在 Tensorflow 中训练模型后：
改编自文档
# ------------------------- # ----- Toy Context ----- # ------------------------- import tensorflow as tf class Net(tf.keras.Model): """A simple linear model.""" def __init__(self): super(Net, self).__init__() self.l1 = tf.keras.layers.Dense(5) def call(self, x): return self.l1(x) def toy_dataset(): inputs = tf.range(10.0)[:, None] labels = inputs * 5.0 + tf.range(5.0)[None, :] return ( tf.data.Dataset.from_tensor_slices(dict(x=inputs, y=labels)).repeat().batch(2) ) def train_step(net, example, optimizer): """Trains `net` on `example` using `optimizer`.""" with tf.GradientTape() as tape: output = net(example["x"]) loss = tf.reduce_mean(tf.abs(output - example["y"])) variables = net.trainable_variables gradients = tape.gradient(loss, variables) optimizer.apply_gradients(zip(gradients, variables)) return loss # ---------------------------- # ----- Create Objects ----- # ---------------------------- net = Net() opt = tf.keras.optimizers.Adam(0.1) dataset = toy_dataset() iterator = iter(dataset) ckpt = tf.train.Checkpoint( step=tf.Variable(1), optimizer=opt, net=net, iterator=iterator ) manager = tf.train.CheckpointManager(ckpt, "./tf_ckpts", max_to_keep=3) # ---------------------------- # ----- Train and Save ----- # ---------------------------- ckpt.restore(manager.latest_checkpoint) if manager.latest_checkpoint: print("Restored from {}".format(manager.latest_checkpoint)) else: print("Initializing from scratch.") for _ in range(50): example = next(iterator) loss = train_step(net, example, opt) ckpt.step.assign_add(1) if int(ckpt.step) % 10 == 0: save_path = manager.save() print("Saved checkpoint for step {}: {}".format(int(ckpt.step), save_path)) print("loss {:1.2f}".format(loss.numpy())) # --------------------- # ----- Restore ----- # --------------------- # In another script, re-initialize objects opt = tf.keras.optimizers.Adam(0.1) net = Net() dataset = toy_dataset() iterator = iter(dataset) ckpt = tf.train.Checkpoint( step=tf.Variable(1), optimizer=opt, net=net, iterator=iterator ) manager = tf.train.CheckpointManager(ckpt, "./tf_ckpts", max_to_keep=3) # Re-use the manager code above ^ ckpt.restore(manager.latest_checkpoint) if manager.latest_checkpoint: print("Restored from {}".format(manager.latest_checkpoint)) else: print("Initializing from scratch.") for _ in range(50): example = next(iterator) # Continue training or evaluate etc.
详尽而有用的教程 saved_model -> https://www.tensorflow.org/guide/saved_model
saved_model
keras 保存模型的详细指南-> https://www.tensorflow.org/guide/keras/save_and_serialize
keras
检查点捕获模型使用的所有参数（tf.Variable 对象）的确切值。 检查点不包含模型定义的计算的任何描述， 因此通常仅在将使用保存的参数值的源代码可用时才有用。 另一方面，SavedModel 格式除了参数值（检查点）之外，还 包括模型定义的计算的序列化描述。 这种格式的模型 独立 于创建模型的源代码。因此，它们适合通过 TensorFlow Serving、TensorFlow Lite、TensorFlow.js 或其他编程语言（C、C++、Java、Go、Rust、C# 等 TensorFlow API）的程序进行部署。
检查点捕获模型使用的所有参数（tf.Variable 对象）的确切值。 检查点不包含模型定义的计算的任何描述， 因此通常仅在将使用保存的参数值的源代码可用时才有用。
另一方面，SavedModel 格式除了参数值（检查点）之外，还 包括模型定义的计算的序列化描述。 这种格式的模型 独立 于创建模型的源代码。因此，它们适合通过 TensorFlow Serving、TensorFlow Lite、TensorFlow.js 或其他编程语言（C、C++、Java、Go、Rust、C# 等 TensorFlow API）的程序进行部署。
（重点是我自己的）
从文档：
# Create some variables. v1 = tf.get_variable("v1", shape=[3], initializer = tf.zeros_initializer) v2 = tf.get_variable("v2", shape=[5], initializer = tf.zeros_initializer) inc_v1 = v1.assign(v1+1) dec_v2 = v2.assign(v2-1) # Add an op to initialize the variables. init_op = tf.global_variables_initializer() # Add ops to save and restore all the variables. saver = tf.train.Saver() # Later, launch the model, initialize the variables, do some work, and save the # variables to disk. with tf.Session() as sess: sess.run(init_op) # Do some work with the model. inc_v1.op.run() dec_v2.op.run() # Save the variables to disk. save_path = saver.save(sess, "/tmp/model.ckpt") print("Model saved in path: %s" % save_path)
tf.reset_default_graph() # Create some variables. v1 = tf.get_variable("v1", shape=[3]) v2 = tf.get_variable("v2", shape=[5]) # Add ops to save and restore all the variables. saver = tf.train.Saver() # Later, launch the model, use the saver to restore variables from disk, and # do some work with the model. with tf.Session() as sess: # Restore variables from disk. saver.restore(sess, "/tmp/model.ckpt") print("Model restored.") # Check the values of the variables print("v1 : %s" % v1.eval()) print("v2 : %s" % v2.eval())
