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import numpy as np
import torch


def layernorm_forward(x, gamma, beta, eps):
    out, cache = None, None
    M, _ = x.shape
    x_mean = np.mean(x, axis=1).reshape(M, 1)
    x_var = np.var(x, axis=1).reshape(M, 1)

rocking5566

import numpy as np


def batchnorm_forward(x, gamma, beta, eps):
    N, D = x.shape

    # step1: calculate mean
    mu = 1./N * np.sum(x, axis=0)

    # step2: subtract mean vector of every trainings example

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import numpy as np
import tensorflow as tf
from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation, Conv2D, Flatten
from tensorflow.keras.optimizers import RMSprop

# download the mnist to the path '~/.keras/datasets/' if it is the first time to be called
# X shape (60,000 28x28), y shape (10,000, )

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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import sys

from PIL import Image
import numpy as np
import tensorflow as tf

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#include <iostream>
#include <vector>

using namespace std;
typedef vector< vector<float>> matrix;

void PrintMatrix(const matrix& mat)
{
    for (int i = 0, row = mat.size(); i < row; ++i)
    {

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#include <math.h>

using namespace std;
using namespace cv;

cv::Mat GetMask(const Mat& img)
{
    Mat ret;
    Mat imgGray;
    cvtColor(img, imgGray, CV_BGR2GRAY);

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import sys

sys.path.append("/opt/tf_model/research")
sys.path.append("/opt/tf_model/research/object_detection")

import os
import cv2
import numpy as np
import tensorflow as tf
from utils import label_map_util