title: Get Predicted result from tensorflow
body: <p>I am doing my first tensor flow example with following code. </p>
  
  <pre><code>train_x,train_y,test_x,test_y=create_feature_sets_and_labels('pro.txt','neg.txt')
  n_nodes_hl1 = 1500
  n_nodes_hl2 = 1500
  n_nodes_hl3 = 1500
  
  n_classes = 2
  batch_size = 100
  hm_epochs = 7
  
  x = tf.placeholder('float')
  y = tf.placeholder('float')
  
  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])),}
  
  
  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_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_l$                correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
              accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
              print(y)
              print('Accuracy:',accuracy.eval({x:test_x, y:test_y}))
  
  
  train_neural_network(x)
  </code></pre>
  
  <p>It give me accuracy on test data. 
  What i want is to give an input sentence to my train model and it returns me predicted label.</p>
  
  <p>i tried following form this <a href="https://stackoverflow.com/questions/34060332/how-to-get-predicted-class-labels-in-tensorflows-mnist-example">example</a></p>
  
  <pre><code>#with same length as lexicon               
  input = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.$ 
  output = sess.run(y, feed_dict={x :input})
  </code></pre>
  
  <p>it gives me following error.</p>
  
  <pre><code>You must feed a value for placeholder tensor 'Placeholder_1' with dtype float
       [[Node: Placeholder_1 = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
  </code></pre>