title: Tensorflow error: Invalid argument: shape must be a vector
body: <p>I was trying out tensorflow with the Titanic data from Kaggle:<a href="https://www.kaggle.com/c/titanic" rel="nofollow">https://www.kaggle.com/c/titanic</a></p>
  
  <p>Here's the code I tried to implement from Sendex:<a href="https://www.youtube.com/watch?v=PwAGxqrXSCs&amp;index=46&amp;list=PLQVvvaa0QuDfKTOs3Keq_kaG2P55YRn5v#t=398.046664" rel="nofollow">https://www.youtube.com/watch?v=PwAGxqrXSCs&amp;index=46&amp;list=PLQVvvaa0QuDfKTOs3Keq_kaG2P55YRn5v#t=398.046664</a></p>
  
  <pre><code>import tensorflow as tf
  import cleanData
  import numpy as np
  
  train, test = cleanData.read_and_clean()
  train = train[['Pclass', 'Sex', 'Age', 'Fare', 'Child', 'Fam_size', 'Title', 'Mother', 'Survived']]
  
  # one hot
  train['Died'] = int('0')
  train["Died"][train["Survived"] == 0] = 1
  
  print(train.head())
  
  n_nodes_hl1 = 500
  n_classes = 2
  batch_size = 100
  
  # tf graph input
  x = tf.placeholder("float", [None, 8])
  y = tf.placeholder("float")
  
  def neural_network_model(data):
  
      hidden_layer_1 = {'weights':tf.Variable(tf.random_normal([8, n_nodes_hl1])),
                        'biases':tf.Variable(tf.random_normal(n_nodes_hl1))}
  
      output_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl1, n_classes])),
                      'biases':tf.Variable(tf.random_normal([n_classes]))}
  
      l1 = tf.add(tf.matmul(data, hidden_layer_1['weights']), hidden_layer_1['biases'])
      l1 = tf.nn.relu(l1)
  
      output = tf.matmul(l1, output_layer['weights']) + output_layer['biases']
  
      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().minimize(cost)
  
      desired_epochs = 10
  
      with tf.Session() as sess:
          sess.run(tf.initialize_all_variables())
  
          for epoch in range(desired_epochs):
              epoch_loss = 0
              for _ in range(int(train.shape[0])/batch_size):
                  x_epoch, y_epoch = train.next_batch(batch_size)
                  _, c = sess.run([optimizer, cost], feed_dict= {x:x, y:y})
                  epoch_loss += c
              print('Epoch', epoch, 'completed out of', desired_epochs, 'loss:', epoch_loss)
  
          correct = tf.equal(tf.argmax(prediction,1), tf.argmax(y,1))
          accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
          print('Training accuracy:', accuracy.eval({x:x, y:y}))
  
  
  
  train_neural_network(x)
  </code></pre>
  
  <p>When I ran the code I got an error that said: "W tensorflow/core/framework/op_kernel.cc:909] Invalid argument: shape must be a vector of {int32,int64}, got shape []"</p>
  
  <p>Is there a way around this? I saw a post on Github for tensorflow's code and apparently the library doesn't take pandas dataframe as an input..</p>