chilin0525

masroorhasan

#!/usr/bin/env python
from __future__ import print_function
import argparse
import numpy as np
import time
tt = time.time()

import cv2
from grpc.beta import implementations

matheustguimaraes

Install CUDA 10.0 and cuDNN v7.4.2 on Ubuntu 16.04

Originally posted on: https://matheustguimaraes.com/blog/cuda-cudnn-ubuntu-installation

Install the latest NVIDIA driver

Update package lists, download and install NVIDIA driver

sudo apt-get update
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt install nvidia-410

himanshurawlani

import tensorflow as tf

# The export path contains the name and the version of the model
tf.keras.backend.set_learning_phase(0)  # Ignore dropout at inference
model = tf.keras.models.load_model('./inception.h5')
export_path = '../my_image_classifier/1'

# Fetch the Keras session and save the model
# The signature definition is defined by the input and output tensors
# And stored with the default serving key

zeyademam

Troubleshooting Convolutional Neural Networks

Intro

This is a list of hacks gathered primarily from prior experiences as well as online sources (most notably Stanford's CS231n course notes) on how to troubleshoot the performance of a convolutional neural network . We will focus mainly on supervised learning using deep neural networks. While this guide assumes the user is coding in Python3.6 using tensorflow (TF), it can still be helpful as a language agnostic guide.

Suppose we are given a convolutional neural network to train and evaluate and assume the evaluation results are worse than expected. The following are steps to troubleshoot and potentially improve performance. The first section corresponds to must-do's and generally good practices before you start troubleshooting. Every subsequent section header corresponds to a problem and the section is devoted to solving it. The sections are ordered to reflect "more common" issues first and under each header the "most-eas

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

DylanModesitt

from keras import backend as K
from keras import regularizers, constraints, initializers, activations
from keras.layers.recurrent import RNN, Layer, _generate_dropout_mask, _generate_dropout_ones
from keras.engine import InputSpec
from keras.legacy import interfaces
import warnings


# Copied from original keras source
def _time_distributed_dense(x, w, b=None, dropout=None,

AbhishekAshokDubey

# -*- coding: utf-8 -*-
"""
Created on Fri May 05 04:12:36 2017

@author: ADubey4
"""

from __future__ import unicode_literals, print_function
import gensim
from gensim.parsing import PorterStemmer

kylemcdonald

mixxorz

from django import forms
from django.core.exceptions import ValidationError
from django.db import transaction


class InvalidInputsError(Exception):

    def __init__(self, errors, non_field_errors):
        self.errors = errors
        self.non_field_errors = non_field_errors