1. 准备预训练好的模型

  • TensorFlow 预训练好的模型被保存为以下四个文件

  • data 文件是训练好的参数值，meta 文件是定义的神经网络图，checkpoint 文件是所有模型的保存路径，如下所示，为简单起见只保留了一个模型。
model_checkpoint_path: "/home/senius/python/c_python/test/model-40"
all_model_checkpoint_paths: "/home/senius/python/c_python/test/model-40"

2. 导入模型图、参数值和相关变量

import tensorflow as tf import numpy as np sess = tf.Session() X = None # input yhat = None # output def load_model(): """ Loading the pre-trained model and parameters. """ global X, yhat modelpath = r'/home/senius/python/c_python/test/' saver = tf.train.import_meta_graph(modelpath + 'model-40.meta') saver.restore(sess, tf.train.latest_checkpoint(modelpath)) graph = tf.get_default_graph() X = graph.get_tensor_by_name("X:0") yhat = graph.get_tensor_by_name("tanh:0") print('Successfully load the pre-trained model!')
  • 通过 saver.restore 我们可以得到预训练的所有参数值，然后再通过 graph.get_tensor_by_name 得到模型的输入张量和我们想要的输出张量。

3. 运行前向传播过程得到预测值

def predict(txtdata): """ Convert data to Numpy array which has a shape of (-1, 41, 41, 41 3). Test a single example. Arg: txtdata: Array in C. Returns: Three coordinates of a face normal. """ global X, yhat data = np.array(txtdata) data = data.reshape(-1, 41, 41, 41, 3) output = sess.run(yhat, feed_dict={X: data}) # (-1, 3) output = output.reshape(-1, 1) ret = output.tolist() return ret
  • 通过 feed_dict 喂入测试数据，然后 run 输出的张量我们就可以得到预测值。

4. 测试

load_model() testdata = np.fromfile('/home/senius/python/c_python/test/04t30t00.npy', dtype=np.float32) testdata = testdata.reshape(-1, 41, 41, 41, 3) # (150, 41, 41, 41, 3) testdata = testdata[0:2, ...] # the first two examples txtdata = testdata.tolist() output = predict(txtdata) print(output) # [[-0.13345889747142792], [0.5858198404312134], [-0.7211828231811523],  # [-0.03778800368309021], [0.9978875517845154], [0.06522832065820694]]
  • 本例输入是一个三维网格模型处理后的 [41, 41, 41, 3] 的数据，输出一个表面法向量坐标 (x, y, z)。

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