/// 2D convolution layer (e.g. spatial convolution over images).
/// This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs.
/// If use_bias is True, a bias vector is created and added to the outputs.Finally, if activation is not None, it is applied to the outputs as well.
/// </summary>
/// <param name="filters">Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution)</param>
/// <param name="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.</param>
/// <param name="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.</param>
/// <param name="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.</param>
/// <param name="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_size, height, width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be channels_last.</param>
/// <param name="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.</param>
/// <param name="groups">A positive integer specifying the number of groups in which the input is split along the channel axis. Each group is convolved separately with filters / groups filters. The output is the concatenation of all the groups results along the channel axis. Input channels and filters must both be divisible by groups.</param>
/// <param name="activation">Activation function to use. If you don't specify anything, no activation is applied (see keras.activations).</param>
/// <param name="use_bias">Boolean, whether the layer uses a bias vector.</param>
/// <param name="kernel_initializer">The name of the initializer for the kernel weights matrix (see keras.initializers).</param>
/// <param name="bias_initializer">The name of the initializer for the bias vector (see keras.initializers).</param>
/// <param name="kernel_regularizer">The name of the regularizer function applied to the kernel weights matrix (see keras.regularizers).</param>
/// <param name="bias_regularizer">The name of the regularizer function applied to the bias vector (see keras.regularizers).</param>
/// <param name="activity_regularizer">The name of the regularizer function applied to the output of the layer (its "activation") (see keras.regularizers).</param>
/// <param name="bias_constraint">Constraint function for the bias.</param>
/// <returns>N-D tensor with shape: (batch_size, ..., units). For instance, for a 2D input with shape (batch_size, input_dim), the output would have shape (batch_size, units).</returns>
lingbai-kong
2 years ago