1. 準備預訓練好的模型

• TensorFlow 預訓練好的模型被儲存為以下四個檔案

• data 檔案是訓練好的引數值，meta 檔案是定義的神經網路圖，checkpoint 檔案是所有模型的儲存路徑，如下所示，為簡單起見只保留了一個模型。

model_checkpoint_path: "/home/senius/python/c_python/test/model-40"
all_model_checkpoint_paths: "/home/senius/python/c_python/test/model-40"

2. 匯入模型圖、引數值和相關變數

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!')

• 通過 saver.restore 我們可以得到預訓練的所有引數值，然後再通過 graph.get_tensor_by_name 得到模型的輸入張量和我們想要的輸出張量。

3. 執行前向傳播過程得到預測值

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

• 通過 feed_dict 喂入測試資料，然後 run 輸出的張量我們就可以得到預測值。

4. 測試

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]]

• 本例輸入是一個三維網格模型處理後的 [41, 41, 41, 3] 的資料，輸出一個表面法向量座標 (x, y, z)。

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