TensorFlow2.0基本操作（五）
4 自定义网络
keras.Sequential(容器)：model.trainable_variables所有的权重参数，会自动调用model.call()
keras.layers.Layer（继承制）
keras.Model:母类，inherit:1，实现初始化参数，2，
5 模型保存与加载
1）仅保存训练参数
import tensorflow as tf
from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics
def preprocess(x, y):
"""
x is a simple image, not a batch
"""
x = tf.cast(x, dtype=tf.float32) / 255.
x = tf.reshape(x, [28*28])
y = tf.cast(y, dtype=tf.int32)
y = tf.one_hot(y, depth=10)
return x,y
batchsz = 128
(x, y), (x_val, y_val) = datasets.mnist.load_data()
print('datasets:', x.shape, y.shape, x.min(), x.max())
db = tf.data.Dataset.from_tensor_slices((x,y))
db = db.map(preprocess).shuffle(60000).batch(batchsz)
ds_val = tf.data.Dataset.from_tensor_slices((x_val, y_val))
ds_val = ds_val.map(preprocess).batch(batchsz)
sample = next(iter(db))
print(sample[0].shape, sample[1].shape)
network = Sequential([layers.Dense(256, activation='relu'),
layers.Dense(128, activation='relu'),
layers.Dense(64, activation='relu'),
layers.Dense(32, activation='relu'),
layers.Dense(10)])
network.build(input_shape=(None, 28*28))
network.summary()
network.compile(optimizer=optimizers.Adam(lr=0.01),
loss=tf.losses.CategoricalCrossentropy(from_logits=True),
metrics=['accuracy']
)
# 模型训练
network.fit(db, epochs=3, validation_data=ds_val, validation_freq=2)
# 模型测试
network.evaluate(ds_val)
#保存参数
network.save_weights('weights.ckpt')
print('saved weights.')
# 删除网络
del network
# 重新搭建完全一样的网络
network = Sequential([layers.Dense(256, activation='relu'),
layers.Dense(128, activation='relu'),
layers.Dense(64, activation='relu'),
layers.Dense(32, activation='relu'),
layers.Dense(10)])
# 设置前向传播参数
network.compile(optimizer=optimizers.Adam(lr=0.01),
loss=tf.losses.CategoricalCrossentropy(from_logits=True),
metrics=['accuracy']
)
# 加载保存好的参数
network.load_weights('weights.ckpt')
print('loaded weights!')
# 模型评估
network.evaluate(ds_val)
# 备注：最后训练的结果不完全一样，是因为，影响最终结果的不仅仅是这些保存的参数，还有一些其他的因素。
# 如果想要 完全一样，那么就要使用另外的一种保存方式。
datasets: (60000, 28, 28) (60000,) 0 255
(128, 784) (128, 10)
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) multiple 200960
_________________________________________________________________
dense_1 (Dense) multiple 32896
_________________________________________________________________
dense_2 (Dense) multiple 8256
_________________________________________________________________
dense_3 (Dense) multiple 2080
_________________________________________________________________
dense_4 (Dense) multiple 330
=================================================================
Total params: 244,522
Trainable params: 244,522
Non-trainable params: 0
_________________________________________________________________
Epoch 1/3
469/469 [==============================] - 5s 12ms/step - loss: 0.2728 - accuracy: 0.8466
Epoch 2/3
469/469 [==============================] - 5s 11ms/step - loss: 0.1394 - accuracy: 0.9589 - val_loss: 0.2163 - val_accuracy: 0.9512
Epoch 3/3
469/469 [==============================] - 4s 10ms/step - loss: 0.1178 - accuracy: 0.9649
79/79 [==============================] - 0s 5ms/step - loss: 0.1110 - accuracy: 0.9674
saved weights.
loaded weights!
79/79 [==============================] - 1s 10ms/step - loss: 0.1110 - accuracy: 0.9634
[0.1110091062153607, 0.9674]
2） 保存整个网络
import tensorflow as tf
from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics
def preprocess(x, y):
"""
x is a simple image, not a batch
"""
x = tf.cast(x, dtype=tf.float32) / 255.
x = tf.reshape(x, [28*28])
y = tf.cast(y, dtype=tf.int32)
y = tf.one_hot(y, depth=10)
return x,y
batchsz = 128
(x, y), (x_val, y_val) = datasets.mnist.load_data()
print('datasets:', x.shape, y.shape, x.min(), x.max())
db = tf.data.Dataset.from_tensor_slices((x,y))
db = db.map(preprocess).shuffle(60000).batch(batchsz)
ds_val = tf.data.Dataset.from_tensor_slices((x_val, y_val))
ds_val = ds_val.map(preprocess).batch(batchsz)
sample = next(iter(db))
print(sample[0].shape, sample[1].shape)
network = Sequential([layers.Dense(256, activation='relu'),
layers.Dense(128, activation='relu'),
layers.Dense(64, activation='relu'),
layers.Dense(32, activation='relu'),
layers.Dense(10)])
network.build(input_shape=(None, 28*28))
network.summary()
network.compile(optimizer=optimizers.Adam(lr=0.01),
loss=tf.losses.CategoricalCrossentropy(from_logits=True),
metrics=['accuracy']
)
network.fit(db, epochs=3, validation_data=ds_val, validation_freq=2)
# 创建完以后做一个evaluate
network.evaluate(ds_val)
# 接下来进行保存，保存整个model
network.save('model.h5')
print('saved total model.')
