Python tensorflow.contrib.layers.python.layers.layers.fully_connected() Examples

def vgg_arg_scope(weight_decay=0.0005):
  """Defines the VGG arg scope.

  Args:
    weight_decay: The l2 regularization coefficient.

  Returns:
    An arg_scope.
  """
  with arg_scope(
      [layers.conv2d, layers_lib.fully_connected],
      activation_fn=nn_ops.relu,
      weights_regularizer=regularizers.l2_regularizer(weight_decay),
      biases_initializer=init_ops.zeros_initializer()):
    with arg_scope([layers.conv2d], padding='SAME') as arg_sc:
      return arg_sc 

def vgg_arg_scope(weight_decay=0.0005):
  """Defines the VGG arg scope.

  Args:
    weight_decay: The l2 regularization coefficient.

  Returns:
    An arg_scope.
  """
  with arg_scope(
      [layers.conv2d, layers_lib.fully_connected],
      activation_fn=nn_ops.relu,
      weights_regularizer=regularizers.l2_regularizer(weight_decay),
      biases_initializer=init_ops.zeros_initializer()):
    with arg_scope([layers.conv2d], padding='SAME') as arg_sc:
      return arg_sc 

def vgg_arg_scope(weight_decay=0.0005):
    """Defines the VGG arg scope.

    Args:
      weight_decay: The l2 regularization coefficient.

    Returns:
      An arg_scope.
    """
    with arg_scope(
        [layers.conv2d, layers_lib.fully_connected],
        activation_fn=nn_ops.relu,
        weights_regularizer=regularizers.l2_regularizer(weight_decay),
        biases_initializer=init_ops.zeros_initializer()
    ):
        with arg_scope([layers.conv2d], padding='SAME') as arg_sc:
            return arg_sc 

def vgg_arg_scope(weight_decay=0.0005):
    """Defines the VGG arg scope.

    Args:
      weight_decay: The l2 regularization coefficient.

    Returns:
      An arg_scope.
    """
    with arg_scope(
        [layers.conv2d, layers_lib.fully_connected],
        activation_fn=nn_ops.relu,
        weights_regularizer=regularizers.l2_regularizer(weight_decay),
        biases_initializer=init_ops.zeros_initializer()
    ):
        with arg_scope([layers.conv2d], padding='SAME') as arg_sc:
            return arg_sc 

def vgg_arg_scope(weight_decay=0.0005):
    """Defines the VGG arg scope.

    Args:
      weight_decay: The l2 regularization coefficient.

    Returns:
      An arg_scope.
    """
    with arg_scope(
        [layers.conv2d, layers_lib.fully_connected],
        activation_fn=nn_ops.relu,
        weights_regularizer=regularizers.l2_regularizer(weight_decay),
        biases_initializer=init_ops.zeros_initializer()
    ):
        with arg_scope([layers.conv2d], padding='SAME') as arg_sc:
            return arg_sc 

def predictron_arg_scope(weight_decay=0.0001,
                         batch_norm_decay=0.997,
                         batch_norm_epsilon=1e-5,
                         batch_norm_scale=True):
  batch_norm_params = {
    'decay': batch_norm_decay,
    'epsilon': batch_norm_epsilon,
    'scale': batch_norm_scale,
    'updates_collections': tf.GraphKeys.UPDATE_OPS,
  }

  # Set weight_decay for weights in Conv and FC layers.
  with arg_scope(
      [layers.conv2d, layers_lib.fully_connected],
      weights_regularizer=regularizers.l2_regularizer(weight_decay)):
    with arg_scope(
        [layers.conv2d],
        weights_initializer=initializers.variance_scaling_initializer(),
        activation_fn=None,
        normalizer_fn=layers_lib.batch_norm,
        normalizer_params=batch_norm_params) as sc:
      return sc 

def vgg_arg_scope(weight_decay=0.0005):
  """Defines the VGG arg scope.

