tf.layers.conv2d(inputs, 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, trainable=True, name=None, reuse=None)

tf.layers.conv2d(inputs, 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, trainable=True, name=None, reuse=None)

Functional interface for the 2D convolution layer.

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:

• inputs: Tensor input.
• filters: integer, the dimensionality of the output space (i.e. the number output of filters in the convolution).
• kernel_size: an integer or tuple/list of 2 integers, specifying the width and height 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 width and height. 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).
• 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, width, height, channels) while channels_first corresponds to inputs with shape (batch, channels, width, height).
• 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, no bias will be applied.
• kernel_regularizer: Optional regularizer for the convolution kernel.
• bias_regularizer: Optional regularizer for the bias vector.
• activity_regularizer: Regularizer function for the output.
• 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.
• reuse: Boolean, whether to reuse the weights of a previous layer by the same name.

Returns:

Output tensor.

Defined in tensorflow/python/layers/convolutional.py.