116 lines
4.8 KiB
Python
116 lines
4.8 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import tensorflow as tf
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from tensorflow.contrib import legacy_seq2seq
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from model.tf.dcrnn_cell import DCGRUCell
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class DCRNNModel(object):
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def __init__(self, is_training, batch_size, scaler, adj_mx, **model_kwargs):
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# Scaler for data normalization.
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self._scaler = scaler
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# Train and loss
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self._loss = None
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self._mae = None
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self._train_op = None
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max_diffusion_step = int(model_kwargs.get('max_diffusion_step', 2))
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cl_decay_steps = int(model_kwargs.get('cl_decay_steps', 1000))
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filter_type = model_kwargs.get('filter_type', 'laplacian')
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horizon = int(model_kwargs.get('horizon', 1))
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max_grad_norm = float(model_kwargs.get('max_grad_norm', 5.0))
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num_nodes = int(model_kwargs.get('num_nodes', 1))
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num_rnn_layers = int(model_kwargs.get('num_rnn_layers', 1))
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rnn_units = int(model_kwargs.get('rnn_units'))
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seq_len = int(model_kwargs.get('seq_len'))
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use_curriculum_learning = bool(model_kwargs.get('use_curriculum_learning', False))
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input_dim = int(model_kwargs.get('input_dim', 1))
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output_dim = int(model_kwargs.get('output_dim', 1))
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# Input (batch_size, timesteps, num_sensor, input_dim)
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self._inputs = tf.placeholder(tf.float32, shape=(batch_size, seq_len, num_nodes, input_dim), name='inputs')
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# Labels: (batch_size, timesteps, num_sensor, input_dim), same format with input except the temporal dimension.
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self._labels = tf.placeholder(tf.float32, shape=(batch_size, horizon, num_nodes, input_dim), name='labels')
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# GO_SYMBOL = tf.zeros(shape=(batch_size, num_nodes * input_dim))
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GO_SYMBOL = tf.zeros(shape=(batch_size, num_nodes * output_dim))
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cell = DCGRUCell(rnn_units, adj_mx, max_diffusion_step=max_diffusion_step, num_nodes=num_nodes,
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filter_type=filter_type)
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cell_with_projection = DCGRUCell(rnn_units, adj_mx, max_diffusion_step=max_diffusion_step, num_nodes=num_nodes,
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num_proj=output_dim, filter_type=filter_type)
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encoding_cells = [cell] * num_rnn_layers
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decoding_cells = [cell] * (num_rnn_layers - 1) + [cell_with_projection]
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encoding_cells = tf.contrib.rnn.MultiRNNCell(encoding_cells, state_is_tuple=True)
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decoding_cells = tf.contrib.rnn.MultiRNNCell(decoding_cells, state_is_tuple=True)
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global_step = tf.train.get_or_create_global_step()
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# Outputs: (batch_size, timesteps, num_nodes, output_dim)
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with tf.variable_scope('DCRNN_SEQ'):
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inputs = tf.unstack(tf.reshape(self._inputs, (batch_size, seq_len, num_nodes * input_dim)), axis=1)
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labels = tf.unstack(
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tf.reshape(self._labels[..., :output_dim], (batch_size, horizon, num_nodes * output_dim)), axis=1)
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labels.insert(0, GO_SYMBOL)
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def _loop_function(prev, i):
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if is_training:
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# Return either the model's prediction or the previous ground truth in training.
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if use_curriculum_learning:
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c = tf.random_uniform((), minval=0, maxval=1.)
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threshold = self._compute_sampling_threshold(global_step, cl_decay_steps)
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result = tf.cond(tf.less(c, threshold), lambda: labels[i], lambda: prev)
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else:
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result = labels[i]
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else:
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# Return the prediction of the model in testing.
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result = prev
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return result
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_, enc_state = tf.contrib.rnn.static_rnn(encoding_cells, inputs, dtype=tf.float32)
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outputs, final_state = legacy_seq2seq.rnn_decoder(labels, enc_state, decoding_cells,
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loop_function=_loop_function)
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# Project the output to output_dim.
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outputs = tf.stack(outputs[:-1], axis=1)
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self._outputs = tf.reshape(outputs, (batch_size, horizon, num_nodes, output_dim), name='outputs')
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self._merged = tf.summary.merge_all()
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@staticmethod
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def _compute_sampling_threshold(global_step, k):
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"""
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Computes the sampling probability for scheduled sampling using inverse sigmoid.
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:param global_step:
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:param k:
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:return:
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"""
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return tf.cast(k / (k + tf.exp(global_step / k)), tf.float32)
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@property
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def inputs(self):
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return self._inputs
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@property
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def labels(self):
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return self._labels
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@property
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def loss(self):
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return self._loss
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@property
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def mae(self):
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return self._mae
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@property
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def merged(self):
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return self._merged
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@property
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def outputs(self):
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return self._outputs
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