# 深度学习TF—5.tf.kears高层API 原创

1147-柳同学

### 一、metrics

• 新建一个评价指标
acc_meter = metrics.Accuracy()
loss_meter = metrics.Mean()

• update data- 添加数据
loss_meter.update_state(loss)
acc_meter.update_state(y,pred)

• result().numpy()-显示结果
print(step,'loss:', loss_meter.result().numpy())
...
print(step,'Evaluate Acc:',total_correct/total, acc_meter.result().numpy())

• reset_states()-清零
if step % 100 == 0:
print(step,'loss:', loss_meter.result().numpy())
# 清除上一个时间戳的数据
loss_meter.reset_states()

if step % 500 == 0:
print(step,'Evaluate Acc:',total_correct/total, acc_meter.result().numpy())
acc_meter.reset_states()


#### 1.实战

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

import tensorflow as tf
from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics

# 预处理函数
def preprocess(x, y):
x = tf.cast(x, dtype=tf.float32) / 255.
y = tf.cast(y, dtype=tf.int32)

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).repeat(10)

ds_val = tf.data.Dataset.from_tensor_slices((x_val, y_val))
ds_val = ds_val.map(preprocess).batch(batchsz)

# 构建多层网络
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)])
network.build(input_shape=(None, 28 * 28))
network.summary()

# 优化器

# 评价指标-acc,loss
acc_meter = metrics.Accuracy()
loss_meter = metrics.Mean()

for step, (x, y) in enumerate(db):

# [b, 28, 28] => [b, 784]
x = tf.reshape(x, (-1, 28 * 28))
# [b, 784] => [b, 10]
out = network(x)
# [b] => [b, 10]
y_onehot = tf.one_hot(y, depth=10)
# [b]
loss = tf.reduce_mean(tf.losses.categorical_crossentropy(y_onehot, out, from_logits=True))

loss_meter.update_state(loss)

if step % 100 == 0:
print(step, 'loss:', loss_meter.result().numpy())
loss_meter.reset_states()

# evaluate
if step % 500 == 0:
total, total_correct = 0., 0
acc_meter.reset_states()

for step, (x, y) in enumerate(ds_val):
# [b, 28, 28] => [b, 784]
x = tf.reshape(x, (-1, 28 * 28))
# [b, 784] => [b, 10]
out = network(x)

# [b, 10] => [b]
pred = tf.argmax(out, axis=1)
pred = tf.cast(pred, dtype=tf.int32)
# bool type
correct = tf.equal(pred, y)
# bool tensor => int tensor => numpy
total_correct += tf.reduce_sum(tf.cast(correct, dtype=tf.int32)).numpy()
total += x.shape[0]

acc_meter.update_state(y, pred)

print(step, 'Evaluate Acc:', total_correct / total, acc_meter.result().numpy())

datasets: (60000, 28, 28) (60000,) 0 255
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
_________________________________________________________________
0 loss: 2.3095727
78 Evaluate Acc: 0.1032 0.1032
100 loss: 0.49836162
200 loss: 0.24281283
300 loss: 0.20814449
400 loss: 0.19040857
500 loss: 0.1471103
78 Evaluate Acc: 0.956 0.956
600 loss: 0.15806517
700 loss: 0.13501912
800 loss: 0.13778095
900 loss: 0.13771541
1000 loss: 0.11204889
78 Evaluate Acc: 0.9666 0.9666
1100 loss: 0.10818114
1200 loss: 0.10698662
1300 loss: 0.10993517
1400 loss: 0.10309881
1500 loss: 0.092004016
78 Evaluate Acc: 0.9658 0.9658
1600 loss: 0.09988546
1700 loss: 0.09517718
1800 loss: 0.102653
1900 loss: 0.10128655
2000 loss: 0.084593534
78 Evaluate Acc: 0.9696 0.9696
2100 loss: 0.089395694
2200 loss: 0.084114745
2300 loss: 0.08294669
2400 loss: 0.0765419
2500 loss: 0.07786285
78 Evaluate Acc: 0.9716 0.9716
2600 loss: 0.08739958
2700 loss: 0.08950595
2800 loss: 0.08106578
2900 loss: 0.06466477
3000 loss: 0.077431396
78 Evaluate Acc: 0.9707 0.9707
3100 loss: 0.08382876
3200 loss: 0.076059125
3300 loss: 0.07230227
3400 loss: 0.05853687
3500 loss: 0.07312769
78 Evaluate Acc: 0.9703 0.9703
3600 loss: 0.07384481
3700 loss: 0.08926408
3800 loss: 0.066682965
3900 loss: 0.05534654
4000 loss: 0.073996484
78 Evaluate Acc: 0.9741 0.9741
4100 loss: 0.066883035
4200 loss: 0.070191
4300 loss: 0.08581101
4400 loss: 0.07324687
4500 loss: 0.056211904
78 Evaluate Acc: 0.9751 0.9751
4600 loss: 0.05384313


### 二、compile&fit&Evaluate&Predict

#### 1.compile—编译模型

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets,optimizers,losses,metrics,layers,Sequential

# 数据预处理
def preprocess(x, y):
x = tf.cast(x, dtype=tf.float32) / 255.
y = tf.cast(y, dtype=tf.int32)

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())
x = x.reshape((-1,28*28))
x_val = x_val.reshape((-1,28*28))
# 数据集加载并预处理
db = tf.data.Dataset.from_tensor_slices((x, y))
db = db.map(preprocess).shuffle(60000).batch(batchsz).repeat(10)

ds_val = tf.data.Dataset.from_tensor_slices((x_val, y_val))
ds_val = ds_val.map(preprocess).batch(batchsz, drop_remainder=True)

