# Prepare model
base_model = tf.keras.applications.ResNet50(input_shape=IMG_SHAPE,
include_top=False,
weights='imagenet',
pooling='avg')
# base_model.summary()
def dprelu_layer_factory():
return DPReLU(shared_axes=[1, 2], name='dprelu')
# Replace ReLU activation layer
base_model = insert_layer_nonseq(base_model,
'.*relu.*',
dprelu_layer_factory,
position='replace')
# Fix possible problems with new model
base_model.save(work_dir + '/temp2.h5')
base_model = load_model(work_dir + '/temp2.h5',
custom_objects={'DPReLU': DPReLU})
print(base_model.summary())
model = tf.keras.Sequential(
[base_model, tf.keras.layers.Dense(10, activation='softmax')])
model.summary()
base_learning_rate = 0.0001
base_model.compile(
# Prepare model
base_model = tf.keras.applications.ResNet50(input_shape=IMG_SHAPE,
include_top=False,
weights='imagenet',
pooling='avg')
# base_model.summary()
def normal_layer_factory():
return tf.keras.layers.Layer(name='nl')
# Skip batch normalization layer
base_model = insert_layer_nonseq(base_model,
'.*bn',
normal_layer_factory,
position='replace')
# Fix possible problems with new model
base_model.save(work_dir + '/temp1.h5')
base_model = load_model(work_dir + '/temp1.h5')
print(base_model.summary())
model = tf.keras.Sequential(
[base_model, tf.keras.layers.Dense(10, activation='softmax')])
model.summary()
base_learning_rate = 0.0001
model.compile(
optimizer=tf.keras.optimizers.Adam(lr=base_learning_rate),
def normal_layer_factory():
return tf.keras.layers.Layer(name='nl')
# Prepare the model
base_model = tf.keras.applications.ResNet50(input_shape=IMG_SHAPE,
include_top=False,
weights=None,
pooling='avg')
# Replace ReLU activation layer
if args.activation == 'dprelu':
base_model = insert_layer_nonseq(base_model,
'.*relu.*',
dprelu_layer_factory,
position='replace')
# Fix possible problems with new model
base_model.save(work_dir + '/temp.h5')
base_model = load_model(work_dir + '/temp.h5',
custom_objects={'DPReLU': DPReLU})
base_model = insert_layer_nonseq(base_model,
'.*out.*',
dprelu_layer_factory,
position='replace')
# Fix possible problems with new model
base_model.save(work_dir + '/temp.h5')
base_model = load_model(work_dir + '/temp.h5',
custom_objects={'DPReLU': DPReLU})