(self)
| 272 | saver.save(sess, self.model_dir) |
| 273 | |
| 274 | def test(self): |
| 275 | x = tf.placeholder("float", [None, self.max_seq_len * 2, self.word_vec_dim]) |
| 276 | y = tf.placeholder("float", [None, self.max_seq_len, self.one_hot_word_vectors_dim]) |
| 277 | |
| 278 | weights = { |
| 279 | 'enc2dec': tf.Variable(tf.random_normal([self.word_vec_dim, self.one_hot_word_vectors_dim])), |
| 280 | 'hid2tar': tf.Variable(tf.random_normal([self.n_hidden, self.one_hot_word_vectors_dim])), |
| 281 | } |
| 282 | biases = { |
| 283 | 'enc2dec': tf.Variable(tf.random_normal([self.max_seq_len, self.one_hot_word_vectors_dim])), |
| 284 | 'hid2tar': tf.Variable(tf.random_normal([self.max_seq_len, self.one_hot_word_vectors_dim])), |
| 285 | } |
| 286 | |
| 287 | optimizer, cost, decoder_layer2_outputs = self.model(x, y, weights, biases, training=False) |
| 288 | |
| 289 | init = tf.global_variables_initializer() |
| 290 | sess = tf.Session() |
| 291 | sess.run(init) |
| 292 | saver = tf.train.Saver() |
| 293 | saver.restore(sess, self.model_dir) |
| 294 | |
| 295 | XY, Y = self.next_batch() |
| 296 | n_steps = len(XY) |
| 297 | for step in range(n_steps): |
| 298 | train_XY = XY[step:] |
| 299 | train_Y = Y[step:] |
| 300 | loss = sess.run(cost, feed_dict={x: train_XY, y: train_Y}) |
| 301 | print sess.run(decoder_layer2_outputs, feed_dict={x: train_XY, y: train_Y}) |
| 302 | print 'loss=%f' % loss |
| 303 | |
| 304 | def predict(self): |
| 305 | x = tf.placeholder("float", [None, self.max_seq_len * 2, self.word_vec_dim]) |
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