Function dense_module

tests/utils/custom_layers/basic_layers.py:100–134

(
    prev_layer, n_units, is_train, use_batchnorm=True, activation_fn=None, dense_init=tl.initializers.random_uniform(),
    batch_norm_init=tl.initializers.truncated_normal(mean=1., stddev=0.02), bias_init=tf.zeros_initializer(), name=None
)

def dense_module(
    prev_layer, n_units, is_train, use_batchnorm=True, activation_fn=None, dense_init=tl.initializers.random_uniform(),
    batch_norm_init=tl.initializers.truncated_normal(mean=1., stddev=0.02), bias_init=tf.zeros_initializer(), name=None
):

    if activation_fn not in ["ReLU", "ReLU6", "Leaky_ReLU", "PReLU", "PReLU6", "PTReLU6", "CReLU", "ELU", "SELU",
                             "tanh", "sigmoid", "softmax", None]:
        raise Exception("Unknown 'activation_fn': %s" % activation_fn)

    # Flatten: Conv to FC
    if prev_layer.outputs.get_shape().__len__() != 2:  # The input dimension must be rank 2
        layer = tl.layers.FlattenLayer(prev_layer, name='flatten')

    else:
        layer = prev_layer

    layer = tl.layers.DenseLayer(
        layer,
        n_units=n_units,
        act=None,
        W_init=dense_init,
        b_init=None if use_batchnorm else bias_init,  # Not useful as the convolutions are batch normalized
        name='dense' if name is None else name
    )

    if use_batchnorm:
        layer = tl.layers.BatchNormLayer(
            layer, act=None, is_train=is_train, gamma_init=batch_norm_init, name='batch_norm'
        )

    logits = layer.outputs

    layer = activation_module(layer, activation_fn)

    return layer, logits