  Args:
    weight_decay: The l2 regularization coefficient.

  Returns:
    An arg_scope.
  """
  with arg_scope(
      [layers.conv2d, layers_lib.fully_connected],
      activation_fn=nn_ops.relu,
      weights_regularizer=regularizers.l2_regularizer(weight_decay),
      biases_initializer=init_ops.zeros_initializer()):
    with arg_scope([layers.conv2d], padding='SAME') as arg_sc:
      return arg_sc 

def vgg_arg_scope(weight_decay=0.0005):
  """Defines the VGG arg scope.

  Args:
    weight_decay: The l2 regularization coefficient.

  Returns:
    An arg_scope.
  """
  with arg_scope(
      [layers.conv2d, layers_lib.fully_connected],
      activation_fn=nn_ops.relu,
      weights_regularizer=regularizers.l2_regularizer(weight_decay),
      biases_initializer=init_ops.zeros_initializer()):
    with arg_scope([layers.conv2d], padding='SAME') as arg_sc:
      return arg_sc 

def get_conditional_batch_norm_param(conditional_layer, output_dim, scope='gamma', activation_fn=None):
  """Outputs the batch norm parameter transformed from the `conditional_layer` using a fully connected layer."""
  if conditional_layer is None:
    raise ValueError('`conditional_layer` must not be None.')
  return layers.fully_connected(conditional_layer, output_dim, scope=scope, activation_fn=activation_fn) 

def BatchNormClassifier(self, inputs):
        inputs = layers.batch_norm(inputs, decay=0.1, fused=None)
        return layers.fully_connected(inputs, 1, activation_fn=math_ops.sigmoid) 

def alexnet_v2_arg_scope(weight_decay=0.0005):
  with arg_scope(
      [layers.conv2d, layers_lib.fully_connected],
      activation_fn=nn_ops.relu,
      biases_initializer=init_ops.constant_initializer(0.1),
      weights_regularizer=regularizers.l2_regularizer(weight_decay)):
    with arg_scope([layers.conv2d], padding='SAME'):
      with arg_scope([layers_lib.max_pool2d], padding='VALID') as arg_sc:
        return arg_sc 

def BatchNormClassifier(self, inputs):
        inputs = layers.batch_norm(inputs, decay=0.1, fused=None)
        return layers.fully_connected(inputs, 1, activation_fn=math_ops.sigmoid) 

def inception_v2_arg_scope(weight_decay=0.00004,
                           batch_norm_var_collection='moving_vars'):
  """Defines the default InceptionV2 arg scope.

  Args:
    weight_decay: The weight decay to use for regularizing the model.
    batch_norm_var_collection: The name of the collection for the batch norm
      variables.

  Returns:
    An `arg_scope` to use for the inception v3 model.
  """
  batch_norm_params = {
      # Decay for the moving averages.
      'decay': 0.9997,
      # epsilon to prevent 0s in variance.
      'epsilon': 0.001,
      # collection containing update_ops.
      'updates_collections': ops.GraphKeys.UPDATE_OPS,
      # collection containing the moving mean and moving variance.
      'variables_collections': {
          'beta': None,
          'gamma': None,
          'moving_mean': [batch_norm_var_collection],
          'moving_variance': [batch_norm_var_collection],
      }
  }

  # Set weight_decay for weights in Conv and FC layers.
  with arg_scope(
      [layers.conv2d, layers_lib.fully_connected],
      weights_regularizer=regularizers.l2_regularizer(weight_decay)):
    with arg_scope(
        [layers.conv2d],
        weights_initializer=initializers.variance_scaling_initializer(),
        activation_fn=nn_ops.relu,
        normalizer_fn=layers_lib.batch_norm,
        normalizer_params=batch_norm_params) as sc:
      return sc 

def BatchNormClassifier(inputs):
  inputs = layers.batch_norm(inputs, decay=0.1)
  return layers.fully_connected(inputs, 1, activation_fn=math_ops.sigmoid) 