del network
print('loaded model from file.')
network = tf.keras.models.load_model('model.h5', compile=False) # 这里就没有Sequential()这个方法了，直接从文件中恢复这个模型
network.compile(optimizer=optimizers.Adam(lr=0.01),
loss=tf.losses.CategoricalCrossentropy(from_logits=True),
metrics=['accuracy']
)
x_val = tf.cast(x_val, dtype=tf.float32) / 255.
x_val = tf.reshape(x_val, [-1, 28*28])
y_val = tf.cast(y_val, dtype=tf.int32)
y_val = tf.one_hot(y_val, depth=10)
ds_val = tf.data.Dataset.from_tensor_slices((x_val, y_val)).batch(128)
network.evaluate(ds_val) # 最后再做一个evaluate()
datasets: (60000, 28, 28) (60000,) 0 255
(128, 784) (128, 10)
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) multiple 200960
_________________________________________________________________
dense_1 (Dense) multiple 32896
_________________________________________________________________
dense_2 (Dense) multiple 8256
_________________________________________________________________
dense_3 (Dense) multiple 2080
_________________________________________________________________
dense_4 (Dense) multiple 330
=================================================================
Total params: 244,522
Trainable params: 244,522
Non-trainable params: 0
_________________________________________________________________
Epoch 1/3
469/469 [==============================] - 5s 12ms/step - loss: 0.2801 - accuracy: 0.8401
Epoch 2/3
469/469 [==============================] - 5s 11ms/step - loss: 0.1357 - accuracy: 0.9582 - val_loss: 0.1496 - val_accuracy: 0.9562
Epoch 3/3
469/469 [==============================] - 4s 10ms/step - loss: 0.1084 - accuracy: 0.9689
79/79 [==============================] - 0s 6ms/step - loss: 0.1324 - accuracy: 0.9636
saved total model.
loaded model from file.
79/79 [==============================] - 0s 6ms/step - loss: 0.1324 - accuracy: 0.9584
[0.13236246153615014, 0.9636]
3） 第三种保存的方式，主要用于模型在工业的部署，这种方法更加的通用，可以供其它语言进行使用
5 Kera实战
CIFAR10自定义网络实战-1
32*32
要使用到一个自定义的网络层：My Dense Layer
import tensorflow as tf
# datasets数据集的管理, layers, optimizers优化器,sequential容器, metrics测试的度量器
from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics
from tensorflow import keras
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
def preprocess(x, y):
# [0~255] => [-1~1]
x = 2 * tf.cast(x, dtype=tf.float32) / 255. - 1.
y = tf.cast(y, dtype=tf.int32)
return x, y
batchsz = 128
# [50k, 32, 32, 3], [10k, 1]
(x, y), (x_val, y_val) = datasets.cifar10.load_data()
y = tf.squeeze(y) # 压缩掉张量里面的一维
y_val = tf.squeeze(y_val)
y = tf.one_hot(y, depth=10) # [50k, 10]
y_val = tf.one_hot(y_val, depth=10) # [10k, 10]
print('datasets:', x.shape, y.shape, x_val.shape, y_val.shape, x.min(), x.max())
train_db = tf.data.Dataset.from_tensor_slices((x, y))
train_db = train_db.map(preprocess).shuffle(10000).batch(batchsz)
test_db = tf.data.Dataset.from_tensor_slices((x_val, y_val))
test_db = test_db.map(preprocess).batch(batchsz)
# 这里生成一个sample，查看下它的shape
sample = next(iter(train_db))
print('batch:', sample[0].shape, sample[1].shape)
# 接下来新建一个这样的网络对象。
# （1）实现一个自定义的层
class MyDense(layers.Layer): # 因为我们要自定义一个网络结构，因此我们要新建一个类，这个类要继承自layers.Layer这样一个母类
# to replace standard layers.Dense()
# 同样的我们要继承两个函数，自定义以下两个函数:
# 1) 初始化的一个函数
def __init__(self, inp_dim, outp_dim): # 输入的维度，和输出的维度
super(MyDense, self).__init__()
# 新建一个kernel（核函数）
self.kernel = self.add_variable('w', [inp_dim, outp_dim]) # kernel 等于这样的一个shape
# self.bias = self.add_variable('b', [outp_dim])
# 2） 前向逻辑的函数
def __call__(self, inputs, training=None):
x = inputs @ self.kernel # 此处没有加bias
return x