simple_save
很多好的答案，为了完整起见，我将添加我的 2 美分： simple_save 。也是使用tf.data.DatasetAPI 的独立代码示例。
tf.data.Dataset
蟒蛇3； 张量流* 1.14 *
import tensorflow as tf from tensorflow.saved_model import tag_constants with tf.Graph().as_default(): with tf.Session() as sess: ... # Saving inputs = { "batch_size_placeholder": batch_size_placeholder, "features_placeholder": features_placeholder, "labels_placeholder": labels_placeholder, } outputs = {"prediction": model_output} tf.saved_model.simple_save( sess, 'path/to/your/location/', inputs, outputs )
恢复：
graph = tf.Graph() with restored_graph.as_default(): with tf.Session() as sess: tf.saved_model.loader.load( sess, [tag_constants.SERVING], 'path/to/your/location/', ) batch_size_placeholder = graph.get_tensor_by_name('batch_size_placeholder:0') features_placeholder = graph.get_tensor_by_name('features_placeholder:0') labels_placeholder = graph.get_tensor_by_name('labels_placeholder:0') prediction = restored_graph.get_tensor_by_name('dense/BiasAdd:0') sess.run(prediction, feed_dict={ batch_size_placeholder: some_value, features_placeholder: some_other_value, labels_placeholder: another_value })
原创博文
为了演示，以下代码生成随机数据。
Dataset
Iterator
input_tensor
softmax_cross_entropy_with_logits
Adam
tf.saved_model.simple_save
simple/
tf.saved_model.loader.load
graph.get_tensor_by_name
graph.get_operation_by_name
代码：
import os import shutil import numpy as np import tensorflow as tf from tensorflow.python.saved_model import tag_constants def model(graph, input_tensor): """Create the model which consists of a bidirectional rnn (GRU(10)) followed by a dense classifier Args: graph (tf.Graph): Tensors' graph input_tensor (tf.Tensor): Tensor fed as input to the model Returns: tf.Tensor: the model's output layer Tensor """ cell = tf.nn.rnn_cell.GRUCell(10) with graph.as_default(): ((fw_outputs, bw_outputs), (fw_state, bw_state)) = tf.nn.bidirectional_dynamic_rnn( cell_fw=cell, cell_bw=cell, inputs=input_tensor, sequence_length=[10] * 32, dtype=tf.float32, swap_memory=True, scope=None) outputs = tf.concat((fw_outputs, bw_outputs), 2) mean = tf.reduce_mean(outputs, axis=1) dense = tf.layers.dense(mean, 5, activation=None) return dense def get_opt_op(graph, logits, labels_tensor): """Create optimization operation from model's logits and labels Args: graph (tf.Graph): Tensors' graph logits (tf.Tensor): The model's output without activation labels_tensor (tf.Tensor): Target labels Returns: tf.Operation: the operation performing a stem of Adam optimizer """ with graph.as_default(): with tf.variable_scope('loss'): loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( logits=logits, labels=labels_tensor, name='xent'), name="mean-xent" ) with tf.variable_scope('optimizer'): opt_op = tf.train.AdamOptimizer(1e-2).minimize(loss) return opt_op if __name__ == '__main__': # Set random seed for reproducibility # and create synthetic data np.random.seed(0) features = np.random.randn(64, 10, 30) labels = np.eye(5)[np.random.randint(0, 5, (64,))] graph1 = tf.Graph() with graph1.as_default(): # Random seed for reproducibility tf.set_random_seed(0) # Placeholders batch_size_ph = tf.placeholder(tf.int64, name='batch_size_ph') features_data_ph = tf.placeholder(tf.float32, [None, None, 30], 'features_data_ph') labels_data_ph = tf.placeholder(tf.int32, [None, 5], 'labels_data_ph') # Dataset dataset = tf.data.Dataset.from_tensor_slices((features_data_ph, labels_data_ph)) dataset = dataset.batch(batch_size_ph) iterator = tf.data.Iterator.from_structure(dataset.output_types, dataset.output_shapes) dataset_init_op = iterator.make_initializer(dataset, name='dataset_init') input_tensor, labels_tensor = iterator.get_next() # Model logits = model(graph1, input_tensor) # Optimization opt_op = get_opt_op(graph1, logits, labels_tensor) with tf.Session(graph=graph1) as sess: # Initialize variables tf.global_variables_initializer().run(session=sess) for epoch in range(3): batch = 0 # Initialize dataset (could feed epochs in Dataset.repeat(epochs)) sess.run( dataset_init_op, feed_dict={ features_data_ph: features, labels_data_ph: labels, batch_size_ph: 32 }) values = [] while True: try: if epoch < 2: # Training _, value = sess.run([opt_op, logits]) print('Epoch {}, batch {} | Sample value: {}'.format(epoch, batch, value[0])) batch += 1 else: # Final inference values.append(sess.run(logits)) print('Epoch {}, batch {} | Final inference | Sample value: {}'.format(epoch, batch, values[-1][0])) batch += 1 except tf.errors.OutOfRangeError: break # Save model state print('\nSaving...') cwd = os.getcwd() path = os.path.join(cwd, 'simple') shutil.rmtree(path, ignore_errors=True) inputs_dict = { "batch_size_ph": batch_size_ph, "features_data_ph": features_data_ph, "labels_data_ph": labels_data_ph } outputs_dict = { "logits": logits } tf.saved_model.simple_save( sess, path, inputs_dict, outputs_dict ) print('Ok') # Restoring graph2 = tf.Graph() with graph2.as_default(): with tf.Session(graph=graph2) as sess: # Restore saved values print('\nRestoring...') tf.saved_model.loader.load( sess, [tag_constants.SERVING], path ) print('Ok') # Get restored placeholders labels_data_ph = graph2.get_tensor_by_name('labels_data_ph:0') features_data_ph = graph2.get_tensor_by_name('features_data_ph:0') batch_size_ph = graph2.get_tensor_by_name('batch_size_ph:0') # Get restored model output restored_logits = graph2.get_tensor_by_name('dense/BiasAdd:0') # Get dataset initializing operation dataset_init_op = graph2.get_operation_by_name('dataset_init') # Initialize restored dataset sess.run( dataset_init_op, feed_dict={ features_data_ph: features, labels_data_ph: labels, batch_size_ph: 32 } ) # Compute inference for both batches in dataset restored_values = [] for i in range(2): restored_values.append(sess.run(restored_logits)) print('Restored values: ', restored_values[i][0]) # Check if original inference and restored inference are equal valid = all((v == rv).all() for v, rv in zip(values, restored_values)) print('\nInferences match: ', valid)
这将打印：
$ python3 save_and_restore.py Epoch 0, batch 0 | Sample value: [-0.13851789 -0.3087595 0.12804556 0.20013677 -0.08229901] Epoch 0, batch 1 | Sample value: [-0.00555491 -0.04339041 -0.05111827 -0.2480045 -0.00107776] Epoch 1, batch 0 | Sample value: [-0.19321944 -0.2104792 -0.00602257 0.07465433 0.11674127] Epoch 1, batch 1 | Sample value: [-0.05275984 0.05981954 -0.15913513 -0.3244143 0.10673307] Epoch 2, batch 0 | Final inference | Sample value: [-0.26331693 -0.13013336 -0.12553 -0.04276478 0.2933622 ] Epoch 2, batch 1 | Final inference | Sample value: [-0.07730117 0.11119192 -0.20817074 -0.35660955 0.16990358] Saving... INFO:tensorflow:Assets added to graph. INFO:tensorflow:No assets to write. INFO:tensorflow:SavedModel written to: b'/some/path/simple/saved_model.pb' Ok Restoring... INFO:tensorflow:Restoring parameters from b'/some/path/simple/variables/variables' Ok Restored values: [-0.26331693 -0.13013336 -0.12553 -0.04276478 0.2933622 ] Restored values: [-0.07730117 0.11119192 -0.20817074 -0.35660955 0.16990358] Inferences match: True
