Sequential

  • namespace: Rindow\NeuralNetworks\Model
  • classname: Sequential

The Sequential model is a linear stack of layers.

Methods

constructor

$builer->Sequential(
    array<Layer> $layers=null
)
return $model

You can create a Sequential model by passing a list of layer instances with the Builder.

  • layers: a list of layer instances.

Examples

$model = $nn->models()->Sequential([
    $nn->layers()->Dense(128,['input_shape'=>[10]]),
    $nn->layers()->Sigmoid(),
    $nn->layers()->Dense(1),
    $nn->layers()->Sigmoid(),
]);

add

public function add(
    Layer $layer
) : void

You can simply add layers via the add() method:

  • layer: layer instance.

Examples

$model = $nn->models()->Sequential();
$model->add($nn->layers()->Dense(128,['input_shape'=>[784]]));
$model->add($nn->layers()->Sigmoid());

compile

public function compile(
    array $options=[
        'optimizer'=>'SGD',
        'loss'=>'SparseCategoricalCrossEntropy',
        'metrics'=>array<string> $metrics=['loss','accuracy'],
    ]=null
) : void

Compile a model to configure the learning process. Several options can be specified.

  • optimizer:
    • Specify the optimizer instance.
    • Default is the SGD.
  • loss:
    • Specify an instance of the loss function.
    • Default is the SparseCategoricalCrossEntropy.
  • metrics:
    • Specify the type of metrics in the list.
    • The default is [‘loss’, ‘accuracy’].

Examples

# For Adam,MeanSquaredError
$model->compile([
    'optimizer'=>$nn->optimizers()->Adam(),
    'loss'=>$nn->losses()->MeanSquaredError(),
    'metrics'=>['accuracy','loss'],
]);

# For Defaults: SparseCategoricalCrossEntropy, SGD
$model->compile();

fit

public function fit(
        NDArray $inputs,
        NDArray $tests,
        array $options=[
            'batch_size'=>32,
            'epochs'=>1,
            'verbose'=>1,
            'validation_data'=>array<NDArray>$val_data=null,
            'shuffle'=>true,
        ]=null
) : array
return array $history

The models are trained on the NDArray of input data and labels. For training a model, you will typically use the fit method.

  • inputs: Input data in most cases.
    • Normally, the input data is in the form of batch data.
    • NDArray datasets.
  • tests: Test target data in most cases.
    • The test data is arranged in the order corresponding to the input data.
    • NDArray datasets.

Several options can be specified.

  • batch_size: Batch size
    • Number of samples per paramator values update.
    • Default is 32.
  • epochs: Specify an instance of the loss function.
    • How many times to train the model repeatedly
    • Default is 1.
  • verbose: Verbosity mode.
    • 0 = silent, 1 or greater = progress bar and metrics per epoch.
    • The default is 1.
  • validation_data: evaluate data set.
    • Data on which to evaluate the loss and any model metrics at the end of each epoch.
    • List [inputs_val, tests_val] of NDArray
    • The default is Null. Mean not to evaluate
  • shuffle: Shuffle mode.
    • Boolean whether to shuffle the training data before each epoch
    • The default is true.

Returns

  • history: metrics history
    • Array with key as metrics name which is the list of float values.

Examples

# Train the model
$history = $model->fit($data,$labels,[
        'epochs'=>10,'batch_size'=>8,'verbose'=>1,
        'validation_data'=>[$inputs_val, $tests_val],
        'shuffle'=>true,
]);
$loss = $mo->array($history['loss']);
$accuracy = $mo->array($history['accuracy']);

evaluate

public function evaluate(
    NDArray $inputs,
    NDArray $tests,
    array $options=[
        'batch_size'=>32,
        'verbose'=>0,
    ]=null
) : array<float>
return [$loss,$accuracy]

Returns the loss value & metrics values for the model in test mode.

The models evaluate on the NDArray of input data and tests.

  • inputs: Input data in most cases.
    • Normally, the input data is in the form of batch data.
    • NDArray datasets.
  • tests: Test target data in most cases.
    • The test data is arranged in the order corresponding to the input data.
    • NDArray datasets.

Several options can be specified.

  • batch_size: Batch size
    • Number of samples per paramator values update.
    • Default is 32.
  • verbose: Verbosity mode.
    • 0 = silent, 1 or greater = progress bar and metrics per epoch.
    • The default is 0.

Returns

  • loss: test loss
  • accuracy: test accuracy

Examples

[$loss,$accuracy] = $model->evaluate($data,$labels,[
        'batch_size'=>8,'verbose'=>1,
]);

predict

public function predict(
    NDArray $inputs
) : NDArray
return $predictions

Returns predictions for sample data.

The models predict on the NDArray of input data.

  • inputs: Input data in most cases.
    • Normally, the input data is in the form of batch data.
    • NDArray datasets.

Returns

  • predictions: test accuracy
    • The prediction data is arranged in the order corresponding to the input data.
    • NDArray datasets.

Examples

$data = $mo->array([[1,2,3],[4,5,6]]);
$predictions = $model->predict($data);

toJson

public function toJson() : string
return $json

Returns a JSON string containing the model configuration. To load a model from a JSON save file, extract to array and use the “modelFromConfig” method of the models builder.

Model configuration does not include weight information.

  • json: JSON string

Examples

$json = $model->toJson();
file_put_contents(__DIR__.'/../data/sample.json',$json);

saveWeights

public function saveWeights(
    &$modelWeights,
    $portable=false
) : void

Saves all layer weights.

  • modelWeights: model weights container
    • Specify the save destination objecct or array.
  • portable: Save mode.
    • true: Save in a hardware-independent format. However, the conversion takes time and the weight information has an error due to the conversion.
    • false:Save in a hardware-dependent format. You can save at high speed.

Examples

$modelWeights = [];
$model->saveWeights(
    $modelWeights, $portable=true);

loadWeights

public function loadWeights(
    $modelWeights
) : void

Load the weight information saved by saveWeights into the model.

  • modelWeights: model weights container

Examples

$model->loadWeights($modelWeights);

save

public function save(
    $filepath,
    $portable=null) : void

Saves the model configure and weight information to file. To load a model from a saved file, use the “loadModel” method of the models builder.

  • filepath: file name
    • Specify the save destination file name.
  • portable: Save mode.
    • true: Save in a hardware-independent format. However, the conversion takes time and the weight information has an error due to the conversion.
    • false: Save in a hardware-dependent format. You can save at high speed. ]

Examples

$model->save(__DIR__.'../data/sample.model');