在某些任务中，我们需要针对不同的情况训练多个不同的神经网络模型，这时候，在测试阶段，我们就需要调用多个预训练好的模型分别来进行预测。

弄明白了如何调用单个模型，其实调用多个模型也就顺理成章。我们只需要建立多个图，然后每个图导入一个模型，再针对每个图创建一个会话，分别进行预测即可。

import tensorflow as tf import numpy as np # 建立两个 graph g1 = tf.Graph() g2 = tf.Graph() # 为每个 graph 建创建一个 session sess1 = tf.Session(graph=g1) sess2 = tf.Session(graph=g2) X_1 = None tst_1 = None yhat_1 = None X_2 = None tst_2 = None yhat_2 = None def load_model(sess): """ Loading the pre-trained model and parameters. """ global X_1, tst_1, yhat_1 with sess1.as_default(): with sess1.graph.as_default(): modelpath = r'F:/resnet/model/new0.25-0.35/' saver = tf.train.import_meta_graph(modelpath + 'model-10.meta') saver.restore(sess1, tf.train.latest_checkpoint(modelpath)) graph = tf.get_default_graph() X_1 = graph.get_tensor_by_name("X:0") tst_1 = graph.get_tensor_by_name("tst:0") yhat_1 = graph.get_tensor_by_name("tanh:0") print('Successfully load the model_1!') def load_model_2(): """ Loading the pre-trained model and parameters. """ global X_2, tst_2, yhat_2 with sess2.as_default(): with sess2.graph.as_default(): modelpath = r'F:/resnet/model/new0.25-0.352/' saver = tf.train.import_meta_graph(modelpath + 'model-10.meta') saver.restore(sess2, tf.train.latest_checkpoint(modelpath)) graph = tf.get_default_graph() X_2 = graph.get_tensor_by_name("X:0") tst_2 = graph.get_tensor_by_name("tst:0") yhat_2 = graph.get_tensor_by_name("tanh:0") print('Successfully load the model_2!') def test_1(txtdata): """ Convert data to Numpy array which has a shape of (-1, 41, 41, 41, 3). Test a single axample. Arg: txtdata: Array in C. Returns: The normal of a face. """ global X_1, tst_1, yhat_1 data = np.array(txtdata) data = data.reshape(-1, 41, 41, 41, 3) output = sess1.run(yhat_1, feed_dict={X_1: data, tst_1: True}) # (100, 3) output = output.reshape(-1, 1) ret = output.tolist() return ret def test_2(txtdata): """ Convert data to Numpy array which has a shape of (-1, 41, 41, 41, 3). Test a single axample. Arg: txtdata: Array in C. Returns: The normal of a face. """ global X_2, tst_2, yhat_2 data = np.array(txtdata) data = data.reshape(-1, 41, 41, 41, 3) output = sess2.run(yhat_2, feed_dict={X_2: data, tst_2: True}) # (100, 3) output = output.reshape(-1, 1) ret = output.tolist() return ret

最后，本程序只是为了说明问题，抛砖引玉，代码有很多冗余之处，不要模仿！

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