# （2） 实现一个自定义的网络
class MyNetwork(keras.Model):
# 同样的道理，他也需要实现两个函数
# 1）
def __init__(self):
super(MyNetwork, self).__init__()
# 这里新建5层
self.fc1 = MyDense(32*32*3, 256)
self.fc2 = MyDense(256, 128)
self.fc3 = MyDense(128, 64)
self.fc4 = MyDense(64, 32)
self.fc5 = MyDense(32, 10)
# 2) 实现一个前向传播的逻辑
def call(self,inputs, training=None):
'''
:param inputs:[b,32,32,3]
:param training:
:return:
'''
x = tf.reshape(inputs, [-1, 32*32*3])
# [b, 32*32*3] => [b, 256]
x = self.fc1(x)
x = tf.nn.relu(x)
# [b, 256] => [b, 128]
x = self.fc2(x)
x = tf.nn.relu(x)
# [b, 128] => [b, 64]
x = self.fc3(x)
x = tf.nn.relu(x)
# [b, 64] => [b, 32]
x = self.fc4(x)
x = tf.nn.relu(x)
# [b, 32] => [b, 10]
x = self.fc5(x)
return x
# 接下来我们就将网络和loss装配起来就可以了。
network = MyNetwork()
network.compile(optimizer=optimizers.Adam(lr=1e-3), # 开始装配
loss=tf.losses.CategoricalCrossentropy(from_logits=True), # 这里为了追求一个数据稳定性，一般都使用from_logits=True
metrics=['accuracy'])
network.fit(train_db, epochs=15, validation_data=test_db, validation_freq=1) # 做测试，频率为1
# 一般我们会选择保存一个参数，而不是整个网络，因为这样是一个轻量级的保存方式
network.evaluate(test_db) # 首先evaluate()来评估一下模型
network.save_weights('ckpt/weights.ckpt') # 后缀名自己随便起，因为这个是他自己定义的，和我们怎么定义没有关系
del network # 把这个网络删除掉
print('saved to ckpt/weights.ckpt')
# 到此为止模型已经保存起来了，保存起来以后我们需要额外的再创建一下
# 再创建模型的时候，因为以上只是单纯的保存了权值，因此还是需要以下步骤：
# 1）我们把网络创建的这一部分加载进来
network = MyNetwork()
network.compile(optimizer=optimizers.Adam(lr=1e-3), # 开始装配
loss=tf.losses.CategoricalCrossentropy(from_logits=True), # 这里为了追求一个数据稳定性，一般都使用from_logits=True
metrics=['accuracy'])
# 2）我们把权值给更新
network.load_weights('ckpt/weights.ckpt')
print('loaded weights from file.')
network.evaluate(test_db) # 做一下模型测试，对比之前和之后来看一下是否有变化
# 基本上对于cifar10来讲，如果不使用卷积神经网络或者深层次的RsNet的话，就很难达到70%的准确率
# 因为只保存了权值，因此最后的结果并不是完全一样，比如随机种子没有保存等等，但是差别不大
File "", line 78
:param inputs:[b,32,32,3]
^
IndentationError: unexpected indent
'''
# 本人此处是用pycharm运行的，copy到了这里，notebook同样可以运行
F:\Anaconda3\envs\gpu\python.exe H:/lesson13/keras_train.py
datasets: (50000, 32, 32, 3) (50000, 10) (10000, 32, 32, 3) (10000, 10) 0 255
batch: (128, 32, 32, 3) (128, 10)
Epoch 1/15
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391/391 [==============================] - 6s 15ms/step - loss: 1.7180 - accuracy: 0.3428 - val_loss: 1.5808 - val_accuracy: 0.4397
Epoch 2/15
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79/79 [==============================] - 1s 7ms/step - loss: 2.0116 - accuracy: 0.4835
saved to ckpt/weights.ckpt
loaded weights from file.
1/79 [..............................] - ETA: 21s - loss: 1.7383 - accuracy: 0.4766
9/79 [==>...........................] - ETA: 2s - loss: 1.9050 - accuracy: 0.4498
16/79 [=====>........................] - ETA: 1s - loss: 1.9337 - accuracy: 0.4591
24/79 [========>.....................] - ETA: 0s - loss: 1.9727 - accuracy: 0.4660
32/79 [===========>..................] - ETA: 0s - loss: 1.9818 - accuracy: 0.4690
40/79 [==============>...............] - ETA: 0s - loss: 1.9753 - accuracy: 0.4724
46/79 [================>.............] - ETA: 0s - loss: 1.9714 - accuracy: 0.4742
52/79 [==================>...........] - ETA: 0s - loss: 1.9880 - accuracy: 0.4757
60/79 [=====================>........] - ETA: 0s - loss: 2.0014 - accuracy: 0.4770
68/79 [========================>.....] - ETA: 0s - loss: 2.0076 - accuracy: 0.4778
76/79 [===========================>..] - ETA: 0s - loss: 2.0038 - accuracy: 0.4785
79/79 [==============================] - 1s 11ms/step - loss: 2.0116 - accuracy: 0.4787
Process finished with exit code 0
'''
```
这里就是目录下保存的文件
```python
```