def __init__(self, n_output_node, input_shape):
super(MobileNetV2Generator, self).__init__(n_output_node, input_shape)
""" configuration for complete net:
self.cfg = [(1, 16, 1, 1),
(6, 24, 2, 1) , # NOTE: change stride 2 -> 1 for CIFAR10
(6, 32, 3, 2),
(6, 64, 4, 2),
(6, 96, 3, 1),
(6, 160, 3, 2),
(6, 320, 1, 1)]
"""
# we try smaller net configuration (so autokeras will be able to expand the net)
self.cfg = [(1, 16, 1, 1),
(6, 24, 2, 1)] # , # NOTE: change stride 2 -> 1 for CIFAR10
#(6, 32, 3, 2) ,
#(6, 64, 4, 2),
#(6, 96, 3, 1),
#(6, 160, 3, 2),
#(6, 320, 1, 1)]
self.in_planes = 32
self.block_expansion = 1
self.n_dim = len(self.input_shape) - 1
if len(self.input_shape) > 4:
raise ValueError('The input dimension is too high.')
elif 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.adaptive_avg_pooling = get_global_avg_pooling_class(self.n_dim)
self.batch_norm = get_batch_norm_class(self.n_dim)
def __init__(self, n_output_node, input_shape):
super(ResNetGenerator, self).__init__(n_output_node, input_shape)
# self.layers = [2, 2, 2, 2]
self.in_planes = 64
self.block_expansion = 1
self.n_dim = len(self.input_shape) - 1
if len(self.input_shape) > 4:
raise ValueError('The input dimension is too high.')
elif 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.adaptive_avg_pooling = get_global_avg_pooling_class(self.n_dim)
self.batch_norm = get_batch_norm_class(self.n_dim)
def __init__(self, n_output_node, input_shape):
super().__init__(n_output_node, input_shape)
# DenseNet Constant
self.num_init_features = 64
self.growth_rate = 32
self.block_config = (6, 12, 24, 16)
self.bn_size = 4
self.drop_rate = 0
# Stub layers
self.n_dim = len(self.input_shape) - 1
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.adaptive_avg_pooling = get_global_avg_pooling_class(self.n_dim)
self.max_pooling = get_pooling_class(self.n_dim)
self.avg_pooling = get_avg_pooling_class(self.n_dim)
self.batch_norm = get_batch_norm_class(self.n_dim)
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)
def __init__(self, n_output_node, input_shape):
"""Initialize the instance.
Args:
n_output_node: An integer. Number of output nodes in the network.
input_shape: A tuple. Input shape of the network.
"""
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)
def __init__(self, n_output_node, input_shape):
"""Initialize the instance.
Args:
n_output_node: An integer. Number of output nodes in the network.
input_shape: A tuple. Input shape of the network.
"""
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)