def LogisticClassifier(inputs):
  return layers.fully_connected(inputs, 1, activation_fn=math_ops.sigmoid) 

def alexnet_v2_arg_scope(weight_decay=0.0005):
  with arg_scope(
      [layers.conv2d, layers_lib.fully_connected],
      activation_fn=nn_ops.relu,
      biases_initializer=init_ops.constant_initializer(0.1),
      weights_regularizer=regularizers.l2_regularizer(weight_decay)):
    with arg_scope([layers.conv2d], padding='SAME'):
      with arg_scope([layers_lib.max_pool2d], padding='VALID') as arg_sc:
        return arg_sc 

def overfeat_arg_scope(weight_decay=0.0005):
  with arg_scope(
      [layers.conv2d, layers_lib.fully_connected],
      activation_fn=nn_ops.relu,
      weights_regularizer=regularizers.l2_regularizer(weight_decay),
      biases_initializer=init_ops.zeros_initializer()):
    with arg_scope([layers.conv2d], padding='SAME'):
      with arg_scope([layers_lib.max_pool2d], padding='VALID') as arg_sc:
        return arg_sc 

def BatchNormClassifier(self, inputs):
        inputs = layers.batch_norm(inputs, decay=0.1, fused=None)
        return layers.fully_connected(inputs, 1, activation_fn=math_ops.sigmoid) 

def overfeat_arg_scope(weight_decay=0.0005):
  with arg_scope(
      [layers.conv2d, layers_lib.fully_connected],
      activation_fn=nn_ops.relu,
      weights_regularizer=regularizers.l2_regularizer(weight_decay),
      biases_initializer=init_ops.zeros_initializer()):
    with arg_scope([layers.conv2d], padding='SAME'):
      with arg_scope([layers_lib.max_pool2d], padding='VALID') as arg_sc:
        return arg_sc 

def alexnet_v2_arg_scope(weight_decay=0.0005):
  with arg_scope(
      [layers.conv2d, layers_lib.fully_connected],
      activation_fn=nn_ops.relu,
      biases_initializer=init_ops.constant_initializer(0.1),
      weights_regularizer=regularizers.l2_regularizer(weight_decay)):
    with arg_scope([layers.conv2d], padding='SAME'):
      with arg_scope([layers_lib.max_pool2d], padding='VALID') as arg_sc:
        return arg_sc 

def alexnet_v2_arg_scope(weight_decay=0.0005):
  with arg_scope(
      [layers.conv2d, layers_lib.fully_connected],
      activation_fn=nn_ops.relu,
      biases_initializer=init_ops.constant_initializer(0.1),
      weights_regularizer=regularizers.l2_regularizer(weight_decay)):
    with arg_scope([layers.conv2d], padding='SAME'):
      with arg_scope([layers_lib.max_pool2d], padding='VALID') as arg_sc:
        return arg_sc 

def overfeat_arg_scope(weight_decay=0.0005):
  with arg_scope(
      [layers.conv2d, layers_lib.fully_connected],
      activation_fn=nn_ops.relu,
      weights_regularizer=regularizers.l2_regularizer(weight_decay),
      biases_initializer=init_ops.zeros_initializer()):
    with arg_scope([layers.conv2d], padding='SAME'):
      with arg_scope([layers_lib.max_pool2d], padding='VALID') as arg_sc:
        return arg_sc 

def LogisticClassifier(inputs):
  return layers.fully_connected(inputs, 1, activation_fn=math_ops.sigmoid) 

def BatchNormClassifier(inputs):
  inputs = layers.batch_norm(inputs, decay=0.1)
  return layers.fully_connected(inputs, 1, activation_fn=math_ops.sigmoid) 

def truncated_vgg_16(inputs, is_training=True, scope='vgg_16'):
    """Oxford Net VGG 16-Layers version D Example.

    For use in SSD object detection network, which has this particular
    truncated version of VGG16 detailed in its paper.

