title: Dimensions must be equal, but are 15 and 1 for 'MatMul_1' (op: 'MatMul') with input shapes: [1,15], [1,500]
body: <p>I cannot understand the dimension part. Is it about the shape [1,15]I set?</p>
  
  <pre><code>import tensorflow as tf
  import numpy as np
  import pandas as pd
  
  
  
  with open('train.CSV', 'r') as f:  
      data0 = f.readlines()  
  
      for line in data0:  
          odom = line.split()         
          numbers_float0 = map(float, odom) 
  
  with open('trainY.CSV', 'r') as f:  
      data1 = f.readlines()  
  
  for line in data1:  
      odom = line.split()        
      numbers_float1 = map(float, odom)  
  
  with open('test.CSV', 'r') as f:  
      data2 = f.readlines()  
  
      for line in data2:  
          odom = line.split()        
          numbers_float2 = map(float, odom) 
  
  with open('Test Y.CSV', 'r') as f:  
      data3 = f.readlines()    
  
      for line in data3:  
          odom = line.split()      
          numbers_float3 = map(float, odom)  
  
  
  
  train_x,train_y,test_x,test_y =             ('numbers_float0','numbers_float1','numbers_float2','numbers_float3')
  n_nodes_hl1 = 500
  n_nodes_hl2 = 500
  n_nodes_hl3 = 500
  
  n_classes = 2
  batch_size = 100
  hm_epochs = 10
  
  x =tf.placeholder('float',[1,15])  
  y = tf.placeholder('float',[1,1])
  
  hidden_1_layer = {'f_fum':n_nodes_hl1,
                    'weight':tf.Variable(tf.random_normal([len(train_x[0]),         n_nodes_hl1])),
                    'bias':tf.Variable(tf.random_normal([n_nodes_hl1]))}
  
  hidden_2_layer = {'f_fum':n_nodes_hl2,
                    'weight':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
                    'bias':tf.Variable(tf.random_normal([n_nodes_hl2]))}
  
  hidden_3_layer = {'f_fum':n_nodes_hl3,
                    'weight':tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),
                'bias':tf.Variable(tf.random_normal([n_nodes_hl3]))}
  
  output_layer = {'f_fum':None,
              'weight':tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])),
                  'bias':tf.Variable(tf.random_normal([n_classes])),}
  
  
  # Nothing changes
  def neural_network_model(data):
  
      l1 = tf.add(tf.matmul(data,hidden_1_layer['weight']),     hidden_1_layer['bias'])
      l1 = tf.nn.relu(l1)
  
      l2 = tf.add(tf.matmul(l1,hidden_2_layer['weight']),     hidden_2_layer['bias'])
      l2 = tf.nn.relu(l2)
  
      l3 = tf.add(tf.matmul(l2,hidden_3_layer['weight']), hidden_3_layer['bias'])
      l3 = tf.nn.relu(l3)
  
      output = tf.matmul(l3,output_layer['weight']) + output_layer['bias']
  
      return output
  
  def train_neural_network(x):
      prediction = neural_network_model(x)
      cost =     tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y))
      #tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(prediction,y) )
  
  
  
  
      optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
  
      with tf.Session() as sess:
          sess.run(tf.initialize_all_variables())
  
          for epoch in range(hm_epochs):
              epoch_loss = 0
              i=0
              while i &lt; len(train_x):
                  start = i
                  end = i+batch_size
                  batch_x = np.array(train_x[start:end])
                  batch_y = np.array(train_y[start:end])
  
                  _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,
                                                            y: batch_y})
                  epoch_loss += c
                  i+=batch_size
  
              print('Epoch', epoch+1, 'completed out of',hm_epochs,'loss:',epoch_loss)
          correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
          accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
  
          print('Accuracy:',accuracy.eval({x:test_x, y:test_y}))
  
  
  train_neural_network(x)
  </code></pre>
  
  <p>Here is the trace error:<a href="https://i.stack.imgur.com/8jKY9.png" rel="nofollow noreferrer">enter image description here</a></p>
  
  <p>Here is the Data train.CSV
  <a href="https://i.stack.imgur.com/8Tf6C.png" rel="nofollow noreferrer">enter image description here</a></p>
  
  <p>The Y data I use is only one column .</p>