getSentiment.py 文件源码

def getSentimentRNN(fileToLoad,modelDir):
    checkpoint_dir = "./rnn_runs/"+modelDir+"/checkpoints/"
    batch_size = 64
    n_hidden = 256

    x_test, y_test, vocabulary, vocabulary_inv,trainS = data_helpers.load_data_for_books("./data/"+fileToLoad+".txt")
    y_test = np.argmax(y_test, axis=1)
    print("Vocabulary size: {:d}".format(len(vocabulary)))
    print("Test set size {:d}".format(len(y_test)))
    x_test = np.fliplr(x_test)

    checkpoint_file = tf.train.latest_checkpoint(checkpoint_dir)
    graph = tf.Graph()
    with graph.as_default():
        session_conf = tf.ConfigProto(
          allow_soft_placement=True,
          log_device_placement=False)
        sess = tf.Session(config=session_conf)
        with sess.as_default():
            # Load the saved meta graph and restore variables
            saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file))
            print("{}.meta".format(checkpoint_file))
            saver.restore(sess, checkpoint_file)

            # Get the placeholders from the graph by name
            input_x = graph.get_operation_by_name("x_input").outputs[0]
            predictions = graph.get_operation_by_name("prediction").outputs[0]
            istate = graph.get_operation_by_name('initial_state').outputs[0]
            keep_prob = graph.get_operation_by_name('keep_prob').outputs[0]
            # Generate batches for one epoch
            batches = data_helpers.batch_iter(x_test, batch_size, 1, shuffle=False)

            # Collect the predictions here
            all_predictions = []
            all_scores = []
            for x_test_batch in batches:
                batch_predictions = sess.run(predictions, {input_x: x_test_batch, istate: np.zeros((len(x_test_batch), 2*n_hidden)), keep_prob: 1.0})
                binaryPred = np.argmax(batch_predictions,axis=1)
                all_predictions = np.concatenate([all_predictions, binaryPred])
                all_scores = np.concatenate([all_scores, batch_predictions[:,1] - batch_predictions[:,0]])

        mbs = float(len(all_predictions[all_predictions == 1]))/len(all_predictions)
        mss = np.mean(all_scores)
        print "Mean Binary Sentiment",mbs
        print "Mean Smooth Sentiment",mss
        return all_predictions,all_scores