dnn.py 文件源码

def build_net(in_shape, out_size, model):
    # input variables
    input_var = (tt.tensor3('input', dtype='float32')
                 if len(in_shape) > 1 else
                 tt.matrix('input', dtype='float32'))
    target_var = tt.matrix('target_output', dtype='float32')

    # stack more layers
    network = lnn.layers.InputLayer(
        name='input', shape=(None,) + in_shape, input_var=input_var)

    if 'conv' in model and model['conv']:
        # reshape to 1 "color" channel
        network = lnn.layers.reshape(
            network, shape=(-1, 1) + in_shape, name='reshape')

        for c in sorted(model['conv'].keys()):
            network = blocks.conv(network, **model['conv'][c])

    # no more output layer if gap is already there!
    if 'gap' in model and model['gap']:
        network = blocks.gap(network, out_size=out_size,
                             out_nonlinearity=model['out_nonlinearity'],
                             **model['gap'])
    else:
        if 'dense' in model and model['dense']:
            network = blocks.dense(network, **model['dense'])

        # output layer
        out_nl = getattr(lnn.nonlinearities, model['out_nonlinearity'])
        network = lnn.layers.DenseLayer(
            network, name='output', num_units=out_size,
            nonlinearity=out_nl)

    return network, input_var, target_var