text_cnn_eval.py 文件源码

def text_cnn_load_model_and_eval(x_test,
                                 checkpoint_file,
                                 allow_soft_placement,
                                 log_device_placement,
                                 embeddings):

    graph = tf.Graph()
    with graph.as_default():
        session_conf = tf.ConfigProto(
            allow_soft_placement=allow_soft_placement,
            log_device_placement=log_device_placement)
        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))
            saver.restore(sess, checkpoint_file)

            # Get the placeholders from the graph by name
            input_x = graph.get_operation_by_name("input_x").outputs[0]
            # input_y = graph.get_operation_by_name("input_y").outputs[0]
            dropout_keep_prob = graph.get_operation_by_name("dropout_keep_prob").outputs[0]

            # Tensors we want to evaluate
            predictions = graph.get_operation_by_name("output/predictions").outputs[0]

            # Generate batches for one epoch
            batch_size = 50
            batches = data_helpers.batch_iter(x_test, batch_size, 1, shuffle=False)

            # Collect the predictions here
            all_predictions = []

            # Load embeddings placeholder
            embedding_size = embeddings.shape[1]
            embeddings_number = embeddings.shape[0]
            print 'embedding_size:%s, embeddings_number:%s' % (embedding_size, embeddings_number)
            # with tf.name_scope("embedding"):
            #     embeddings_placeholder = tf.placeholder(tf.float32, shape=[embeddings_number, embedding_size])
            embeddings_placeholder = graph.get_operation_by_name("embedding/Placeholder").outputs[0]

            for x_test_batch in batches:
                batch_predictions = sess.run(predictions, {input_x: x_test_batch, dropout_keep_prob: 1.0,
                                                           embeddings_placeholder: embeddings})
                all_predictions = np.concatenate([all_predictions, batch_predictions])

    return all_predictions