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import tensorflow as tf
mu = 0
sigma = 0.1
rate = 0.001

x = tf.placeholder(tf.float32, (None, 32, 32, 1), name = "input")
y = tf.placeholder(tf.int32, (None),name = "label")
one_hot_y = tf.one_hot(y, 10)

with tf.name_scope("conv1"):   # Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.

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import tensorflow as tf

x = tf.placeholder(tf.float32, shape=[None, 784], name = "input")
y_ = tf.placeholder(tf.float32, shape=[None, 10], name = "label")   # labels

# Weight Initialization
def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial, name = "weight")

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import argparse
import sys

from tensorflow.examples.tutorials.mnist import input_data
from time import time
t0 = time()

import tensorflow as tf

tf.summary.FileWriterCache.clear()

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package etl;
import java.sql.ResultSet;
import java.sql.ResultSetMetaData;
import java.sql.SQLException;
import java.sql.Connection;
import java.sql.Statement;
import java.sql.DriverManager;
    
public class etl {
       private static String driverName = "org.apache.hive.jdbc.HiveDriver";

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import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
% matplotlib inline

image = mpimg.imread('bbox-example-image.jpg')
def draw_boxes(img, bboxes, color=(0, 0, 255), thick=6):
    imcopy = np.copy(img)
    for bbox in bboxes:

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import cv2
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

def plot3d(pixels, colors_rgb,
        axis_labels=list("RGB"), axis_limits=[(0, 255), (0, 255), (0, 255)]):
    """Plot pixels in 3D."""

    # Create figure and 3D axes

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import numpy as np
class Node(object):
    """
    Base class for nodes in the network.

    Arguments:

        `inbound_nodes`: A list of nodes with edges into this node.
    """
    def __init__(self, inbound_nodes=[]):

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batch_size = 128
hidden_size = 1024

graph = tf.Graph()
with graph.as_default():
  # place holder for train set, constant for other set
  X_train = tf.placeholder(tf.float32,shape=(None, 784))
  y_train = tf.placeholder(tf.float32, shape=(None, 10))
  
  # Variables.