    Args:
      inputs: a tensor of size [batch_size, height, width, channels].
      scope: Optional scope for the variables.

    Returns:
      the last op containing the conv5 tensor and end_points dict.
    """
    with variable_scope.variable_scope(scope, 'vgg_16', [inputs]) as sc:
        end_points_collection = sc.original_name_scope + '_end_points'
        # Collect outputs for conv2d, fully_connected and max_pool2d.
        with arg_scope(
            [layers.conv2d, layers_lib.fully_connected, layers_lib.max_pool2d],
            outputs_collections=end_points_collection
        ):
            net = layers_lib.repeat(
                inputs, 2, layers.conv2d, 64, [3, 3], scope='conv1')
            net = layers_lib.max_pool2d(net, [2, 2], scope='pool1')
            net = layers_lib.repeat(
                net, 2, layers.conv2d, 128, [3, 3], scope='conv2'
            )
            net = layers_lib.max_pool2d(net, [2, 2], scope='pool2')
            net = layers_lib.repeat(
                net, 3, layers.conv2d, 256, [3, 3], scope='conv3'
            )
            net = layers_lib.max_pool2d(net, [2, 2], scope='pool3')
            net = layers_lib.repeat(
                net, 3, layers.conv2d, 512, [3, 3], scope='conv4'
            )
            net = layers_lib.max_pool2d(net, [2, 2], scope='pool4')
            net = layers_lib.repeat(
                net, 3, layers.conv2d, 512, [3, 3], scope='conv5'
            )
            # Convert end_points_collection into a end_point dict.
            end_points = utils.convert_collection_to_dict(
                end_points_collection
            )
            return net, end_points 

def vgg_a(inputs,
          num_classes=1000,
          is_training=True,
          dropout_keep_prob=0.5,
          spatial_squeeze=True,
          scope='vgg_a'):
  """Oxford Net VGG 11-Layers version A Example.

  Note: All the fully_connected layers have been transformed to conv2d layers.
        To use in classification mode, resize input to 224x224.

  Args:
    inputs: a tensor of size [batch_size, height, width, channels].
    num_classes: number of predicted classes.
    is_training: whether or not the model is being trained.
    dropout_keep_prob: the probability that activations are kept in the dropout
      layers during training.
    spatial_squeeze: whether or not should squeeze the spatial dimensions of the
      outputs. Useful to remove unnecessary dimensions for classification.
    scope: Optional scope for the variables.

  Returns:
    the last op containing the log predictions and end_points dict.
  """
  with variable_scope.variable_scope(scope, 'vgg_a', [inputs]) as sc:
    end_points_collection = sc.original_name_scope + '_end_points'
    # Collect outputs for conv2d, fully_connected and max_pool2d.
    with arg_scope(
        [layers.conv2d, layers_lib.max_pool2d],
        outputs_collections=end_points_collection):
      net = layers_lib.repeat(
          inputs, 1, layers.conv2d, 64, [3, 3], scope='conv1')
      net = layers_lib.max_pool2d(net, [2, 2], scope='pool1')
      net = layers_lib.repeat(net, 1, layers.conv2d, 128, [3, 3], scope='conv2')
      net = layers_lib.max_pool2d(net, [2, 2], scope='pool2')
      net = layers_lib.repeat(net, 2, layers.conv2d, 256, [3, 3], scope='conv3')
      net = layers_lib.max_pool2d(net, [2, 2], scope='pool3')
      net = layers_lib.repeat(net, 2, layers.conv2d, 512, [3, 3], scope='conv4')
      net = layers_lib.max_pool2d(net, [2, 2], scope='pool4')
      net = layers_lib.repeat(net, 2, layers.conv2d, 512, [3, 3], scope='conv5')
      net = layers_lib.max_pool2d(net, [2, 2], scope='pool5')
      # Use conv2d instead of fully_connected layers.
      net = layers.conv2d(net, 4096, [7, 7], padding='VALID', scope='fc6')
      net = layers_lib.dropout(
          net, dropout_keep_prob, is_training=is_training, scope='dropout6')
      net = layers.conv2d(net, 4096, [1, 1], scope='fc7')
      net = layers_lib.dropout(
          net, dropout_keep_prob, is_training=is_training, scope='dropout7')
      net = layers.conv2d(
          net,
          num_classes, [1, 1],
          activation_fn=None,
          normalizer_fn=None,
          scope='fc8')
      # Convert end_points_collection into a end_point dict.
      end_points = utils.convert_collection_to_dict(end_points_collection)
      if spatial_squeeze:
        net = array_ops.squeeze(net, [1, 2], name='fc8/squeezed')
        end_points[sc.name + '/fc8'] = net
      return net, end_points 

