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 useBias is True, a bias vector is created and added to the outputs.
If activation is not null, it is applied to the outputs as well.
When using this layer as the first layer in a model,
provide the keyword argument inputShape
(Array of integers, does not include the sample axis),
e.g. inputShape=[128, 128, 3] for 128x128 RGB pictures
in dataFormat='channelsLast'.
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
useBiasis True, a bias vector is created and added to the outputs.If
activationis notnull, it is applied to the outputs as well.When using this layer as the first layer in a model, provide the keyword argument
inputShape(Array of integers, does not include the sample axis), e.g.inputShape=[128, 128, 3]for 128x128 RGB pictures indataFormat='channelsLast'.