Function conv_module

tests/utils/custom_layers/basic_layers.py:64–97

(
    prev_layer, n_out_channel, filter_size, strides, padding, is_train=True, use_batchnorm=True, activation_fn=None,
    conv_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 conv_module(
    prev_layer, n_out_channel, filter_size, strides, padding, is_train=True, use_batchnorm=True, activation_fn=None,
    conv_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)

    conv_name = 'conv2d' if name is None else name
    bn_name = 'batch_norm' if name is None else name + '/BatchNorm'

    layer = tl.layers.Conv2d(
        prev_layer,
        n_filter=n_out_channel,
        filter_size=filter_size,
        strides=strides,
        padding=padding,
        act=None,
        W_init=conv_init,
        b_init=None if use_batchnorm else bias_init,  # Not useful as the convolutions are batch normalized
        name=conv_name
    )

    if use_batchnorm:

        layer = tl.layers.BatchNormLayer(layer, act=None, is_train=is_train, gamma_init=batch_norm_init, name=bn_name)

    logits = layer.outputs

    layer = activation_module(layer, activation_fn, name=conv_name)

    return layer, logits


def dense_module(