[Tensorflow] 使用 tf.train.Checkpoint() 保存 / 加载 keras subclassed model

在 subclassed_model.py 中，通过对 tf.keras.Model 进行子类化，设计了两个自定义模型。

 import tensorflow as tf
 tf.enable_eager_execution()

 # parameters
 UNITS = 8

 class Encoder(tf.keras.Model):
     def __init__(self):
         super(Encoder, self).__init__()
         self.fc1 = tf.keras.layers.Dense(units=UNITS * 2, activation='relu')
         self.fc2 = tf.keras.layers.Dense(units=UNITS, activation='relu')

     def call(self, inputs):
         r = self.fc1(inputs)
         return self.fc2(r)

 class Decoder(tf.keras.Model):
     def __init__(self):
         super(Decoder, self).__init__()
         self.fc = tf.keras.layers.Dense(units=1)

     def call(self, inputs):
         return self.fc(inputs)

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在 save_subclassed_model.py 中，创建了 5000 组训练数据集，实例化 Encoder()、Decoder() 模型，优化器采用 tf.train.AdamOptimizer()，以均方误差作为 Loss 函数。训练过程中，每 5 个 epoch 保存一次模型。

 from subclassed_model import *

 import numpy as np
 import matplotlib.pyplot as plt
 import os

 import tensorflow as tf
 tf.enable_eager_execution()

 # create training data
 X = np.linspace(-1, 1, 5000)
 np.random.shuffle(X)
 y = X ** 3 + 1 + np.random.normal(0, 0.05, (5000,))

 # plot data
 plt.scatter(X, y)
 plt.show()

 # training dataset
 BATCH_SIZE = 16
 BUFFER_SIZE = 128

 training_dataset = tf.data.Dataset.from_tensor_slices((X, y)).batch(BATCH_SIZE).shuffle(BUFFER_SIZE)

 # initialize subclassed models
 encoder = Encoder()
 decoder = Decoder()

 optimizer = tf.train.AdamOptimizer()

 # loss function
 def loss_function(real, pred):
     return tf.losses.mean_squared_error(labels=real, predictions=pred)

 # training
 EPOCHS = 15

 # checkpoint
 checkpoint_dir = './training_checkpoints'
 checkpoint_prefix = os.path.join(checkpoint_dir, 'ckpt')
 checkpoint = tf.train.Checkpoint(optimizer=optimizer,
                                  encoder=encoder,
                                  decoder=decoder)
 if not os.path.exists(checkpoint_dir):
     os.makedirs(checkpoint_dir)

 for epoch in range(EPOCHS):
     epoch_loss = 0

     for (batch, (x, y)) in enumerate(training_dataset):
         x = tf.cast(x, tf.float32)
         y = tf.cast(y, tf.float32)
         x = tf.expand_dims(x, axis=1)   # tf.Tensor([...], shape=(16, 1), dtype=float32)
         y = tf.expand_dims(y, axis=1)   # tf.Tensor([...], shape=(16, 1), dtype=float32)

         with tf.GradientTape() as tape:
             y_ = encoder(x)
             prediction = decoder(y_)
             batch_loss = loss_function(real=y, pred=prediction)

         grads = tape.gradient(batch_loss, encoder.variables + decoder.variables)
         optimizer.apply_gradients(zip(grads, encoder.variables + decoder.variables),
                                   global_step=tf.train.get_or_create_global_step())

         epoch_loss += batch_loss

         if (batch + 1) % 100 == 0:
             print('Epoch {} Batch {} Loss {:.4f}'.format(epoch + 1,
                                                          batch + 1,
                                                          batch_loss.numpy()))

     print('Epoch {} Loss {:.4f}'.format(epoch + 1, epoch_loss / len(X)))

     if (epoch + 1) % 5 == 0:
         checkpoint.save(file_prefix=checkpoint_prefix)

