courtneyfaulkner

#include <stdio.h>                                                                                                                                               
#include <stdlib.h>
#ifdef __APPLE__
#include <OpenCL/opencl.h>
#else
#include <CL/cl.h>
#endif

int main() {

Multihuntr

import numpy as np
import tensorflow as tf
import sys

from tensorflow.examples.tutorials.mnist import input_data

n_pseudo_batches =  int(sys.argv[1]) if len(sys.argv) > 1 else 128
actual_batch_size = int(sys.argv[2]) if len(sys.argv) > 2 else 32
iterations =        int(sys.argv[3]) if len(sys.argv) > 3 else 10

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

melgor

erogol

zhanwenchen

import os
import tensorflow as tf

trained_checkpoint_prefix = 'checkpoints/dev'
export_dir = os.path.join('models', '0') # IMPORTANT: each model folder must be named '0', '1', ... Otherwise it will fail!

loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph) as sess:
    # Restore from checkpoint
    loader = tf.train.import_meta_graph(trained_checkpoint_prefix + '.meta')

feroult

import tensorflow as tf

from open_seq2seq.utils.utils import get_base_config, check_logdir, create_model

# Change with your configs here
args_S2T = ["--config_file=/data/training/v5/config-J5x3.py",
        "--mode=interactive_infer",
        "--logdir=/data/training/v5/models",
        "--batch_size_per_gpu=10",
]

mauri870

import tensorflow as tf
# FIXME: audio_ops.decode_wav is deprecated, use tensorflow_io.IOTensor.from_audio
from tensorflow.contrib.framework.python.ops import audio_ops

# Enable eager execution for a more interactive frontend.
# If using the default graph mode, you'll probably need to run in a session.
tf.enable_eager_execution()

@tf.function
def audio_to_mfccs(

jlgabriel

import requests
import math
import pandas as pd
import flatten_json  
# instalar flatten_json con: pip install flatten_json 
# Referencia: https://github.com/amirziai/flatten

# parámetros

url_api_productos_contar = "https://api.jumpseller.com/v1/products/count.json"

eldrin

import math
from typing import Callable, Optional
from warnings import warn

import torch
from torch import Tensor
from torchaudio import functional as F
from torchaudio.compliance import kaldi