Encoder¶
Inheritance Diagram
-
class
ashpy.models.convolutional.encoders.
Encoder
(layer_spec_input_res, layer_spec_target_res, kernel_size, initial_filters, filters_cap, output_shape, use_dropout=True, dropout_prob=0.3, non_linearity=<class 'tensorflow.python.keras.layers.advanced_activations.LeakyReLU'>)[source]¶ Bases:
ashpy.models.convolutional.interfaces.Conv2DInterface
Primitive Model for all encoder (i.e., convolution) based architecture.
Notes
Default to DCGAN Discriminator architecture.
Examples
Direct Usage:
dummy_generator = Encoder( layer_spec_input_res=(64, 64), layer_spec_target_res=(8, 8), kernel_size=5, initial_filters=4, filters_cap=128, output_shape=1, )
Subclassing
class DummyDiscriminator(Encoder): def call(self, inputs, training=True): print("Dummy Discriminator!") # build the model using # self._layers and inputs return inputs dummy_discriminator = DummyDiscriminator( layer_spec_input_res=(64, 64), layer_spec_target_res=(8, 8), kernel_size=5, initial_filters=16, filters_cap=128, output_shape=1, ) dummy_discriminator(tf.zeros((1,28,28,3)))
Dummy Discriminator!
Methods
__init__
(layer_spec_input_res, …[, …])Instantiate the
Decoder
.Attributes
activity_regularizer
Optional regularizer function for the output of this layer.
dtype
dynamic
inbound_nodes
Deprecated, do NOT use! Only for compatibility with external Keras.
input
Retrieves the input tensor(s) of a layer.
input_mask
Retrieves the input mask tensor(s) of a layer.
input_shape
Retrieves the input shape(s) of a layer.
input_spec
Gets the network’s input specs.
layers
losses
Losses which are associated with this Layer.
metrics
Returns the model’s metrics added using compile, add_metric APIs.
metrics_names
Returns the model’s display labels for all outputs.
name
Returns the name of this module as passed or determined in the ctor.
name_scope
Returns a tf.name_scope instance for this class.
non_trainable_variables
non_trainable_weights
outbound_nodes
Deprecated, do NOT use! Only for compatibility with external Keras.
output
Retrieves the output tensor(s) of a layer.
output_mask
Retrieves the output mask tensor(s) of a layer.
output_shape
Retrieves the output shape(s) of a layer.
run_eagerly
Settable attribute indicating whether the model should run eagerly.
sample_weights
state_updates
Returns the updates from all layers that are stateful.
stateful
submodules
Sequence of all sub-modules.
trainable
trainable_variables
Sequence of variables owned by this module and it’s submodules.
trainable_weights
updates
variables
Returns the list of all layer variables/weights.
weights
Returns the list of all layer variables/weights.
-
__init__
(layer_spec_input_res, layer_spec_target_res, kernel_size, initial_filters, filters_cap, output_shape, use_dropout=True, dropout_prob=0.3, non_linearity=<class 'tensorflow.python.keras.layers.advanced_activations.LeakyReLU'>)[source]¶ Instantiate the
Decoder
.- Parameters
layer_spec_input_res (
tuple
of (int
,int
)) – Shape of the input tensors.layer_spec_target_res (
Union
[int
,Tuple
[int
,int
]]) – (tuple
of (int
,int
)): Shape of tensor desired as output of_get_layer_spec()
.kernel_size (int) – Kernel used by the convolution layers.
initial_filters (int) – Numbers of filters to used as a base value.
filters_cap (int) – Cap filters to a set amount, in the case of an Encoder is a ceil value AKA the max amount of filters.
output_shape (int) – Amount of units of the last
tf.keras.layers.Dense
.
- Returns
- Raises
ValueError – If filters_cap < initial_filters
-
_add_building_block
(filters)[source]¶ Construct the core of the
tf.keras.Model
.The layers specified here get added to the
tf.keras.Model
multiple times consuming the hyper-parameters generated in the_get_layer_spec()
.- Parameters
filters (int) – Number of filters to use for this iteration of the Building Block.
-
_add_final_block
(output_shape)[source]¶ Prepare the results of
_add_building_block()
for the final output.- Parameters
output_shape (int) – Amount of units of the last
tf.keras.layers.Dense