Source code for epynn.pooling.models

# EpyNN/epynn/pool/
# Related third party imports
import numpy as np

# Local application/library specific imports
from epynn.commons.models import Layer
from epynn.pooling.forward import pooling_forward
from epynn.pooling.backward import pooling_backward
from epynn.pooling.parameters import (

[docs]class Pooling(Layer): """ Definition of a pooling layer prototype. :param pool_size: Height and width for pooling window, defaults to `(2, 2)`. :type pool_size: int or tuple[int], optional :param strides: Height and width to shift the pooling window by, defaults to `None` which equals `pool_size`. :type strides: int or tuple[int], optional :param pool: Pooling activation of units, defaults to :func:`np.max`. Use one of min or max pooling. :type pool: function, optional """ def __init__(self, pool_size=(2, 2), strides=None, pool=np.max): super().__init__() pool_size = pool_size if isinstance(pool_size, tuple) else (pool_size, pool_size) strides = strides if isinstance(strides, tuple) else pool_size self.d['ph'], self.d['pw'] = pool_size self.d['sh'], self.d['sw'] = strides self.pool = pool self.activation = { 'pool': self.pool.__name__ } self.trainable = False return None
[docs] def compute_shapes(self, A): """Wrapper for :func:`epynn.pooling.parameters.pooling_compute_shapes()`. :param A: Output of forward propagation from previous layer. :type A: :class:`numpy.ndarray` """ pooling_compute_shapes(self, A) return None
[docs] def initialize_parameters(self): """Wrapper for :func:`epynn.pooling.parameters.initialize_parameters()`. """ pooling_initialize_parameters(self) return None
[docs] def forward(self, A): """Wrapper for :func:`epynn.pooling.forward.pooling_forward()`. :param A: Output of forward propagation from previous layer. :type A: :class:`numpy.ndarray` :return: Output of forward propagation for current layer. :rtype: :class:`numpy.ndarray` """ self.compute_shapes(A) A = pooling_forward(self, A) self.update_shapes(self.fc, self.fs) return A
[docs] def backward(self, dX): """Wrapper for :func:`epynn.pooling.backward.pooling_backward()`. :param dX: Output of backward propagation from next layer. :type dX: :class:`numpy.ndarray` :return: Output of backward propagation for current layer. :rtype: :class:`numpy.ndarray` """ dX = pooling_backward(self, dX) self.update_shapes(self.bc, return dX
[docs] def compute_gradients(self): """Wrapper for :func:`epynn.pooling.parameters.pooling_compute_gradients()`. Dummy method, there are no gradients to compute in layer. """ pooling_compute_gradients(self) return None
[docs] def update_parameters(self): """Wrapper for :func:`epynn.pooling.parameters.pooling_update_parameters()`. Dummy method, there are no parameters to update in layer. """ if self.trainable: pooling_update_parameters(self) return None