def create_new_layer(input_shape, n_dim): 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 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(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 deeper_conv_block(conv_layer, kernel_size, weighted=True): n_dim = get_n_dim(conv_layer) filter_shape = (kernel_size, ) * 2 n_filters = conv_layer.filters weight = np.zeros((n_filters, n_filters) + filter_shape) center = tuple(map(lambda x: int((x - 1) / 2), filter_shape)) for i in range(n_filters): filter_weight = np.zeros((n_filters, ) + filter_shape) index = (i, ) + center filter_weight[index] = 1 weight[i, ...] = filter_weight bias = np.zeros(n_filters) new_conv_layer = get_conv_class(n_dim)(conv_layer.filters, n_filters, kernel_size=kernel_size) bn = get_batch_norm_class(n_dim)(n_filters) if weighted: new_conv_layer.set_weights( (add_noise(weight, np.array([0, 1])), add_noise(bias, np.array([0, 1])))) new_weights = [ add_noise(np.ones(n_filters, dtype=np.float32), np.array([0, 1])), add_noise(np.zeros(n_filters, dtype=np.float32), np.array([0, 1])), add_noise(np.zeros(n_filters, dtype=np.float32), np.array([0, 1])), add_noise(np.ones(n_filters, dtype=np.float32), np.array([0, 1])) ] bn.set_weights(new_weights) return [StubReLU(), new_conv_layer, bn]
def __init__(self, input_shape, weighted=True): """Initializer for Graph. Args: input_shape: A tuple describing the input tensor shape, not including the number of instances. weighted: A boolean marking if there are actual values in the weights of the layers. Sometime we only need the neural architecture information with a graph. In that case, we do not save the weights to save memory and time. """ self.input_shape = input_shape self.weighted = weighted self.node_list = [] self.layer_list = [] # node id start with 0 self.node_to_id = {} self.layer_to_id = {} self.layer_id_to_input_node_ids = {} self.layer_id_to_output_node_ids = {} self.adj_list = {} self.reverse_adj_list = {} self.operation_history = [] self.n_dim = len(input_shape) - 1 self.conv = get_conv_class(self.n_dim) self.batch_norm = get_batch_norm_class(self.n_dim) self.vis = None self._add_node(Node(input_shape))
def wider_bn(layer, start_dim, total_dim, n_add, weighted=True): n_dim = get_n_dim(layer) if not weighted: return get_batch_norm_class(n_dim)(layer.num_features + n_add) weights = layer.get_weights() new_weights = [add_noise(np.ones(n_add, dtype=np.float32), np.array([0, 1])), add_noise(np.zeros(n_add, dtype=np.float32), np.array([0, 1])), add_noise(np.zeros(n_add, dtype=np.float32), np.array([0, 1])), add_noise(np.ones(n_add, dtype=np.float32), np.array([0, 1]))] student_w = tuple() for weight, new_weight in zip(weights, new_weights): temp_w = weight.copy() temp_w = np.concatenate((temp_w[:start_dim], new_weight, temp_w[start_dim:total_dim])) student_w += (temp_w,) new_layer = get_batch_norm_class(n_dim)(layer.num_features + n_add) new_layer.set_weights(student_w) 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)
def create_new_layer(layer, n_dim): 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, LayerType.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, LayerType.DROPOUT): dense_deeper_classes = [StubDense, StubReLU] elif is_layer(layer, LayerType.BATCH_NORM): conv_deeper_classes = [get_conv_class(n_dim), StubReLU] 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(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): """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, input_shape, weighted=True): self.weighted = weighted self.node_list = [] self.layer_list = [] # node id start with 0 self.node_to_id = {} self.layer_to_id = {} self.layer_id_to_input_node_ids = {} self.layer_id_to_output_node_ids = {} self.adj_list = {} self.reverse_adj_list = {} self.operation_history = [] self.n_dim = len(input_shape) - 1 self.conv = get_conv_class(self.n_dim) self.batch_norm = get_batch_norm_class(self.n_dim) self.vis = None self._add_node(Node(input_shape))
def create_new_layer(layer, n_dim): 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, LayerType.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, LayerType.DROPOUT): dense_deeper_classes = [StubDense, StubReLU] elif is_layer(layer, LayerType.BATCH_NORM): conv_deeper_classes = [get_conv_class(n_dim)] #, StubReLU] new_layers = [] 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]) new_layers.append(new_layer) elif layer_class == get_dropout_class(n_dim): new_layer = layer_class(Constant.DENSE_DROPOUT_RATE) new_layers.append(new_layer) elif layer_class == get_conv_class(n_dim): # add conv layer # new_layer = layer_class(input_shape[-1],, input_shape[-1], sample((1, 3, 5), 1)[0], stride=1) # add mobilenet block in_planes = input_shape[-1] expansion = sample((1, 6), 1)[0] stride = sample((1, 2), 1)[0] planes = expansion * in_planes new_layer = layer_class(in_planes, planes, 1, stride=1, padding=0) new_layers.append(new_layer) new_layer = get_batch_norm_class(n_dim)(planes) new_layers.append(new_layer) new_layer = StubReLU() new_layers.append(new_layer) new_layer = layer_class(planes, planes, 3, stride=stride, padding=1, groups=planes) new_layers.append(new_layer) new_layer = get_batch_norm_class(n_dim)(planes) new_layers.append(new_layer) new_layer = StubReLU() new_layers.append(new_layer) new_layer = layer_class(planes, in_planes, 1, stride=1, padding=0) new_layers.append(new_layer) new_layer = get_batch_norm_class(n_dim)(in_planes) new_layers.append(new_layer) elif layer_class == get_batch_norm_class(n_dim): new_layer = layer_class(input_shape[-1]) new_layers.append(new_layer) elif layer_class == get_pooling_class(n_dim): new_layer = layer_class(sample((1, 3, 5), 1)[0]) new_layers.append(new_layer) else: new_layer = layer_class() new_layers.append(new_layer) return new_layers