title: Tensorflow embedding_lookup
body: <p>I am trying to learn the word representation of the imdb dataset "from scratch" through the TensorFlow <code>tf.nn.embedding_lookup()</code> function. If I understand it correctly, I have to set up an embedding layer before the other hidden layer, and then when I perform gradient descent, the layer will "learn" a word representation in the weights of this layer. However, when I try to do this, I get a shape error between my embedding layer and the first fully-connected layer of my network.</p>
  
  <pre><code>def multilayer_perceptron(_X, _weights, _biases):
      with tf.device('/cpu:0'), tf.name_scope("embedding"):
          W = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0),name="W")
          embedding_layer = tf.nn.embedding_lookup(W, _X)    
      layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(embedding_layer, _weights['h1']), _biases['b1'])) 
      layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, _weights['h2']), _biases['b2'])) 
      return tf.matmul(layer_2, weights['out']) + biases['out']
  
  x = tf.placeholder(tf.int32, [None, n_input])
  y = tf.placeholder(tf.float32, [None, n_classes])
  
  pred = multilayer_perceptron(x, weights, biases)
  cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred,y))
  train_step = tf.train.GradientDescentOptimizer(0.3).minimize(cost)
  
  init = tf.initialize_all_variables()
  </code></pre>
  
  <p>The error I get is:</p>
  
  <pre><code>ValueError: Shapes TensorShape([Dimension(None), Dimension(300), Dimension(128)])
  and TensorShape([Dimension(None), Dimension(None)]) must have the same rank
  </code></pre>