Documentation for KerasDnnRegressor
Wrapper class for a regressionbased Keras model.
See Keras API.
Examples:
1 2 3 4 5 6 7 

__init__(self, hidden_layer_sizes=None, learning_rate=0.01, loss='mean_squared_error', epochs=10, nn_batch_size=64, metrics=None, validation_split=0.1, callbacks=None, batch_normalization=True, verbosity=0, dropout_rate=0.2, activations='relu', optimizer='adam')
special
Initialize the object.
Parameters:
Name  Type  Description  Default 

hidden_layer_sizes 
int 
Number of perceptrons per layer. 
None 
learning_rate 
float 
Step size of the learning adjustment. 
0.01 
loss 
str 
Loss function. 
'mean_squared_error' 
epochs 
int 
Number of arbitrary cutoffs, generally defined as "one pass over the entire dataset", used to separate training into distinct phases, which is useful for logging and periodic evaluation. 
10 
nn_batch_size 
int 
Typically the batch_size. A batch is a set of nn_batch_size samples. The samples in a batch are processed independently, in parallel. If training, a batch results in only one update to the model. 
64 
metrics 
list 
List of evaluate metrics. 
None 
callbacks 
list 
Within Keras, there is the ability to add callbacks specifically designed to be run at the end of an epoch. Examples of these are learning rate changes and model checkpointing (saving). 
None 
validation_split 
float 
Split size of validation set. 
0.1 
batch_normalization 
bool 
Batch normalization applies a transformation that maintains the mean output close to 0 and the output standard deviation close to 1. 
True 
verbosity 
int 
The level of verbosity, 0 is least talkative and gives only warn and error, 1 gives adds info and 2 adds debug. 
0 
dropout_rate 
Union[float, list] 
A Dropout layer applies random dropout and rescales the output. In inference mode, the same layer does nothing. Float > added behind each layer List > Same size as hidden_layer_size 
0.2 
activations 
Union[str, list] 
Activation function. 
'relu' 
optimizer 
Union[tensorflow.python.keras.optimizer_v2.optimizer_v2.OptimizerV2, str] 
Optimization algorithm. 
'adam' 
Source code in photonai/modelwrapper/keras_dnn_regressor.py
def __init__(self,
hidden_layer_sizes: int = None,
learning_rate: float = 0.01,
loss: str = "mean_squared_error",
epochs: int = 10,
nn_batch_size: int = 64,
metrics: list = None,
validation_split: float = 0.1,
callbacks: list = None,
batch_normalization: bool = True,
verbosity: int = 0,
dropout_rate: Union[float, list] = 0.2,
activations: Union[str, list] = 'relu',
optimizer: Union[Optimizer, str] = "adam"):
"""
Initialize the object.
Parameters:
hidden_layer_sizes:
Number of perceptrons per layer.
learning_rate:
Step size of the learning adjustment.
loss:
Loss function.
epochs:
Number of arbitrary cutoffs, generally defined as
"one pass over the entire dataset", used to separate training into distinct phases,
which is useful for logging and periodic evaluation.
nn_batch_size:
Typically the batch_size. A batch is a set of nn_batch_size samples.
The samples in a batch are processed independently, in parallel.
If training, a batch results in only one update to the model.
metrics:
List of evaluate metrics.
callbacks:
Within Keras, there is the ability to add callbacks specifically designed
to be run at the end of an epoch. Examples of these
are learning rate changes and model checkpointing (saving).
validation_split:
Split size of validation set.
batch_normalization:
Batch normalization applies a transformation that maintains
the mean output close to 0 and the output standard deviation close to 1.
verbosity:
The level of verbosity, 0 is least talkative and
gives only warn and error, 1 gives adds info and 2 adds debug.
dropout_rate:
A Dropout layer applies random dropout and rescales the output.
In inference mode, the same layer does nothing.
Float > added behind each layer
List > Same size as hidden_layer_size
activations:
Activation function.
optimizer:
Optimization algorithm.
"""
self._loss = ""
self._multi_class = None
self.loss = loss
self.epochs = epochs
self.nn_batch_size = nn_batch_size
self.validation_split = validation_split
if callbacks:
self.callbacks = callbacks
else:
self.callbacks = []
if not metrics:
metrics = ['mean_squared_error']
super(KerasDnnRegressor, self).__init__(hidden_layer_sizes=hidden_layer_sizes,
target_activation="linear",
target_dimension=1,
learning_rate=learning_rate,
loss=loss,
metrics=metrics,
batch_normalization=batch_normalization,
verbosity=verbosity,
dropout_rate=dropout_rate,
activations=activations,
optimizer=optimizer)
fit(self, X, y)
Starting the learning.
Parameters:
Name  Type  Description  Default 

X 
ndarray 
The input samples with shape [n_samples, n_features]. 
required 
y 
ndarray 
The input targets with shape [n_samples, 1]. 
required 
Source code in photonai/modelwrapper/keras_dnn_regressor.py
def fit(self, X: np.ndarray, y: np.ndarray):
"""
Starting the learning.
Parameters:
X:
The input samples with shape [n_samples, n_features].
y:
The input targets with shape [n_samples, 1].
"""
self.create_model(X.shape[1])
super(KerasDnnBaseModel, self).fit(X, y)
return self