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


i encounter same problem training tensorflow predicting column in csv file is:

attributeerror traceback (most recent call last) in () 1 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 able solve it?

from __future__ import print_function import matplotlib.pyplot plt import numpy 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)  numpy import genfromtxt  import csv import tensorflow 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 variables init = tf.global_variables_initializer()  # launch graph tf.session() sess:     sess.run(init)      # training cycle     epoch in range(training_epochs):         avg_cost = 0.         total_batch = int(x.train.num_examples/batch_size)          # loop on batches         in range(total_batch):             batch_x, batch_y = data.train.next_batch(batch_size)             # run optimization op (backprop) , cost op (to 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!")

i think copying pattern mnist example: data.train.next_batch

in mnist example data read object of class has train variable, whereas reading data numpy array.


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