(inputs,
                       is_training,
                       weight_decay,
                       params=None,
                       moments=None,
                       depth_multiplier=1.0,
                       reuse=tf.AUTO_REUSE,
                       scope='four_layer_convnet',
                       backprop_through_moments=True,
                       use_bounded_activation=False,
                       keep_spatial_dims=False)
return _four_layer_convnet(
      inputs,
      is_training,
      scope,
      weight_decay=weight_decay,
      reuse=reuse,
      params=params,
      moments=moments,
      depth_multiplier=depth_multiplier,
      backprop_through_moments=backprop_through_moments,
      use_bounded_activation=use_bounded_activation,
      keep_spatial_dims=keep_spatial_dims)
Embeds inputs using a standard four-layer convnet.

  Args:
    inputs: Tensors of shape [None, ] + image shape, e.g. [15, 84, 84, 3]
    is_training: Whether we are in the training phase.
    weight_decay: float, scaling constant for L2 weight decay on weight
      variables.
    params: None will create new params (or reuse from scope), otherwise an
      ordered dict of convolutional kernels and biases such that
      params['kernel_0'] stores the kernel of the first convolutional layer,
      etc.
    moments: A dict of the means and vars of the different layers to use for
      batch normalization. If not provided, the mean and var are computed based
      on the given inputs.
    depth_multiplier: The depth multiplier for the convnet channels.
    reuse: Whether to reuse the network's weights.
    scope: An optional scope for the tf operations.
    backprop_through_moments: Whether to allow gradients to flow through the
      given support set moments. Only applies to non-transductive batch norm.
    use_bounded_activation: Whether to enable bounded activation. This is useful
      for post-training quantization.
    keep_spatial_dims: bool, if True the spatial dimensions are kept.

  Returns:
    A 2D Tensor, where each row is the embedding of an input in inputs.
Embeds inputs using a standard four-layer convnet.
Embeds inputs using a standard four - layer convnet .
def four_layer_convnet(inputs,
                       is_training,
                       weight_decay,
                       params=None,
                       moments=None,
                       depth_multiplier=1.0,
                       reuse=tf.AUTO_REUSE,
                       scope='four_layer_convnet',
                       backprop_through_moments=True,
                       use_bounded_activation=False,
                       keep_spatial_dims=False):
  """Embeds inputs using a standard four-layer convnet.

  Args:
    inputs: Tensors of shape [None, ] + image shape, e.g. [15, 84, 84, 3]
    is_training: Whether we are in the training phase.
    weight_decay: float, scaling constant for L2 weight decay on weight
      variables.
    params: None will create new params (or reuse from scope), otherwise an
      ordered dict of convolutional kernels and biases such that
      params['kernel_0'] stores the kernel of the first convolutional layer,
      etc.
    moments: A dict of the means and vars of the different layers to use for
      batch normalization. If not provided, the mean and var are computed based
      on the given inputs.
    depth_multiplier: The depth multiplier for the convnet channels.
    reuse: Whether to reuse the network's weights.
    scope: An optional scope for the tf operations.
    backprop_through_moments: Whether to allow gradients to flow through the
      given support set moments. Only applies to non-transductive batch norm.
    use_bounded_activation: Whether to enable bounded activation. This is useful
      for post-training quantization.
    keep_spatial_dims: bool, if True the spatial dimensions are kept.

  Returns:
    A 2D Tensor, where each row is the embedding of an input in inputs.
  """
  return _four_layer_convnet(
      inputs,
      is_training,
      scope,
      weight_decay=weight_decay,
      reuse=reuse,
      params=params,
      moments=moments,
      depth_multiplier=depth_multiplier,
      backprop_through_moments=backprop_through_moments,
      use_bounded_activation=use_bounded_activation,
      keep_spatial_dims=keep_spatial_dims)
def four_layer_convnet ( inputs , is_training , weight_decay , params = None , moments = None , depth_multiplier = 1.0 , reuse = tf . AUTO_REUSE , scope = 'four_layer_convnet' , backprop_through_moments = True , use_bounded_activation = False , keep_spatial_dims = False ) : return _four_layer_convnet ( inputs , is_training , scope , weight_decay = weight_decay , reuse = reuse , params = params , moments = moments , depth_multiplier = depth_multiplier , backprop_through_moments = backprop_through_moments , use_bounded_activation = use_bounded_activation , keep_spatial_dims = keep_spatial_dims )