6.3. UCTB.model_unit package

6.3.1. UCTB.model_unit.BaseModel module

class UCTB.model_unit.BaseModel.BaseModel(code_version, model_dir, gpu_device)

Bases: object

BaseModel is the base class for many models, such as STMeta, ST-MGCN and ST_ResNet,
you can also build your own model using this class. More information can be found in tutorial.
Parameters:
  • code_version – Current version of this model code, which will be used as filename for saving the model.
  • model_dir – The directory to store model files. Default:’model_dir’.
  • gpu_device – To specify the GPU to use. Default: ‘0’.
build(init_vars=True, max_to_keep=5)
Args
init_vars(bool): auto init the parameters if set to True, else no parameters will be initialized. max_to_keep: max file to keep, which equals to max_to_keep in tf.train.Saver.
close()

Close the session, release memory.

fit(sequence_length, output_names=('loss', ), op_names=('train_op', ), evaluate_loss_name='loss', batch_size=64, max_epoch=10000, validate_ratio=0.1, shuffle_data=True, early_stop_method='t-test', early_stop_length=10, early_stop_patience=0.1, verbose=True, save_model=True, save_model_name=None, auto_load_model=True, return_outputs=False, **kwargs)
Parameters:
  • sequence_length – int, the sequence length which is use in mini-batch training
  • output_names – list, [output_tensor1_name, output_tensor1_name, …]
  • op_names – list, [operation1_name, operation2_name, …]
  • evaluate_loss_name – str, should be on of the output_names, evaluate_loss_name was use in early-stopping
  • batch_size – int, default 64, batch size
  • max_epoch – int, default 10000, max number of epochs
  • validate_ratio – float, default 0.1, the ration of data that will be used as validation dataset
  • shuffle_data – bool, default True, whether shuffle data in mini-batch train
  • early_stop_method – should be ‘t-test’ or ‘naive’, both method are explained in train.EarlyStopping
  • early_stop_length – int, must provide when early_stop_method=’t-test’
  • early_stop_patience – int, must provide when early_stop_method=’naive’
  • verbose – Bool, flag to print training information or not
  • save_model – Bool, flog to save model or not
  • save_model_name – String, filename for saving the model, which will overwrite the code_version.
  • auto_load_model – Bool, the “fit” function will automatically load the model from disk, if exists, before the training. Set to False to disable the auto-loading.
  • return_outputs – Bool, set True to return the training log, otherwise nothing will be returned
load(subscript)
Parameters:subscript – String, subscript will be appended to the code version as the model file name, and load the corresponding model using this filename
load_event_scalar(scalar_name='val_loss')
Parameters:scalar_name – load the corresponding scalar name from tensorboard-file, e.g. load_event_scalar(‘val_loss)
predict(sequence_length, output_names=('prediction', ), cache_volume=64, **kwargs)
Parameters:
  • output_names – list, [output_tensor_name1, output_tensor_name2, …]
  • sequence_length – int, the length of sequence, which is use in mini-batch training
  • cache_volume – int, default 64, we need to set cache_volume if the cache can not hold the whole validation dataset

:param : return: outputs_dict: dict, like {output_tensor1_name: output_tensor1_value, …}

save(subscript, global_step)
Parameters:
  • subscript – String, subscript will be appended to the code version as the model filename, and save the corresponding model using this filename
  • global_step – Int, current training steps

6.3.2. UCTB.model_unit.DCRNN_CELL module

class UCTB.model_unit.DCRNN_CELL.DCGRUCell(num_units, input_dim, num_graphs, supports, max_diffusion_step, num_node, num_proj=None, activation=<function tanh>, reuse=None, use_gc_for_ru=True, name=None)

Bases: tensorflow.python.ops.rnn_cell_impl.RNNCell

Graph Convolution Gated Recurrent Unit cell.

call(inputs, **kwargs)
compute_output_shape(input_shape)
output_size
state_size

6.3.3. UCTB.model_unit.GraphModelLayers module

class UCTB.model_unit.GraphModelLayers.GAL

Bases: object

This class provides static methods for adding Graph Attention Layer.

static add_ga_layer_matrix(inputs, units, num_head, activation=<function tanh>)

This method use Multi-head attention technique to add Graph Attention Layer.

Parameters:
  • input (ndarray) – The set of node features data, with shape [batch, num_node, num_featuer].
  • unit (int) – The number of merge_gal_units used in GAL.
  • num_head (int) – The number of multi-head used in GAL.
  • activation (function) – activation function. default:tf.nn.tanh.
Returns:

The weight matrix after softmax function. gc_output: The final GAL aggregated feature representation from input feature.
Return type:

alpha
static add_residual_ga_layer(inputs, units, num_head, activation=<function tanh>)

Call the add_ga_layer_matrix function to build the Graph Attention Layer, and add the residual layer to optimize the deep neural network.

static attention_merge_weight(inputs, units, num_head, activation=<function leaky_relu>)
class UCTB.model_unit.GraphModelLayers.GCL

Bases: object

This class provides static methods for adding Graph Convolution Layer.

static add_gc_layer(inputs, gcn_k, laplacian_matrix, output_size, dtype=tf.float32, use_bias=True, trainable=True, initializer=None, regularizer=None, activation=<function tanh>)
Parameters:
  • Input (ndarray) – The input features with shape [batch, num_node, num_feature].
  • gcn_k (int) – The highest order of Chebyshev Polynomial approximation in GCN.
  • laplacian_matrix (ndarray) – Laplacian matrix used in GCN, with shape [num_node, num_node].
  • output_size (int) – Number of output channels.
  • dtype – Data type. default:tf.float32.
  • use_bias (bool) – It determines whether to add bias in the output. default:True.
  • trainable (bool) – It determines whether weights tensor can be trained. default:True.
  • initializer – It determines whether the “weight” tensor is initialized. default:None.
  • regularizer – It determines whether the “weight” tensor is regularized. default:None.
  • activation (function) – activation function. default:tf.nn.tanh.
Returns:

Returns the result of convolution of inputs and laplacian_matrix
static add_multi_gc_layers(inputs, gcn_k, gcn_l, output_size, laplacian_matrix, activation=<function tanh>)

Call add_gc_layer function to add multi Graph Convolution Layer.`gcn_l` is the number of layers added.

6.3.4. UCTB.model_unit.ST_RNN module

class UCTB.model_unit.ST_RNN.GCLSTMCell(units, num_node, laplacian_matrix, gcn_k=1, gcn_l=1, **kwargs)

Bases: tensorflow.python.keras.layers.recurrent.LSTMCell

GCLSTMCell is one of our implemented ST-RNN models in handling the spatial and temporal features. We performed GCN on both LSTM inputs and hidden-states. The code is inherited from tf.keras.layers.LSTMCell, thus it can be used almost the same as LSTMCell except that you need to provide the GCN parameters in the __init__ function.

Parameters:
  • units (int) – number of units of LSTM
  • num_node (int) – number of nodes in the graph
  • laplacian_matrix (ndarray) – laplacian matrix used in GCN, with shape [num_node, num_node]
  • gcn_k (int) – highest order of Chebyshev Polynomial approximation in GCN
  • gcn_l (int) – number of GCN layers
  • kwargs – other parameters supported by LSTMCell, such as activation, kernel_initializer … and so on.