def __build_model(self):
self.price_pred = Input((self.number_of_assets, ))
self.prev_weights = Input((self.number_of_assets, ))
price_pred_reshaped = Reshape(
(self.number_of_assets, 1, 1))(self.price_pred)
prev_weights_reshaped = Reshape(
(self.number_of_assets, 1, 1))(self.prev_weights)
network = Concatenate(axis=2)(
[price_pred_reshaped, prev_weights_reshaped])
network = Conv2D(16, (1, 2),
activation='relu',
kernel_initializer='VarianceScaling')(network)
network = Flatten()(network)
network = Dense(network.get_shape()[1],
'relu',
kernel_initializer='VarianceScaling')(network)
new_weights = Dense(self.number_of_assets,
'linear',
kernel_initializer='VarianceScaling')(network)
self.model = tf.keras.models.Model(
inputs=[self.price_pred, self.prev_weights], outputs=new_weights)
self.model.compile(optimizer='adam', loss=self.__get_loss_function())
def bn_feature_net_skip_2D(receptive_field=61,
input_shape=(256, 256, 1),
inputs=None,
fgbg_model=None,
n_skips=2,
last_only=True,
norm_method='std',
padding_mode='reflect',
**kwargs):
if K.image_data_format() == 'channels_first':
channel_axis = 1
else:
channel_axis = -1
inputs = Input(shape=input_shape)
img = ImageNormalization2D(norm_method=norm_method,
filter_size=receptive_field)(inputs)
models = []
model_outputs = []
if fgbg_model is not None:
for layer in fgbg_model.layers:
layer.trainable = False
models.append(fgbg_model)
fgbg_output = fgbg_model(inputs)
if isinstance(fgbg_output, list):
fgbg_output = fgbg_output[-1]
model_outputs.append(fgbg_output)
for _ in range(n_skips + 1):
if model_outputs:
model_input = Concatenate(axis=channel_axis)(
[img, model_outputs[-1]])
else:
model_input = img
new_input_shape = model_input.get_shape().as_list()[1:]
models.append(
bn_feature_net_2D(receptive_field=receptive_field,
input_shape=new_input_shape,
norm_method=None,
dilated=True,
padding=True,
padding_mode=padding_mode,
**kwargs))
model_outputs.append(models[-1](model_input))
if last_only:
model = Model(inputs=inputs, outputs=model_outputs[-1])
else:
if fgbg_model is None:
model = Model(inputs=inputs, outputs=model_outputs)
else:
model = Model(inputs=inputs, outputs=model_outputs[1:])
return model
def bn_feature_net_skip_3D(receptive_field=61,
input_shape=(5, 256, 256, 1),
fgbg_model=None,
last_only=True,
n_skips=2,
norm_method='std',
padding_mode='reflect',
**kwargs):
"""Creates a 3D featurenet with skip-connections.
Args:
receptive_field (int): the receptive field of the neural network.
input_shape (tuple): Create input tensor with this shape.
fgbg_model (tensorflow.keras.Model): Concatenate output of this model
with the inputs as a skip-connection.
last_only (bool): Model will only output the final prediction,
and not return any of the underlying model predictions.
n_skips (int): The number of skip-connections
norm_method (str): The type of ImageNormalization to use
padding_mode (str): Type of padding, one of 'reflect' or 'zero'
kwargs (dict): Other model options defined in bn_feature_net_3D
Returns:
tensorflow.keras.Model: 3D FeatureNet with skip-connections
"""
channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
inputs = Input(shape=input_shape)
img = ImageNormalization3D(norm_method=norm_method,
filter_size=receptive_field)(inputs)
models = []
model_outputs = []
if fgbg_model is not None:
for layer in fgbg_model.layers:
layer.trainable = False
models.append(fgbg_model)
fgbg_output = fgbg_model(inputs)
if isinstance(fgbg_output, list):
fgbg_output = fgbg_output[-1]
model_outputs.append(fgbg_output)
for _ in range(n_skips + 1):
if model_outputs:
model_input = Concatenate(axis=channel_axis)(
[img, model_outputs[-1]])
else:
model_input = img
new_input_shape = model_input.get_shape().as_list()[1:]
models.append(
bn_feature_net_3D(receptive_field=receptive_field,
input_shape=new_input_shape,
norm_method=None,
dilated=True,
padding=True,
padding_mode=padding_mode,
**kwargs))
model_outputs.append(models[-1](model_input))
if last_only:
model = Model(inputs=inputs, outputs=model_outputs[-1])
elif fgbg_model is None:
model = Model(inputs=inputs, outputs=model_outputs)
else:
model = Model(inputs=inputs, outputs=model_outputs[1:])
return model
