python - Iterate over Numpy array for Tensorflow -

hello,

i entry level in python.i have searched every doc on python , numpy didn't find.i want train multivariable logistic regression model.i have 100x2 numpy array train_x data , 100x1 numpy array train_y data.i couldn't feed placeholders.i think can't able iterate multidimensional matrix placeholder wants.

here raw code better understand:

import matplotlib.pyplot plt import tensorflow tf import numpy numpy  learning_rate = 0.01 total_iterator = 1500 display_per = 100  data = numpy.loadtxt("ex2data1.txt",dtype=numpy.float32,delimiter=",");  training_x = numpy.asarray(data[:,[0,1]]) # 100 x 2 training_y = numpy.asarray(data[:,[2]],dtype=numpy.int) # 100 x 1  m = data.shape[0] # thats sample size = 100  x_i = tf.placeholder(tf.float32,[none,2]) # n x 2 y_i = tf.placeholder(tf.float32,[none,1]) # n x 1  w = tf.variable(tf.zeros([2,1]))  # 2 x 1           b = tf.variable(tf.zeros([1,1]))  # 1 x 1        h = tf.matmul(w,x_i)+b  cost = tf.reduce_sum(tf.add(tf.multiply(y_i,tf.log(h)),tf.multiply(1-y_i,tf.log(1-h)))) / -m ### wanted try simple cross function learned in lesson ### ### didn't such error @ scope ###  initializer = tf.train.gradientdescentoptimizer(learning_rate).minimize(cost)  init = tf.global_variables_initializer()  tf.session() sess:     sess.run(init)      k in range(total_iterator):             (x,y) in  zip(training_x,training_y):             sess.run(initializer,feed_dict={x_i: x , y_i: y}) ### ?!??!? ###              ### @ scope: error such 'can't feed,             ### placeholder:0'###          if k % display_per==0:             print("iteration: ",k, "cost: ", sess.run(cost,feed_dict={x_i:training_x,y_i:training_y}),"w: ",sess.run(w),\                 "b: ",sess.run(b))      print("optim. finished")     print("iteration: ", k, "cost: ", sess.run(cost, feed_dict={x_i: training_x, y_i: training_y}), "w: ", sess.run(w), \           "b: ", sess.run(b)) 

thanks answers.i think have pass 2 dimensional matrix slice train_x x_i.maybe wrong start end.

the problem x,y in loop 1-d, while placeholder 2-d. (note define placeholder tf.placeholder(tf.float32,[none,2]), defines 2-d placeholder. done in order optimization , calculations in batches).

the quickest solution reshape x , y:

sess.run(initializer,feed_dict={x_i: np.reshape(x, [1,-1]),                                 y_i: np.reshape(y, [1, -1])})