运行 save_subclassed_model.py。

2019-06-27 12:57:14.253635: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2019-06-27 12:57:15.660142: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:
name: GeForce GTX 1060 major: 6 minor: 1 memoryClockRate(GHz): 1.6705
pciBusID: 0000:01:00.0
totalMemory: 6.00GiB freeMemory: 4.97GiB
2019-06-27 12:57:15.660397: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2019-06-27 12:57:16.488227: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-06-27 12:57:16.488385: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0
2019-06-27 12:57:16.488476: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N
2019-06-27 12:57:16.488772: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4722 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060, pci bus id: 0000:01:00.0, compute capability: 6.1)
Epoch 1 Batch 100 Loss 0.1120
Epoch 1 Batch 200 Loss 0.0179
Epoch 1 Batch 300 Loss 0.0347
Epoch 1 Loss 0.0111
Epoch 2 Batch 100 Loss 0.0144
Epoch 2 Batch 200 Loss 0.0097
Epoch 2 Batch 300 Loss 0.0141
Epoch 2 Loss 0.0012
Epoch 3 Batch 100 Loss 0.0060
Epoch 3 Batch 200 Loss 0.0037
Epoch 3 Batch 300 Loss 0.0054
Epoch 3 Loss 0.0007
Epoch 4 Batch 100 Loss 0.0088
Epoch 4 Batch 200 Loss 0.0038
Epoch 4 Batch 300 Loss 0.0093
Epoch 4 Loss 0.0004
Epoch 5 Batch 100 Loss 0.0039
Epoch 5 Batch 200 Loss 0.0044
Epoch 5 Batch 300 Loss 0.0031
Epoch 5 Loss 0.0003
Epoch 6 Batch 100 Loss 0.0025
Epoch 6 Batch 200 Loss 0.0038
Epoch 6 Batch 300 Loss 0.0027
Epoch 6 Loss 0.0002
Epoch 7 Batch 100 Loss 0.0026
Epoch 7 Batch 200 Loss 0.0032
Epoch 7 Batch 300 Loss 0.0041
Epoch 7 Loss 0.0002
Epoch 8 Batch 100 Loss 0.0022
Epoch 8 Batch 200 Loss 0.0031
Epoch 8 Batch 300 Loss 0.0026
Epoch 8 Loss 0.0002
Epoch 9 Batch 100 Loss 0.0040
Epoch 9 Batch 200 Loss 0.0014
Epoch 9 Batch 300 Loss 0.0040
Epoch 9 Loss 0.0002
Epoch 10 Batch 100 Loss 0.0023
Epoch 10 Batch 200 Loss 0.0030
Epoch 10 Batch 300 Loss 0.0038
Epoch 10 Loss 0.0002
Epoch 11 Batch 100 Loss 0.0028
Epoch 11 Batch 200 Loss 0.0020
Epoch 11 Batch 300 Loss 0.0025
Epoch 11 Loss 0.0002
Epoch 12 Batch 100 Loss 0.0027
Epoch 12 Batch 200 Loss 0.0045
Epoch 12 Batch 300 Loss 0.0021
Epoch 12 Loss 0.0002
Epoch 13 Batch 100 Loss 0.0016
Epoch 13 Batch 200 Loss 0.0033
Epoch 13 Batch 300 Loss 0.0024
Epoch 13 Loss 0.0002
Epoch 14 Batch 100 Loss 0.0034
Epoch 14 Batch 200 Loss 0.0028
Epoch 14 Batch 300 Loss 0.0033
Epoch 14 Loss 0.0002
Epoch 15 Batch 100 Loss 0.0019
Epoch 15 Batch 200 Loss 0.0030
Epoch 15 Batch 300 Loss 0.0037
Epoch 15 Loss 0.0002

Process finished with exit code 0

查看 checkpoint_dir 目录下的文件。

---

在 load_subclassed_model.py 中，创建了 200 组测试数据，加载了 the latest checkpoint 中保存的模型参数，对模型进行了测试。

 from subclassed_model import *

 import numpy as np
 import matplotlib.pyplot as plt

 import tensorflow as tf
 tf.enable_eager_execution()

 # load model
 encoder = Encoder()
 decoder = Decoder()
 optimizer = tf.train.AdamOptimizer()

 checkpoint_dir = './training_checkpoints'

 checkpoint = tf.train.Checkpoint(optimizer=optimizer,
                                  encoder=encoder,
                                  decoder=decoder)
 checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))

 # build model
 BATCH_SIZE = 16

 encoder.build(input_shape=tf.TensorShape((BATCH_SIZE, 1)))
 decoder.build(input_shape=tf.TensorShape((BATCH_SIZE, UNITS)))

 encoder.summary()
 decoder.summary()

 # create validation data
 X_test = np.linspace(-1, 1, 200)

 # validation dataset
 val_dataset = tf.data.Dataset.from_tensor_slices(X_test).batch(1)

 # predict and plot
 results = []
 for (batch, x) in enumerate(val_dataset):
     x = tf.cast(x, tf.float32)
     x = tf.expand_dims(x, axis=1)
     y_ = encoder(x)
     prediction = decoder(y_)
     # print(prediction.numpy()[0][0])
     results.append(prediction.numpy()[0][0])

 # plot results
 plt.scatter(X_test, results)
 plt.show()

运行 load_subclassed_model.py。

2019-06-27 13:27:40.712260: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2019-06-27 13:27:42.105938: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:
name: GeForce GTX 1060 major: 6 minor: 1 memoryClockRate(GHz): 1.6705
pciBusID: 0000:01:00.0
totalMemory: 6.00GiB freeMemory: 4.97GiB
2019-06-27 13:27:42.106200: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2019-06-27 13:27:42.921364: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-06-27 13:27:42.921510: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0
2019-06-27 13:27:42.921594: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N
2019-06-27 13:27:42.921777: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4722 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060, pci bus id: 0000:01:00.0, compute capability: 6.1)
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
dense (Dense)                multiple                  32
_________________________________________________________________
dense_1 (Dense)              multiple                  136
=================================================================
Total params: 168
Trainable params: 168
Non-trainable params: 0
_________________________________________________________________
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
dense_2 (Dense)              multiple                  9
=================================================================
Total params: 9
Trainable params: 9
Non-trainable params: 0
_________________________________________________________________

Process finished with exit code 0

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