title: trying to adapt the tensorflow cnn tutorial to SVHN data Placeholder issue
body: <p>i am new to cnn and to tensorflow, so i tried to adapt their example of cnn that they run on the mnist dataset onto the SVHN dataset. the images are 32x32 instead of 28x28 and they have 3 color channels instead of one. I get an error message :</p>
  
  <pre><code> tensorflow.python.framework.errors.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float
  </code></pre>
  
  <p>I cannot yet make heads or tails of the whole thing. Maybe someone spots a few glaring mistakes that i surely made. I encourage you to be brutal :)
  Here is my code in its entirety :</p>
  
  <pre><code>import urllib
  import os.path
  import numpy as np
  testfile = urllib.URLopener()
  testfile2=urllib.URLopener()
  import scipy.io as scp
  
  if not  os.path.isfile("test.mat"):
      testfile.retrieve("http://ufldl.stanford.edu/housenumbers/test_32x32.mat", "test.mat")
  
  if not  os.path.isfile("train.mat"):
      testfile.retrieve("http://ufldl.stanford.edu/housenumbers/train_32x32.mat", "train.mat")
  testdata=scp.loadmat('test.mat')
  traindata=scp.loadmat('train.mat')
  trainDataX = traindata['X']
  trainDataY = traindata['y']
  testDataX = testdata['X']
  testDataY = testdata['y']
  def OnehotEndoding(Y):
      Ytr=[]
      for el in Y:
          temp=np.zeros(10)
          if el==10:
              temp[0]=1
          elif el==1:
              temp[1]=1
          elif el==2:
              temp[2]=1
          elif el==3:
              temp[3]=1
          elif el==4:
              temp[4]=1
          elif temp[5]==1:
              temp[5]=1
          elif temp[6]==1:
              temp[6]=1
          elif temp[7]==1:
              temp[7]=1
          elif temp[8]==1:
              temp[8]=1
          elif temp[9]==1:
              temp[9]=1
          Ytr.append(temp)
  
      return np.asarray(Ytr)
  trainDataY = OnehotEndoding(trainDataY)
  testDataY = OnehotEndoding(testDataY)
  def transposeArray(data):
      print 'started'
      xtrain = []
      trainLen = data.shape[3]
      print trainLen
      for x in xrange(trainLen):
          xtrain.append(data[:,:,:,x])
  
      xtrain = np.asarray(xtrain)
      return xtrain
  trainDataX = transposeArray(trainDataX)
  testDataX = transposeArray(testDataX)
  print trainDataX.shape
  import tensorflow as tf
  sess = tf.InteractiveSession()
  
  
  x = tf.placeholder(tf.float32, shape=[None, 32,32,3])
  
  y_ = tf.placeholder(tf.float32, shape=[None, 10])
  
  def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)
  
  def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)
  
  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')
  
  W_conv1 = weight_variable([5, 5, 3, 32])
  b_conv1 = bias_variable([32])
  x_image = tf.reshape(x, [-1,32,32,3])
  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])
  b_conv2 = bias_variable([64])
  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])
  b_fc1 = bias_variable([1024])
  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(tf.float32)
  h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
  W_fc2 = weight_variable([1024, 10])
  b_fc2 = bias_variable([10])
  y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
  
  x = tf.placeholder(tf.float32, shape=[None, 32,32,3])
  y_ = tf.placeholder(tf.float32, shape=[None, 10])
  
  cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1]))
  train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
  correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  with tf.Session() as sess:
      epoch=10000
      batch_size=100
      sess.run(tf.initialize_all_variables())
      p = np.random.permutation(range(len(trainDataX)))
      trX, trY = trainDataX[p], trainDataY[p]
      print len(trainDataX)
      start = 0
      end = 0  
      for step in range(epoch):
          start = end
          end = start + batch_size
  
          if start &gt;= len(trainDataX):
              start = 0
              end = start + batch_size
  
          if end &gt;= len(trainDataX):
              end = len(trainDataX) - 1
          inX, outY = trX[start:end], trY[start:end]
          #sess.run(optimizer, feed_dict= {tf_X: inX, tf_Y: outY, keep_prob:0.75})
          if step % 100 == 0:
              train_accuracy = accuracy.eval(feed_dict={x: inX, y_: outY, keep_prob:1})
              print("step %d, training accuracy %g"%(step, train_accuracy))
          train_step.run(feed_dict={x: inX, y_: outY, keep_prob: 0.5})
  
      print("test accuracy %g"%accuracy.eval(feed_dict={
      x: testDataX, y_:testDataY , keep_prob: 1.0}))
  </code></pre>