title: How to convert tensor to numpy array
body: <p>I'm beginner of tensorflow. I made simple autoencoder with the help. I want to convert final <code>decoded</code> tensor to numpy array.I tried using <code>.eval()</code> but I could not work it. how can I convert tensor to numpy?</p>
  
  <p>My input image size is 512*512*1 and data type is raw image format.</p>
  
  <h2>code</h2>
  
  <pre><code>#input
  image_size = 512
  hidden = 256
  input_image = np.fromfile('PATH',np.float32)
  
  # Variables
  x_placeholder = tf.placeholder("float", (image_size*image_size))
  
  x = tf.reshape(x_placeholder, [image_size * image_size, 1])
  w_enc = tf.Variable(tf.random_normal([hidden, image_size * image_size], mean=0.0, stddev=0.05))
  w_dec = tf.Variable(tf.random_normal([image_size * image_size, hidden], mean=0.0, stddev=0.05))
  b_enc = tf.Variable(tf.zeros([hidden, 1]))
  b_dec = tf.Variable(tf.zeros([image_size * image_size, 1]))
  
  #model
  encoded = tf.sigmoid(tf.matmul(w_enc, x) + b_enc)
  decoded = tf.sigmoid(tf.matmul(w_dec,encoded) + b_dec)
  
  # Cost Function
  cross_entropy = -1. * x * tf.log(decoded) - (1. - x) * tf.log(1. - decoded)
  loss = tf.reduce_mean(cross_entropy)
  train_step = tf.train.AdagradOptimizer(0.1).minimize(loss)
  
  # Train
  init = tf.global_variables_initializer()
  
  with tf.Session() as sess:
      sess.run(init)
      print('Training...')
      for _ in xrange(10):
          loss_val, _ = sess.run([loss, train_step], feed_dict = {x_placeholder: input_image})
          print loss_val
  </code></pre>