def create_new_layer(layer, n_dim):
''' create new layer for the graph
'''
input_shape = layer.output.shape
dense_deeper_classes = [StubDense, get_dropout_class(n_dim), StubReLU]
conv_deeper_classes = [
get_conv_class(n_dim),
get_batch_norm_class(n_dim), StubReLU
]
if is_layer(layer, "ReLU"):
conv_deeper_classes = [
get_conv_class(n_dim),
get_batch_norm_class(n_dim)
]
dense_deeper_classes = [StubDense, get_dropout_class(n_dim)]
elif is_layer(layer, "Dropout"):
dense_deeper_classes = [StubDense, StubReLU]
elif is_layer(layer, "BatchNormalization"):
conv_deeper_classes = [get_conv_class(n_dim), StubReLU]
layer_class = None
if len(input_shape) == 1:
# It is in the dense layer part.
layer_class = sample(dense_deeper_classes, 1)[0]
else:
# It is in the conv layer part.
layer_class = sample(conv_deeper_classes, 1)[0]
if layer_class == StubDense:
new_layer = StubDense(input_shape[0], input_shape[0])
elif layer_class == get_dropout_class(n_dim):
new_layer = layer_class(Constant.DENSE_DROPOUT_RATE)
elif layer_class == get_conv_class(n_dim):
new_layer = layer_class(input_shape[-1],
input_shape[-1],
sample((1, 3, 5), 1)[0],
stride=1)
elif layer_class == get_batch_norm_class(n_dim):
new_layer = layer_class(input_shape[-1])
elif layer_class == get_pooling_class(n_dim):
new_layer = layer_class(sample((1, 3, 5), 1)[0])
else:
new_layer = layer_class()
return new_layer
def __init__(self, n_output_node, input_shape):
super(CnnGenerator, self).__init__(n_output_node, input_shape)
self.n_dim = len(self.input_shape) - 1
if len(self.input_shape) > 4:
raise ValueError("The input dimension is too high.")
if len(self.input_shape) < 2:
raise ValueError("The input dimension is too low.")
self.conv = get_conv_class(self.n_dim)
self.dropout = get_dropout_class(self.n_dim)
self.global_avg_pooling = get_global_avg_pooling_class(self.n_dim)
self.pooling = get_pooling_class(self.n_dim)
self.batch_norm = get_batch_norm_class(self.n_dim)