense_3)
prediction = Activation("softmax", name="softmax")(dense_3)
model = Model(input=inputs, output=prediction)
layer_dict = dict([(layer.name, layer) for layer in model.layers])
return model, layer_dict
def AlexNet(outdim=1000, weights_path=None, heatmap=False, l1=0, l2=0, usemil=False, usemymil=False, k=1., usemysoftmil=False, softmink=1., softmaxk=1.,\
sparsemil=False, sparsemill1=0., sparsemill2=0., saveact=False):
l1factor = l1
l2factor = l2
if heatmap:
inputs = Input(shape=(3, None, None))
else:
inputs = Input(shape=(3, 227, 227))
conv_1 = Convolution2D(96,
11,
11,
subsample=(4, 4),
activation='relu',
W_regularizer=l1l2(l1=l1factor, l2=l2factor),
name='conv_1')(inputs)
conv_2 = MaxPooling2D((3, 3), strides=(2, 2))(conv_1)
conv_2 = crosschannelnormalization(name="convpool_1")(conv_2)
conv_2 = ZeroPadding2D((2, 2))(conv_2)
conv_2 = merge([
Convolution2D(128,
5,
5,
activation="relu",
name='conv_2_' + str(i + 1),
W_regularizer=l1l2(l1=l1factor, l2=l2factor))(
splittensor(ratio_split=2, id_split=i)(conv_2))
for i in range(2)
],
mode='concat',
concat_axis=1,
name="conv_2")
conv_3 = MaxPooling2D((3, 3), strides=(2, 2))(conv_2)
conv_3 = crosschannelnormalization()(conv_3)
conv_3 = ZeroPadding2D((1, 1))(conv_3)
conv_3 = Convolution2D(384,
3,
3,
activation='relu',
name='conv_3',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(conv_3)
conv_4 = ZeroPadding2D((1, 1))(conv_3)
conv_4 = merge([
Convolution2D(192,
3,
3,
activation="relu",
name='conv_4_' + str(i + 1),
W_regularizer=l1l2(l1=l1factor, l2=l2factor))(
splittensor(ratio_split=2, id_split=i)(conv_4))
for i in range(2)
],
mode='concat',
concat_axis=1,
name="conv_4")
conv_5 = ZeroPadding2D((1, 1))(conv_4)
conv_5 = merge([
Convolution2D(128,
3,
3,
activation="relu",
name='conv_5_' + str(i + 1),
W_regularizer=l1l2(l1=l1factor, l2=l2factor))(
splittensor(ratio_split=2, id_split=i)(conv_5))
for i in range(2)
],
mode='concat',
concat_axis=1,
name="conv_5")
dense_1 = MaxPooling2D((3, 3), strides=(2, 2), name="convpool_5")(conv_5)
if heatmap:
dense_1 = Convolution2D(4096,
6,
6,
activation="relu",
name="dense_1",
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_1)
dense_2 = Convolution2D(4096,
1,
1,
activation="relu",
name="dense_2",
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_1)
dense_3 = Convolution2D(outdim,
1,
1,
name="dense_3",
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_2)
prediction = Softmax4D(axis=1, name="softmax")(dense_3)
elif usemil:
dense_1 = Convolution2D(128,
1,
1,
activation='relu',
name='mil_1',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_1)
dense_2 = Convolution2D(128,
1,
1,
activation='relu',
name='mil_2',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_1)
dense_3 = Convolution2D(outdim,
1,
1,
name='mil_3',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_2)
prediction_1 = Softmax4D(axis=1, name='softmax')(dense_3)
#prediction = Flatten(name='flatten')(prediction_1)
#dense_3 = Dense(outdim,name='dense_3',W_regularizer=l1l2(l1=l1factor, l2=l2factor))(prediction)
#prediction = Activation("softmax",name="softmax2")(dense_3)
prediction_1 = MaxPooling2D((6, 6), name='output')(prediction_1)
prediction = Flatten(name='flatten')(prediction_1)
prediction = Recalc(axis=1, name='Recalcmil')(prediction)
elif usemymil:
dense_1 = Convolution2D(128,
1,
1,
activation='relu',
name='mil_1',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_1)
dense_2 = Convolution2D(128,
1,
1,
activation='relu',
name='mil_2',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_1)
dense_3 = Convolution2D(1,
1,
1,
activation='sigmoid',
name='mil_3',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_2)
#prediction_1 = Softmax4D(axis=1, name='softmax')(dense_3)
#prediction = ExtractDim(axis=1, name='extract')(prediction_1)
prediction = Flatten(name='flatten')(dense_3)
prediction = ReRank(k=k, label=1, name='output')(prediction)
elif usemysoftmil:
dense_1 = Convolution2D(128,
1,
1,
activation='relu',
name='mil_1',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_1)
dense_2 = Convolution2D(128,
1,
1,
activation='relu',
name='mil_2',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_1)
dense_3 = Convolution2D(1,
1,
1,
activation='sigmoid',
name='mil_3',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_2)
