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Source code for nets.nn.functional

"""
Defines elementary functions used in Neural Network layers.
"""

import numpy as np
import nets


[docs]def dropout(t, prob=0.5): r"""Zeros elements from a ``Tensor`` with a probability ``prob``. .. math:: \text{dropout}(T) = T \times Z \quad \text{where} Z = (z_{i})_{i} \quad and z_i = \begin{cases} 1, &\quad p \ge prob \\ 0, &\quad else. \end{cases} Args: t (Tensor): tensor to zeros prob (float [0, 1]): probability to zero an element Returns: Tensor: input tensor with some zeros """ # Randomly generates number following a uniform distribution between [0, 1] probabilities = np.random.uniform(low=0.0, high=1.0, size=t.shape) # Generate a mask of (0, 1). 0 means probabilities[index] > prob, 1 else. mask = np.where(probabilities > prob, 0, 1) mask = nets.Tensor(mask) # Applies the mask to the tensor to get the dropout (elementwise multiplication) t_drop = t * mask return t_drop
# TODO: defines a single convolution filter def conv2d(t, filter): pass

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