def inception_v3_arg_scope(weight_decay=0.00004,
                           stddev=0.1,
                           batch_norm_var_collection='moving_vars'):
  """Defines the default InceptionV3 arg scope.

  Args:
    weight_decay: The weight decay to use for regularizing the model.
    stddev: The standard deviation of the trunctated normal weight initializer.
    batch_norm_var_collection: The name of the collection for the batch norm
      variables.

  Returns:
    An `arg_scope` to use for the inception v3 model.
  """
  batch_norm_params = {
      # Decay for the moving averages.
      'decay': 0.9997,
      # epsilon to prevent 0s in variance.
      'epsilon': 0.001,
      # collection containing update_ops.
      'updates_collections': ops.GraphKeys.UPDATE_OPS,
      # collection containing the moving mean and moving variance.
      'variables_collections': {
          'beta': None,
          'gamma': None,
          'moving_mean': [batch_norm_var_collection],
          'moving_variance': [batch_norm_var_collection],
      }
  }

  # Set weight_decay for weights in Conv and FC layers.
  with arg_scope(
      [layers.conv2d, layers_lib.fully_connected],
      weights_regularizer=regularizers.l2_regularizer(weight_decay)):
    with arg_scope(
        [layers.conv2d],
        weights_initializer=init_ops.truncated_normal_initializer(
            stddev=stddev),
        activation_fn=nn_ops.relu,
        normalizer_fn=layers_lib.batch_norm,
        normalizer_params=batch_norm_params) as sc:
      return sc 

def truncated_vgg_16(inputs, is_training=True, scope='vgg_16'):
    """Oxford Net VGG 16-Layers version D Example.

    For use in SSD object detection network, which has this particular
    truncated version of VGG16 detailed in its paper.

    Args:
      inputs: a tensor of size [batch_size, height, width, channels].
      scope: Optional scope for the variables.

    Returns:
      the last op containing the conv5 tensor and end_points dict.
    """
    with variable_scope.variable_scope(scope, 'vgg_16', [inputs]) as sc:
        end_points_collection = sc.original_name_scope + '_end_points'
        # Collect outputs for conv2d, fully_connected and max_pool2d.
        with arg_scope(
            [layers.conv2d, layers_lib.fully_connected, layers_lib.max_pool2d],
            outputs_collections=end_points_collection
        ):
            net = layers_lib.repeat(
                inputs, 2, layers.conv2d, 64, [3, 3], scope='conv1')
            net = layers_lib.max_pool2d(net, [2, 2], scope='pool1')
            net = layers_lib.repeat(
                net, 2, layers.conv2d, 128, [3, 3], scope='conv2'
            )
            net = layers_lib.max_pool2d(net, [2, 2], scope='pool2')
            net = layers_lib.repeat(
                net, 3, layers.conv2d, 256, [3, 3], scope='conv3'
            )
            net = layers_lib.max_pool2d(net, [2, 2], scope='pool3')
            net = layers_lib.repeat(
                net, 3, layers.conv2d, 512, [3, 3], scope='conv4'
            )
            net = layers_lib.max_pool2d(net, [2, 2], scope='pool4')
            net = layers_lib.repeat(
                net, 3, layers.conv2d, 512, [3, 3], scope='conv5'
            )
            # Convert end_points_collection into a end_point dict.
            end_points = utils.convert_collection_to_dict(
                end_points_collection
            )
            return net, end_points 

