tf.compat.v1.layers.Conv2D 2D convolution layer (e.g. spatial convolution over images). Inherits From: Conv2D, Layer, Layer, Module 
tf.compat.v1.layers.Conv2D(
    filters, kernel_size, strides=(1, 1), padding='valid',
    data_format='channels_last', dilation_rate=(1, 1), activation=None,
    use_bias=True, kernel_initializer=None, bias_initializer=tf.zeros_initializer(),
    kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None,
    kernel_constraint=None, bias_constraint=None, trainable=True, name=None,
    **kwargs
)
 This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If use_bias is True (and a bias_initializer is provided), a bias vector is created and added to the outputs. Finally, if activation is not None, it is applied to the outputs as well.
 


 Arguments
  filters   Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).  
  kernel_size   An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.  
  strides   An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1.  
  padding   One of "valid" or "same" (case-insensitive). "valid" means no padding. "same" results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input.  
  data_format   A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width).  
  dilation_rate   An integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any dilation_rate value != 1 is incompatible with specifying any stride value != 1.  
  activation   Activation function. Set it to None to maintain a linear activation.  
  use_bias   Boolean, whether the layer uses a bias.  
  kernel_initializer   An initializer for the convolution kernel.  
  bias_initializer   An initializer for the bias vector. If None, the default initializer will be used.  
  kernel_regularizer   Optional regularizer for the convolution kernel.  
  bias_regularizer   Optional regularizer for the bias vector.  
  activity_regularizer   Optional regularizer function for the output.  
  kernel_constraint   Optional projection function to be applied to the kernel after being updated by an Optimizer (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training.  
  bias_constraint   Optional projection function to be applied to the bias after being updated by an Optimizer.  
  trainable   Boolean, if True also add variables to the graph collection GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).  
  name   A string, the name of the layer.   
 


 Attributes
  graph  
 
  scope_name