title: Efficiently grab gradients from TensorFlow?
body: <p>I'm trying to implement an asynchronous parameter server, DistBelief style using TensorFlow. I found that minimize() is split into two functions, compute_gradients and apply_gradients, so my plan is to insert a network boundary between them. I have a question about how to evaluate all the gradients simultaneously and pull them out all at once. I understand that eval only evaluates the subgraph necessary, but it also only returns one tensor, not the chain of tensors required to compute that tensor.</p>
  
  <p>How can I do this more efficiently? I took the Deep MNIST example as a starting point:</p>
  
  <pre class="lang-py prettyprint-override"><code>import tensorflow as tf
  import download_mnist
  
  def weight_variable(shape, name):
     initial = tf.truncated_normal(shape, stddev=0.1)
     return tf.Variable(initial, name=name)
  
  def bias_variable(shape, name):
     initial = tf.constant(0.1, shape=shape)
     return tf.Variable(initial, name=name)
  
  def conv2d(x, W):
     return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
  
  def max_pool_2x2(x):
     return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                           strides=[1, 2, 2, 1], padding='SAME')
  
  mnist = download_mnist.read_data_sets('MNIST_data', one_hot=True)
  session = tf.InteractiveSession()
  x = tf.placeholder("float", shape=[None, 784], name='x')
  x_image = tf.reshape(x, [-1,28,28,1], name='reshape')
  y_ = tf.placeholder("float", shape=[None, 10], name='y_')
  W_conv1 = weight_variable([5, 5, 1, 32], 'W_conv1')
  b_conv1 = bias_variable([32], 'b_conv1')
  h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
  h_pool1 = max_pool_2x2(h_conv1)
  W_conv2 = weight_variable([5, 5, 32, 64], 'W_conv2')
  b_conv2 = bias_variable([64], 'b_conv2')
  h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
  h_pool2 = max_pool_2x2(h_conv2)
  W_fc1 = weight_variable([7 * 7 * 64, 1024], 'W_fc1')
  b_fc1 = bias_variable([1024], 'b_fc1')
  h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
  h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
  keep_prob = tf.placeholder("float", name='keep_prob')
  h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
  W_fc2 = weight_variable([1024, 10], 'W_fc2')
  b_fc2 = bias_variable([10], 'b_fc2')
  y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
  
  loss = -tf.reduce_sum(y_ * tf.log(y_conv))
  optimizer = tf.train.AdamOptimizer(1e-4)
  correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
  accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
  compute_gradients = optimizer.compute_gradients(loss)
  session.run(tf.initialize_all_variables())
  
  batch = mnist.train.next_batch(50)
  feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}
  
  
  gradients = []
  for grad_var in compute_gradients:
      grad = grad_var[0].eval(feed_dict=feed_dict)
      var = grad_var[1]
      gradients.append((grad, var))
  </code></pre>
  
  <p>I think this last for loop is actually recalculating the last gradient several times, whereas the first gradient is computed only once? How can I grab all the gradients without recomputing them?</p>