保存 TF 服務的模型 · 精通 TensorFlow 1.x

# 保存 TF 服務的模型為了服務模型，需要先保存它們。在本節中，我們將從官方 TensorFlow 文檔中演示 MNIST 示例的略微修改版本，可從以下鏈接獲得： [https://www.tensorflow.org/serving/serving_basic](https://www.tensorflow.org/serving/serving_basic) 。 TensorFlow 團隊建議使用 SavedModel 來保存和恢復在 TensorFlow 中構建和訓練的模型。根據 TensorFlow 文檔： SavedModel 是一種語言中立的，可恢復的，密集的序列化格式。SavedModel 支持更高級別的系統和工具來生成，使用和轉換 TensorFlow 模型。您可以按照 Jupyter 筆記本中的代碼`ch-11b_Saving_TF_Models_with_SavedModel_for_TF_Serving`。我們按照以下方式繼續保存模型： 1. 定義模型變量： ```py model_name = 'mnist' model_version = '1' model_dir = os.path.join(models_root,model_name,model_version) ``` 1. 像我們在第 4 章中所做的那樣獲取 MNIST 數據 - MLP 模型： ```py from tensorflow.examples.tutorials.mnist import input_data dataset_home = os.path.join('.','mnist') mnist = input_data.read_data_sets(dataset_home, one_hot=True) x_train = mnist.train.images x_test = mnist.test.images y_train = mnist.train.labels y_test = mnist.test.labels pixel_size = 28 num_outputs = 10 # 0-9 digits num_inputs = 784 # total pixels ``` 1. 定義將構建并返回模型的 MLP 函數： ```py def mlp(x, num_inputs, num_outputs,num_layers,num_neurons): w=[] b=[] for i in range(num_layers): w.append(tf.Variable(tf.random_normal( [num_inputs if i==0 else num_neurons[i-1], num_neurons[i]]),name="w_{0:04d}".format(i) ) ) b.append(tf.Variable(tf.random_normal( [num_neurons[i]]), name="b_{0:04d}".format(i) ) ) w.append(tf.Variable(tf.random_normal( [num_neurons[num_layers-1] if num_layers > 0 \ else num_inputs, num_outputs]),name="w_out")) b.append(tf.Variable(tf.random_normal([num_outputs]), name="b_out")) # x is input layer layer = x # add hidden layers for i in range(num_layers): layer = tf.nn.relu(tf.matmul(layer, w[i]) + b[i]) # add output layer layer = tf.matmul(layer, w[num_layers]) + b[num_layers] model = layer probs = tf.nn.softmax(model) return model,probs ``` 上述`mlp()`函數返回模型和概率。概率是應用于模型的 softmax 激活。 1. 為圖像輸入和目標輸出定義`x_p`和`y_p`占位符： ```py # input images serialized_tf_example = tf.placeholder(tf.string, name='tf_example') feature_configs = {'x': tf.FixedLenFeature(shape=[784], dtype=tf.float32),} tf_example = tf.parse_example(serialized_tf_example, feature_configs) # use tf.identity() to assign name x_p = tf.identity(tf_example['x'], name='x_p') # target output y_p = tf.placeholder(dtype=tf.float32, name="y_p", shape=[None, num_outputs]) ``` 1. 創建模型，以及損失，優化器，準確性和訓練函數： ```py num_layers = 2 num_neurons = [] for i in range(num_layers): num_neurons.append(256) learning_rate = 0.01 n_epochs = 50 batch_size = 100 n_batches = mnist.train.num_examples//batch_size model,probs = mlp(x=x_p, num_inputs=num_inputs, num_outputs=num_outputs, num_layers=num_layers, num_neurons=num_neurons) loss_op = tf.nn.softmax_cross_entropy_with_logits loss = tf.reduce_mean(loss_op(logits=model, labels=y_p)) optimizer = tf.train.GradientDescentOptimizer(learning_rate) train_op = optimizer.minimize(loss) pred_check = tf.equal(tf.argmax(probs,1), tf.argmax(y_p,1)) accuracy_op = tf.reduce_mean(tf.cast(pred_check, tf.float32)) values, indices = tf.nn.top_k(probs, 10) table = tf.contrib.lookup.index_to_string_table_from_tensor( tf.constant([str(i) for i in range(10)])) prediction_classes = table.lookup(tf.to_int64(indices)) ``` 1. 在 TensorFlow 會話中，像以前一樣訓練模型，但使用構建器對象來保存模型： ```py from tf.saved_model.signature_constants import \ CLASSIFY_INPUTS from tf.saved_model.signature_constants import \ CLASSIFY_OUTPUT_CLASSES from tf.saved_model.signature_constants import \ CLASSIFY_OUTPUT_SCORES from tf.saved_model.signature_constants import \ CLASSIFY_METHOD_NAME from tf.saved_model.signature_constants import \ PREDICT_METHOD_NAME from tf.saved_model.signature_constants import \ DEFAULT_SERVING_SIGNATURE_DEF_KEY ``` ```py with tf.Session() as tfs: tfs.run(tf.global_variables_initializer()) for epoch in range(n_epochs): epoch_loss = 0.0 for batch in range(n_batches): x_batch, y_batch = mnist.train.next_batch(batch_size) feed_dict = {x_p: x_batch, y_p: y_batch} _,batch_loss = tfs.run([train_op,loss], feed_dict=feed_dict) epoch_loss += batch_loss average_loss = epoch_loss / n_batches print("epoch: {0:04d} loss = {1:0.6f}" .format(epoch,average_loss)) feed_dict={x_p: x_test, y_p: y_test} accuracy_score = tfs.run(accuracy_op, feed_dict=feed_dict) print("accuracy={0:.8f}".format(accuracy_score)) # save the model # definitions for saving the models builder = tf.saved_model.builder.SavedModelBuilder(model_dir) # build signature_def_map bti_op = tf.saved_model.utils.build_tensor_info bsd_op = tf.saved_model.utils.build_signature_def classification_inputs = bti_op(serialized_tf_example) classification_outputs_classes = bti_op(prediction_classes) classification_outputs_scores = bti_op(values) classification_signature = (bsd_op( inputs={CLASSIFY_INPUTS: classification_inputs}, outputs={CLASSIFY_OUTPUT_CLASSES: classification_outputs_classes, CLASSIFY_OUTPUT_SCORES: classification_outputs_scores }, method_name=CLASSIFY_METHOD_NAME)) tensor_info_x = bti_op(x_p) tensor_info_y = bti_op(probs) prediction_signature = (bsd_op( inputs={'inputs': tensor_info_x}, outputs={'outputs': tensor_info_y}, method_name=PREDICT_METHOD_NAME)) legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op') builder.add_meta_graph_and_variables( tfs, [tf.saved_model.tag_constants.SERVING], signature_def_map={ 'predict_images':prediction_signature, DEFAULT_SERVING_SIGNATURE_DEF_KEY: classification_signature, }, legacy_init_op=legacy_init_op) builder.save() ``` 一旦看到以下輸出，就會保存模型： ```py accuracy=0.92979997 INFO:tensorflow:No assets to save. INFO:tensorflow:No assets to write. INFO:tensorflow:SavedModel written to: b'/home/armando/models/mnist/1/saved_model.pb' ``` 接下來，我們運行 ModelServer 并提供剛剛保存的模型。