teras.models.GAINDiscriminator

class teras.models.GAINDiscriminator(data_dim, hidden_dims=None, activation_hidden='relu', activation_out='sigmoid', **kwargs)

Discriminator model for the GAIN architecture proposed by Jinsung Yoon et al. in the paper GAIN: Missing Data Imputation using Generative Adversarial Nets.

Note that the Generator and Discriminator share the exact same architecture by default. They differ in the inputs they receive and their loss functions.

Reference(s):

https://arxiv.org/abs/1806.02920

Parameters:

• data_dim (int) – int, The dimensionality of the input dataset. Note the dimensionality must be equal to the dimensionality of the transformed dataset that is passed to the fit method and not that of original dataset as the dimensionality of raw input dataset may change during transformation. One way to access the dimensionality of the transformed dataset is through the .data_dim attribute of the GAINDataSampler instance used in sampling the dataset.

• hidden_dims (Union[List[int], Tuple[int]]) – list, A list of hidden dimensionalities for constructing hidden block. For each value, a Dense layer of that dimensionality is added to the hidden block. By default, units_values = [data_dim, data_dim].

• activation_hidden (Union[str, Callable, Layer]) – Activation function to use for the hidden layers in the hidden block. Defaults to “relu”.

• activation_out (Union[str, Callable, Layer]) – Activation function to use for the output layer of the Discriminator. Defaults to “sigmoid”

__init__(data_dim, hidden_dims=None, activation_hidden='relu', activation_out='sigmoid', **kwargs)

Methods

__init__(data_dim[, hidden_dims, ...])
add_loss(loss)	Can be called inside of the call() method to add a scalar loss.
add_metric()
add_variable(shape, initializer[, dtype, ...])	Add a weight variable to the layer.
add_weight([shape, initializer, dtype, ...])	Add a weight variable to the layer.
build(input_shape)
build_from_config(config)	Builds the layer's states with the supplied config dict.
call(inputs, **kwargs)
compile([optimizer, loss, loss_weights, ...])	Configures the model for training.
compile_from_config(config)	Compiles the model with the information given in config.
compiled_loss(y, y_pred[, sample_weight, ...])
compute_loss([x, y, y_pred, sample_weight, ...])	Compute the total loss, validate it, and return it.
compute_mask(inputs, previous_mask)
compute_metrics(x, y, y_pred[, sample_weight])	Update metric states and collect all metrics to be returned.
compute_output_shape(input_shape)
compute_output_spec(*args, **kwargs)
count_params()	Count the total number of scalars composing the weights.
evaluate([x, y, batch_size, verbose, ...])	Returns the loss value & metrics values for the model in test mode.
export(filepath[, format])	Create a TF SavedModel artifact for inference.
fit([x, y, batch_size, epochs, verbose, ...])	Trains the model for a fixed number of epochs (dataset iterations).
from_config(config)	Creates an operation from its config.
get_build_config()	Returns a dictionary with the layer's input shape.
get_compile_config()	Returns a serialized config with information for compiling the model.
get_config()	Returns the config of the object.
get_layer([name, index])	Retrieves a layer based on either its name (unique) or index.
get_metrics_result()	Returns the model's metrics values as a dict.
get_weights()	Return the values of layer.weights as a list of NumPy arrays.
load_own_variables(store)	Loads the state of the layer.
load_weights(filepath[, skip_mismatch])	Load weights from a file saved via save_weights().
loss(y, y_pred[, sample_weight])
make_predict_function([force])
make_test_function([force])
make_train_function([force])
predict(x[, batch_size, verbose, steps, ...])	Generates output predictions for the input samples.
predict_on_batch(x)	Returns predictions for a single batch of samples.
predict_step(data)
quantize(mode)	Quantize the weights of the model.
quantized_call(*args, **kwargs)
reset_metrics()
save(filepath[, overwrite, zipped])	Saves a model as a .keras file.
save_own_variables(store)	Saves the state of the layer.
save_weights(filepath[, overwrite])	Saves all layer weights to a .weights.h5 file.
set_weights(weights)	Sets the values of layer.weights from a list of NumPy arrays.
stateless_call(trainable_variables, ...[, ...])	Call the layer without any side effects.
stateless_compute_loss(trainable_variables, ...)
summary([line_length, positions, print_fn, ...])	Prints a string summary of the network.
symbolic_call(*args, **kwargs)
test_on_batch(x[, y, sample_weight, return_dict])	Test the model on a single batch of samples.
test_step(data)
to_json(**kwargs)	Returns a JSON string containing the network configuration.
train_on_batch(x[, y, sample_weight, ...])	Runs a single gradient update on a single batch of data.
train_step(data)

Attributes

compiled_metrics
compute_dtype	The dtype of the computations performed by the layer.
distribute_reduction_method
distribute_strategy
dtype	Alias of layer.variable_dtype.
dtype_policy
input	Retrieves the input tensor(s) of a symbolic operation.
input_dtype	The dtype layer inputs should be converted to.
input_spec
jit_compile
layers
losses	List of scalar losses from add_loss, regularizers and sublayers.
metrics	List of all metrics.
metrics_names
metrics_variables	List of all metric variables.
non_trainable_variables	List of all non-trainable layer state.
non_trainable_weights	List of all non-trainable weight variables of the layer.
output	Retrieves the output tensor(s) of a layer.
path	The path of the layer.
quantization_mode	The quantization mode of this layer, None if not quantized.
run_eagerly
supports_masking	Whether this layer supports computing a mask using compute_mask.
trainable	Settable boolean, whether this layer should be trainable or not.
trainable_variables	List of all trainable layer state.
trainable_weights	List of all trainable weight variables of the layer.
variable_dtype	The dtype of the state (weights) of the layer.
variables	List of all layer state, including random seeds.
weights	List of all weight variables of the layer.