original_docstring: deepnn builds the graph for a deep net for classifying digits.
  
      Args:
          x: an input tensor with the dimensions (N_examples, 784), where 784 is
              the number of pixels in a standard MNIST image.
  
      Returns:
          A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with
              values equal to the logits of classifying the digit into one of 10
              classes (the digits 0-9). keep_prob is a scalar placeholder for the
              probability of dropout.
      
docstring: deepnn builds the graph for a deep net for classifying digits.
docstring_tokens: deepnn builds the graph for a deep net for classifying digits .
code: def deepnn(x):
      """deepnn builds the graph for a deep net for classifying digits.
  
      Args:
          x: an input tensor with the dimensions (N_examples, 784), where 784 is
              the number of pixels in a standard MNIST image.
  
      Returns:
          A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with
              values equal to the logits of classifying the digit into one of 10
              classes (the digits 0-9). keep_prob is a scalar placeholder for the
              probability of dropout.
      """
      # Reshape to use within a convolutional neural net.
      # Last dimension is for "features" - there is only one here, since images
      # are grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
      with tf.name_scope('reshape'):
          x_image = tf.reshape(x, [-1, 28, 28, 1])
  
      # First convolutional layer - maps one grayscale image to 32 feature maps.
      with tf.name_scope('conv1'):
          W_conv1 = weight_variable([5, 5, 1, 32])
          b_conv1 = bias_variable([32])
          h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
  
      # Pooling layer - downsamples by 2X.
      with tf.name_scope('pool1'):
          h_pool1 = max_pool_2x2(h_conv1)
  
      # Second convolutional layer -- maps 32 feature maps to 64.
      with tf.name_scope('conv2'):
          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)
  
      # Second pooling layer.
      with tf.name_scope('pool2'):
          h_pool2 = max_pool_2x2(h_conv2)
  
      # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
      # is down to 7x7x64 feature maps -- maps this to 1024 features.
      with tf.name_scope('fc1'):
          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)
  
      # Dropout - controls the complexity of the model, prevents co-adaptation of
      # features.
      with tf.name_scope('dropout'):
          keep_prob = tf.placeholder(tf.float32)
          h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
  
      # Map the 1024 features to 10 classes, one for each digit
      with tf.name_scope('fc2'):
          W_fc2 = weight_variable([1024, 10])
          b_fc2 = bias_variable([10])
  
          y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
      return y_conv, keep_prob
code_tokens: def deepnn ( x ) : with tf . name_scope ( 'reshape' ) : x_image = tf . reshape ( x , [ - 1 , 28 , 28 , 1 ] ) with tf . name_scope ( 'conv1' ) : W_conv1 = weight_variable ( [ 5 , 5 , 1 , 32 ] ) b_conv1 = bias_variable ( [ 32 ] ) h_conv1 = tf . nn . relu ( conv2d ( x_image , W_conv1 ) + b_conv1 ) with tf . name_scope ( 'pool1' ) : h_pool1 = max_pool_2x2 ( h_conv1 ) with tf . name_scope ( 'conv2' ) : 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 ) with tf . name_scope ( 'pool2' ) : h_pool2 = max_pool_2x2 ( h_conv2 ) with tf . name_scope ( 'fc1' ) : 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 ) with tf . name_scope ( 'dropout' ) : keep_prob = tf . placeholder ( tf . float32 ) h_fc1_drop = tf . nn . dropout ( h_fc1 , keep_prob ) with tf . name_scope ( 'fc2' ) : W_fc2 = weight_variable ( [ 1024 , 10 ] ) b_fc2 = bias_variable ( [ 10 ] ) y_conv = tf . matmul ( h_fc1_drop , W_fc2 ) + b_fc2 return y_conv , keep_prob
short_docstring: deepnn builds the graph for a deep net for classifying digits.
short_docstring_tokens: deepnn builds the graph for a deep net for classifying digits .
comment: """deepnn builds the graph for a deep net for classifying digits.
  
      Args:
          x: an input tensor with the dimensions (N_examples, 784), where 784 is
              the number of pixels in a standard MNIST image.
  
      Returns:
          A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with
              values equal to the logits of classifying the digit into one of 10
              classes (the digits 0-9). keep_prob is a scalar placeholder for the
              probability of dropout.
      """$SEP$# Reshape to use within a convolutional neural net.$SEP$# Last dimension is for "features" - there is only one here, since images$SEP$# are grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.$SEP$# First convolutional layer - maps one grayscale image to 32 feature maps.$SEP$# Pooling layer - downsamples by 2X.$SEP$# Second convolutional layer -- maps 32 feature maps to 64.$SEP$# Second pooling layer.$SEP$# Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image$SEP$# is down to 7x7x64 feature maps -- maps this to 1024 features.$SEP$# Dropout - controls the complexity of the model, prevents co-adaptation of$SEP$# features.$SEP$# Map the 1024 features to 10 classes, one for each digit