(images, num_classes=10, is_training=False,
          dropout_keep_prob=0.5,
          prediction_fn=slim.softmax,
          scope='LeNet')
return logits, end_points
Creates a variant of the LeNet model.

  Note that since the output is a set of 'logits', the values fall in the
  interval of (-infinity, infinity). Consequently, to convert the outputs to a
  probability distribution over the characters, one will need to convert them
  using the softmax function:

        logits = lenet.lenet(images, is_training=False)
        probabilities = tf.nn.softmax(logits)
        predictions = tf.argmax(logits, 1)

  Args:
    images: A batch of `Tensors` of size [batch_size, height, width, channels].
    num_classes: the number of classes in the dataset.
    is_training: specifies whether or not we're currently training the model.
      This variable will determine the behaviour of the dropout layer.
    dropout_keep_prob: the percentage of activation values that are retained.
    prediction_fn: a function to get predictions out of logits.
    scope: Optional variable_scope.

  Returns:
    logits: the pre-softmax activations, a tensor of size
      [batch_size, `num_classes`]
    end_points: a dictionary from components of the network to the corresponding
      activation.
Creates a variant of the LeNet model.
Creates a variant of the LeNet model .
def lenet(images, num_classes=10, is_training=False,
          dropout_keep_prob=0.5,
          prediction_fn=slim.softmax,
          scope='LeNet'):
  """Creates a variant of the LeNet model.

  Note that since the output is a set of 'logits', the values fall in the
  interval of (-infinity, infinity). Consequently, to convert the outputs to a
  probability distribution over the characters, one will need to convert them
  using the softmax function:

        logits = lenet.lenet(images, is_training=False)
        probabilities = tf.nn.softmax(logits)
        predictions = tf.argmax(logits, 1)

  Args:
    images: A batch of `Tensors` of size [batch_size, height, width, channels].
    num_classes: the number of classes in the dataset.
    is_training: specifies whether or not we're currently training the model.
      This variable will determine the behaviour of the dropout layer.
    dropout_keep_prob: the percentage of activation values that are retained.
    prediction_fn: a function to get predictions out of logits.
    scope: Optional variable_scope.

  Returns:
    logits: the pre-softmax activations, a tensor of size
      [batch_size, `num_classes`]
    end_points: a dictionary from components of the network to the corresponding
      activation.
  """
  end_points = {}

  with tf.variable_scope(scope, 'LeNet', [images, num_classes]):
    net = slim.conv2d(images, 32, [5, 5], scope='conv1')
    net = slim.max_pool2d(net, [2, 2], 2, scope='pool1')
    net = slim.conv2d(net, 64, [5, 5], scope='conv2')
    net = slim.max_pool2d(net, [2, 2], 2, scope='pool2')
    net = slim.flatten(net)
    end_points['Flatten'] = net

    net = slim.fully_connected(net, 1024, scope='fc3')
    net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                       scope='dropout3')
    logits = slim.fully_connected(net, num_classes, activation_fn=None,
                                  scope='fc4')

  end_points['Logits'] = logits
  end_points['Predictions'] = prediction_fn(logits, scope='Predictions')

  return logits, end_points
def lenet ( images , num_classes = 10 , is_training = False , dropout_keep_prob = 0.5 , prediction_fn = slim . softmax , scope = 'LeNet' ) : end_points = { } with tf . variable_scope ( scope , 'LeNet' , [ images , num_classes ] ) : net = slim . conv2d ( images , 32 , [ 5 , 5 ] , scope = 'conv1' ) net = slim . max_pool2d ( net , [ 2 , 2 ] , 2 , scope = 'pool1' ) net = slim . conv2d ( net , 64 , [ 5 , 5 ] , scope = 'conv2' ) net = slim . max_pool2d ( net , [ 2 , 2 ] , 2 , scope = 'pool2' ) net = slim . flatten ( net ) end_points [ 'Flatten' ] = net net = slim . fully_connected ( net , 1024 , scope = 'fc3' ) net = slim . dropout ( net , dropout_keep_prob , is_training = is_training , scope = 'dropout3' ) logits = slim . fully_connected ( net , num_classes , activation_fn = None , scope = 'fc4' ) end_points [ 'Logits' ] = logits end_points [ 'Predictions' ] = prediction_fn ( logits , scope = 'Predictions' ) return logits , end_points