Tensorflow 'numpy.ndarray' object has no attribute 'train'

I also encounter the same problem Training TensorFlow for Predicting a Column in a csv file which is:

AttributeError Traceback (most recent call last) in () 1 for i in range(1000): ----> 2 batch_xs, batch_ys = data.train.next_batch(100) 3 sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

AttributeError: 'numpy.ndarray' object has no attribute 'train'

How do you able to solve it?

from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
import matplotlib

# Import MNIST data
#from tensorflow.examples.tutorials.mnist import input_data
#mnistt = input_data.read_data_sets("/tttmp/data/", one_hot=True)

from numpy import genfromtxt

import csv
import tensorflow as tf
%matplotlib inline

# Read data...
x_input = genfromtxt('Data_Coffee.csv',delimiter=',')
y_input = genfromtxt('Class_Coffee.csv',delimiter=',')

data=genfromtxt('Data_Coffee.csv',delimiter=',')

matSize = np.shape(data)

# Parameters
learning_rate = 0.001
training_epochs = 15
batch_size = 100
display_step = 1


# tf Graph input
x = tf.placeholder(tf.float32, [None, matSize[0]])
y = tf.placeholder(tf.float32, [None, matSize[1]])

#x= genfromtxt('Data_Coffee.csv',delimiter=',')
#y= genfromtxt('Class_Coffee.csv',delimiter=',')


# Initializing the variables
init = tf.global_variables_initializer()

# Launch the graph
with tf.Session() as sess:
    sess.run(init)

    # Training cycle
    for epoch in range(training_epochs):
        avg_cost = 0.
        total_batch = int(x.train.num_examples/batch_size)

        # Loop over all batches
        for i in range(total_batch):
            batch_x, batch_y = data.train.next_batch(batch_size)
            # Run optimization op (backprop) and cost op (to get loss value)
            _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})
            # Compute average loss
            avg_cost += c / total_batch
        # Display logs per epoch step
        if epoch % display_step == 0:
            print("Epoch:", '%04d' % (epoch+1), "cost=", \
                "{:.9f}".format(avg_cost))
    print("Optimization Finished!")