| with tf.Session() as train_sess: | |
| init = tf.global_variables_initializer() | |
| train_sess.run(init) | |
| for step in range(self.max_step_per_action): | |
| batch_x, batch_y = self.mnist.train.next_batch(self.bathc_size) | |
| feed = {model.X: batch_x, | |
| model.Y: batch_y, | |
| model.dropout_keep_prob: self.dropout_rate, | |
| model.cnn_dropout_rates: cnn_drop_rate} | |
| _ = train_sess.run(train_op, feed_dict=feed) | |
| batch_x, batch_y = self.mnist.test.next_batch(10000) | |
| loss, acc = train_sess.run( | |
| [loss_op, model.accuracy], | |
| feed_dict={model.X: batch_x, | |
| model.Y: batch_y, | |
| model.dropout_keep_prob: 1.0, | |
| model.cnn_dropout_rates: [1.0]*len(cnn_drop_rate)}) | |
| if acc - pre_acc <= 0.01: | |
| return acc, acc | |
| else: | |
| return 0.1, acc |
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