| 104 | self.get_embeddings() |
| 105 | |
| 106 | def train(self, batch_size=1024, epochs=1, initial_epoch=0, verbose=1): |
| 107 | adjacency = self.A.toarray().astype(np.float32) |
| 108 | laplacian = self.L.toarray().astype(np.float32) |
| 109 | if batch_size >= self.node_size: |
| 110 | if batch_size > self.node_size: |
| 111 | print('batch_size({0}) > node_size({1}),set batch_size = {1}'.format( |
| 112 | batch_size, self.node_size)) |
| 113 | batch_size = self.node_size |
| 114 | return self.model.fit( |
| 115 | adjacency, |
| 116 | [adjacency, laplacian], |
| 117 | batch_size=batch_size, |
| 118 | epochs=epochs, |
| 119 | initial_epoch=initial_epoch, |
| 120 | verbose=verbose, |
| 121 | shuffle=False, |
| 122 | ) |
| 123 | else: |
| 124 | steps_per_epoch = (self.node_size - 1) // batch_size + 1 |
| 125 | hist = History() |
| 126 | hist.set_model(self.model) |
| 127 | hist.on_train_begin() |
| 128 | logs = {} |
| 129 | for epoch in range(initial_epoch, epochs): |
| 130 | start_time = time.time() |
| 131 | losses = np.zeros(3) |
| 132 | for i in range(steps_per_epoch): |
| 133 | index = np.arange( |
| 134 | i * batch_size, min((i + 1) * batch_size, self.node_size)) |
| 135 | A_train = adjacency[index, :] |
| 136 | L_mat_train = laplacian[index][:, index] |
| 137 | batch_losses = np.asarray(self.model.train_on_batch(A_train, [A_train, L_mat_train])) |
| 138 | losses += batch_losses |
| 139 | losses = losses / steps_per_epoch |
| 140 | |
| 141 | logs['loss'] = losses[0] |
| 142 | logs['2nd_loss'] = losses[1] |
| 143 | logs['1st_loss'] = losses[2] |
| 144 | epoch_time = int(time.time() - start_time) |
| 145 | hist.on_epoch_end(epoch, logs) |
| 146 | if verbose > 0: |
| 147 | print('Epoch {0}/{1}'.format(epoch + 1, epochs)) |
| 148 | print('{0}s - loss: {1: .4f} - 2nd_loss: {2: .4f} - 1st_loss: {3: .4f}'.format( |
| 149 | epoch_time, losses[0], losses[1], losses[2])) |
| 150 | return hist |
| 151 | |
| 152 | def evaluate(self, ): |
| 153 | adjacency = self.A.toarray().astype(np.float32) |