scarecrow1123

{
    // ....
    "trainer": {
        "type": "fp16-trainer",
        "mixed_precision": true,
        // other options
    }
    // ....
}

JohnGiorgi

import torch
from torch.nn.modules.dropout import _DropoutNd


class WordDropout(_DropoutNd):
    """During training, randomly replaces some of the elements of the input tensor with
    `dropout_constant` with probability `p` using samples from a Bernoulli distriution. Each channel
    will be zerored out independently on every forward call.

    Input is expected to be a 2D tensor of indices representing tokenized sentences.

JohnGiorgi

import torch


class BiaffineAttention(torch.nn.Module):
    """Implements a biaffine attention operator for binary relation classification.

    PyTorch implementation of the biaffine attention operator from "End-to-end neural relation
    extraction using deep biaffine attention" (https://arxiv.org/abs/1812.11275) which can be used
    as a classifier for binary relation classification.

Mahedi-61

#!/bin/bash

### steps ####
# Verify the system has a cuda-capable gpu
# Download and install the nvidia cuda toolkit and cudnn
# Setup environmental variables
# Verify the installation
###

### to verify your gpu is cuda enable check

cbaziotis

def dot_product(x, kernel):
    """
    Wrapper for dot product operation, in order to be compatible with both
    Theano and Tensorflow
    Args:
        x (): input
        kernel (): weights
    Returns:
    """
    if K.backend() == 'tensorflow':

kevin-smets

Default

Powerlevel10k