#prediction_1 = Softmax4D(axis=1, name='softmax')(dense_3)
#prediction = ExtractDim(axis=1, name='extract')(prediction_1)
prediction = Flatten(name='flatten')(dense_3)
prediction = SoftReRank(softmink=softmink,
softmaxk=softmaxk,
label=1,
name='output')(prediction)
elif sparsemil:
dense_1 = Convolution2D(128,
1,
1,
activation='relu',
name='mil_1',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_1)
dense_2 = Convolution2D(128,
1,
1,
activation='relu',
name='mil_2',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_1)
prediction_1 = Convolution2D(1,1,1,activation='sigmoid',name='mil_3',W_regularizer=l1l2(l1=l1factor, l2=l2factor),\
activity_regularizer=activity_l1l2(l1=sparsemill1, l2=sparsemill2))(dense_2)
# prediction_1 = Softmax4D(axis=1, name='softmax')(prediction_1)
#dense_3 = Convolution2D(outdim,1,1,name='mil_3',W_regularizer=l1l2(l1=l1factor, l2=l2factor))(dense_2)
#prediction_1 = Softmax4D(axis=1, name='softmax')(dense_3)
#prediction_1 = ActivityRegularizerOneDim(l1=sparsemill1, l2=sparsemill2)(prediction_1)
#prediction = MaxPooling2D((6,6), name='output')(prediction_1)
# prediction_1 = Convolution2D(1, 3, 3, activation='sigmoid', border_mode='same', name='smooth', \
# W_regularizer=l1l2(l1=l1factor, l2=l2factor), activity_regularizer=activity_l1l2(l1=sparsemill1, l2=sparsemill2))(prediction_1)
prediction = Flatten(name='flatten')(prediction_1)
if saveact:
model = Model(input=inputs, output=prediction)
return model
prediction = RecalcExpand(axis=1, name='Recalcmil')(prediction)
else:
dense_1 = Flatten(name="flatten")(dense_1)
dense_1 = Dense(4096,
activation='relu',
name='dense_1',
W_regularizer=l1l2(l1=l1factor, l2=l2factor))(dense_1)
dense_2 = Dropout(0.5)(dense_1)
dense_2 = Dense(4096,
activation='relu',
name='dense_2',
W_regularizer=l1l2(l1=l1factor, l2=l2factor))(dense_2)
dense_3 = Dropout(0.5)(dense_2)
dense_3 = Dense(outdim,
name='dense_3',
W_regularizer=l1l2(l1=l1factor, l2=l2factor))(dense_3)
prediction = Activation("softmax", name="softmax")(dense_3)
model = Model(input=inputs, output=prediction)
if weights_path:
model.load_weights(weights_path)
return model
def load_svm_model(nb_class, weights_path=None):
inputs = Input(shape=(3,227,227))
conv_1 = Convolution2D(96, 11, 11,subsample=(4,4),activation='relu',
name='conv_1')(inputs)
conv_2 = MaxPooling2D((3, 3), strides=(2,2))(conv_1)
conv_2 = crosschannelnormalization(name="convpool_1")(conv_2)
conv_2 = ZeroPadding2D((2,2))(conv_2)
conv_2 = merge([
Convolution2D(128,5,5,activation="relu",name='conv_2_'+str(i+1))(
splittensor(ratio_split=2,id_split=i)(conv_2)
) for i in range(2)], mode='concat',concat_axis=1,name="conv_2")
conv_3 = MaxPooling2D((3, 3), strides=(2, 2))(conv_2)
conv_3 = crosschannelnormalization()(conv_3)
conv_3 = ZeroPadding2D((1,1))(conv_3)
conv_3 = Convolution2D(384,3,3,activation='relu',name='conv_3')(conv_3)
conv_4 = ZeroPadding2D((1,1))(conv_3)
conv_4 = merge([
Convolution2D(192,3,3,activation="relu",name='conv_4_'+str(i+1))(
splittensor(ratio_split=2,id_split=i)(conv_4)
) for i in range(2)], mode='concat',concat_axis=1,name="conv_4")
conv_5 = ZeroPadding2D((1,1))(conv_4)
conv_5 = merge([
Convolution2D(128,3,3,activation="relu",name='conv_5_'+str(i+1))(
splittensor(ratio_split=2,id_split=i)(conv_5)
) for i in range(2)], mode='concat',concat_axis=1,name="conv_5")
conv_5 = MaxPooling2D((3, 3), strides=(2,2),name="convpool_5")(conv_5)
dense_1 = Flatten(name="flatten")(conv_5)
dense_1 = Dense(4096, activation='relu',name='dense_1')(dense_1)
dense_2 = Dropout(0.5)(dense_1)
dense_2 = Dense(4096, activation='relu',name='dense_2')(dense_2)
dense_3 = Dropout(0.5)(dense_2)
dense_3 = Dense(nb_class,name='dense_3')(dense_3)
prediction = Activation("softmax",name="softmax")(dense_3)
base_model = Model(input=inputs, output=prediction)
if weights_path:
base_model.load_weights(weights_path)
#model = Model(input=inputs, output=conv_5)
base_model = Model(input=inputs, output=dense_2)
'''
for layer in base_model.layers:
layer.trainable = False
model = get_top_model_for_svm(
shape=base_model.output_shape[1:],
nb_class=nb_class,
weights_file_path="model/alex_topmodel" + str(fold_count) + ".h5",
input= base_model.input,
output= base_model.output)
'''
return base_model