def inception_v2_arg_scope(weight_decay=0.00004,
                           batch_norm_var_collection='moving_vars',
                           batch_norm_decay=0.9997,
                           batch_norm_epsilon=0.001,
                           updates_collections=ops.GraphKeys.UPDATE_OPS,
                           use_fused_batchnorm=True):
  """Defines the default InceptionV2 arg scope.

  Args:
    weight_decay: The weight decay to use for regularizing the model.
    batch_norm_var_collection: The name of the collection for the batch norm
      variables.
    batch_norm_decay: Decay for batch norm moving average
    batch_norm_epsilon: Small float added to variance to avoid division by zero
    updates_collections: Collections for the update ops of the layer
    use_fused_batchnorm: Enable fused batchnorm.

  Returns:
    An `arg_scope` to use for the inception v3 model.
  """
  batch_norm_params = {
      # Decay for the moving averages.
      'decay': batch_norm_decay,
      # epsilon to prevent 0s in variance.
      'epsilon': batch_norm_epsilon,
      # collection containing update_ops.
      'updates_collections': updates_collections,
      # Enable fused batchnorm.
      'fused': use_fused_batchnorm,
      # collection containing the moving mean and moving variance.
      'variables_collections': {
          'beta': None,
          'gamma': None,
          'moving_mean': [batch_norm_var_collection],
          'moving_variance': [batch_norm_var_collection],
      }
  }

  # Set weight_decay for weights in Conv and FC layers.
  with arg_scope(
      [layers.conv2d, layers_lib.fully_connected],
      weights_regularizer=regularizers.l2_regularizer(weight_decay)):
    with arg_scope(
        [layers.conv2d],
        weights_initializer=initializers.variance_scaling_initializer(),
        activation_fn=nn_ops.relu,
        normalizer_fn=layers_lib.batch_norm,
        normalizer_params=batch_norm_params) as sc:
      return sc 

def inception_v1_arg_scope(weight_decay=0.00004,
                           use_batch_norm=True,
                           batch_norm_var_collection='moving_vars'):
  """Defines the default InceptionV1 arg scope.

  Note: Althougth the original paper didn't use batch_norm we found it useful.

  Args:
    weight_decay: The weight decay to use for regularizing the model.
    use_batch_norm: "If `True`, batch_norm is applied after each convolution.
    batch_norm_var_collection: The name of the collection for the batch norm
      variables.

  Returns:
    An `arg_scope` to use for the inception v3 model.
  """
  batch_norm_params = {
      # Decay for the moving averages.
      'decay': 0.9997,
      # epsilon to prevent 0s in variance.
      'epsilon': 0.001,
      # collection containing update_ops.
      'updates_collections': ops.GraphKeys.UPDATE_OPS,
      # collection containing the moving mean and moving variance.
      'variables_collections': {
          'beta': None,
          'gamma': None,
          'moving_mean': [batch_norm_var_collection],
          'moving_variance': [batch_norm_var_collection],
      }
  }
  if use_batch_norm:
    normalizer_fn = layers_lib.batch_norm
    normalizer_params = batch_norm_params
  else:
    normalizer_fn = None
    normalizer_params = {}
  # Set weight_decay for weights in Conv and FC layers.
  with arg_scope(
      [layers.conv2d, layers_lib.fully_connected],
      weights_regularizer=regularizers.l2_regularizer(weight_decay)):
    with arg_scope(
        [layers.conv2d],
        weights_initializer=initializers.variance_scaling_initializer(),
        activation_fn=nn_ops.relu,
        normalizer_fn=normalizer_fn,
        normalizer_params=normalizer_params) as sc:
      return sc