simplenet package¶
Submodules¶
simplenet.simplenet module¶
simplenet.simplenet :: Define SimpleNet class and common functions.
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class
simplenet.simplenet.
SimpleNet
(hidden_layer_sizes: typing.Sequence[int], input_shape: typing.Tuple[int, int], output_shape: typing.Tuple[int, int], activation_function: typing.Callable[..., numpy.ndarray] = <function sigmoid>, output_activation: typing.Callable[..., numpy.ndarray] = <function sigmoid>, loss_function: typing.Callable[..., float] = <function neg_log_likelihood>, learning_rate: float = 1.0, dtype: str = 'float32', seed: int = None) → None[source]¶ Bases:
object
Simple example of a multilayer perceptron.
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__init__
(hidden_layer_sizes: typing.Sequence[int], input_shape: typing.Tuple[int, int], output_shape: typing.Tuple[int, int], activation_function: typing.Callable[..., numpy.ndarray] = <function sigmoid>, output_activation: typing.Callable[..., numpy.ndarray] = <function sigmoid>, loss_function: typing.Callable[..., float] = <function neg_log_likelihood>, learning_rate: float = 1.0, dtype: str = 'float32', seed: int = None) → None[source]¶ Initialize the MPL.
Parameters: - hidden_layer_sizes – Number of neurons in each hidden layer
- input_shape – Shape of inputs (m x n), use None for unknown m
- output_shape – Shape of outputs (m x o), use None for unknown m
- activation_function – Activation function for all layers prior to output
- output_activation – Activation function for output layer
- learning_rate – learning rate
- dtype – Data type for floats (e.g. np.float32 vs np.float64)
- seed – Optional random seed for consistent outputs (for debugging)
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export_model
(filename: str) → None[source]¶ Export the learned biases and weights to a file.
Saves each weight and bias in order with an index and a prefix of W or b to ensure it can be restored in the proper order.
Parameters: filename – Filename for the saved file.
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import_model
(filename: str) → None[source]¶ Import learned biases and weights from a file.
Parameters: filename – Name of file from which to import
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learn
(inputs: typing.Union[typing.Sequence[int], typing.Sequence[float], numpy.ndarray], targets: typing.Union[typing.Sequence[int], typing.Sequence[float], numpy.ndarray]) → None[source]¶ Perform a forward and backward pass, updating weights.
Parameters: - inputs – Array of input values
- targets – Array of true outputs
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predict
(inputs: typing.Union[typing.Sequence[int], typing.Sequence[float], numpy.ndarray]) → numpy.ndarray[source]¶ Use existing weights to predict outputs for given inputs.
Note: this method does not update weights.
Parameters: inputs – Array of inputs for which to make predictions Returns: Array of predictions
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validate
(inputs: numpy.ndarray, targets: numpy.ndarray, epsilon: float = 1e-07) → bool[source]¶ Use gradient checking to validate backpropagation.
This method uses a naive implementation of gradient checking to try to verify the analytic gradients.
Parameters: - inputs – Array of input values
- targets – Array of true outputs
- epsilon – Small value by which to perturb values for gradient checking
Returns: Boolean reflecting whether or not the gradients seem to match
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simplenet.simplenet.
cross_entropy
(y_hat: numpy.ndarray, targets: numpy.ndarray, der: bool = False) → float[source]¶ Calculate the categorical cross entropy loss.
Parameters: - y_hat – Array of predicted values from 0 to 1
- targets – Array of true values
Returns: Mean loss for the sample
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simplenet.simplenet.
neg_log_likelihood
(y_hat: numpy.ndarray, targets: numpy.ndarray, der: bool = False) → float[source]¶ Calculate the negative log likelihood loss.
I believe this is also called the binary cross-entropy loss function.
Parameters: - y_hat – Array of predicted values from 0 to 1
- targets – Array of true values
Returns: Mean loss for the sample
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simplenet.simplenet.
relu
(arr: numpy.ndarray, der: bool = False) → numpy.ndarray[source]¶ Calculate the relu activation function.
Parameters: - arr – Input array
- der – Whether to calculate the derivative
Returns: Array of outputs from 0 to maximum of the array in a given axis
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simplenet.simplenet.
sigmoid
(arr: numpy.ndarray, der: bool = False) → <built-in function array>[source]¶ Calculate the sigmoid activation function.
\[\frac{1}{1 + e ^ {-x}}\]Derivative:
\[x * (1 - x)\]Parameters: arr – Input array of weighted sums Returns: Array of outputs from 0 to 1
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simplenet.simplenet.
softmax
(arr: numpy.ndarray) → numpy.ndarray[source]¶ Calculate the softmax activation function.
This equation uses a “stable softmax” that subtracts the maximum from the exponents, but which should not change the results.
\[\frac{e^x}{\sum_{} {e^x}}\]Parameters: arr – Input array of weighted sums Returns: Array of outputs from 0 to 1
Module contents¶
simplenet :: Simple multilayer perceptron in Python using numpy.