# 构建多层网络
network = Sequential([layers.Dense(256, activation='relu'),
layers.Dense(128, activation='relu'),
layers.Dense(64, activation='relu'),
layers.Dense(32, activation='relu'),
layers.Dense(1)])
network.build(input_shape=(None, 28 * 28))

# 编译模型
loss = losses.CategoricalCrossentropy(from_logits=True),
metrics = ['accuracy'])



#### 2.fit—训练模型

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets,optimizers,losses,metrics,layers,Sequential

# 数据预处理
def preprocess(x, y):
x = tf.cast(x, dtype=tf.float32) / 255.
y = tf.cast(y, dtype=tf.int32)

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())
x = x.reshape((-1,28*28))
x_val = x_val.reshape((-1,28*28))
# 数据集加载并预处理
db = tf.data.Dataset.from_tensor_slices((x, y))
db = db.map(preprocess).shuffle(60000).batch(batchsz).repeat(10)

ds_val = tf.data.Dataset.from_tensor_slices((x_val, y_val))
ds_val = ds_val.map(preprocess).batch(batchsz, drop_remainder=True)

# 构建多层网络
network = Sequential([layers.Dense(256, activation='relu'),
layers.Dense(128, activation='relu'),
layers.Dense(64, activation='relu'),
layers.Dense(32, activation='relu'),
layers.Dense(1)])
network.build(input_shape=(None, 28 * 28))

# 编译模型
loss = losses.CategoricalCrossentropy(from_logits=True),
metrics = ['accuracy'])

# 训练模型
network.fit(db,epochs=100)


#### 3.Evaluate—评估模型

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets,optimizers,losses,metrics,layers,Sequential
print(tf.__version__)
# 数据预处理
def preprocess(x, y):
x = tf.cast(x, dtype=tf.float32) / 255.
y = tf.cast(y, dtype=tf.int32)

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())
x = x.reshape((-1,28*28))
x_val = x_val.reshape((-1,28*28))
# 数据集加载并预处理
db = tf.data.Dataset.from_tensor_slices((x, y))
db = db.map(preprocess).shuffle(60000).batch(batchsz).repeat(10)

ds_val = tf.data.Dataset.from_tensor_slices((x_val, y_val))
ds_val = ds_val.map(preprocess).batch(batchsz, drop_remainder=True)

# 构建多层网络
network = Sequential([layers.Dense(256, activation='relu'),
layers.Dense(128, activation='relu'),
layers.Dense(64, activation='relu'),
layers.Dense(32, activation='relu'),
layers.Dense(1)])
network.build(input_shape=(None, 28 * 28))

# 编译模型
loss = losses.CategoricalCrossentropy(from_logits=True),
metrics = ['accuracy'])

# 训练模型
network.fit(db,epochs=10,validation_data = ds_val)


#### 4.predict—预测

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets,optimizers,losses,metrics,layers,Sequential
from sklearn.metrics import accuracy_score
import numpy as np
print(tf.__version__)
# 数据预处理
def preprocess(x, y):
x = tf.cast(x, dtype=tf.float32) / 255.
y = tf.cast(y, dtype=tf.int32)

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())

x = x.reshape((-1,28*28))
y = tf.one_hot(y,depth=10)
x_val = x_val.reshape((-1,28*28))
y_val = tf.one_hot(y_val,depth=10)

# 数据集加载并预处理
db = tf.data.Dataset.from_tensor_slices((x, y))
db = db.map(preprocess).shuffle(60000).batch(batchsz).repeat(10)

ds_val = tf.data.Dataset.from_tensor_slices((x_val, y_val))
ds_val = ds_val.map(preprocess).batch(batchsz, drop_remainder=True)

# 构建多层网络
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)])
network.build(input_shape=(None, 28 * 28))

# 编译模型
loss = losses.CategoricalCrossentropy(from_logits=True),
metrics = ['accuracy'])

# 训练模型
network.fit(db,epochs=5,validation_data = ds_val,validation_freq=2)
network.summary()

# val
network.evaluate(ds_val)

# predict
pred = network.predict(x_val)

y_true = tf.argmax(y_val,axis=1)
y_pred = tf.argmax(pred,axis=1)
correct = tf.equal(y_true,y_pred)
total_correct = tf.reduce_sum(tf.cast(correct,dtype=np.int32)).numpy()
print(total_correct/x_val.shape[0])

Epoch 1/5
4690/4690 [==============================] - 16s 3ms/step - loss: 0.1098 - accuracy: 0.9695
Epoch 2/5
4690/4690 [==============================] - 18s 4ms/step - loss: 0.0531 - accuracy: 0.9873 - val_loss: 0.1227 - val_accuracy: 0.9776
Epoch 3/5
4690/4690 [==============================] - 19s 4ms/step - loss: 0.0448 - accuracy: 0.9902
Epoch 4/5
4690/4690 [==============================] - 18s 4ms/step - loss: 0.0376 - accuracy: 0.9923 - val_loss: 0.1778 - val_accuracy: 0.9763
Epoch 5/5
4690/4690 [==============================] - 19s 4ms/step - loss: 0.0368 - accuracy: 0.9921
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
dense (Dense)                (None, 256)               200960
_________________________________________________________________
dense_1 (Dense)              (None, 128)               32896
_________________________________________________________________
dense_2 (Dense)              (None, 64)                8256
_________________________________________________________________
dense_3 (Dense)              (None, 32)                2080
_________________________________________________________________
dense_4 (Dense)              (None, 10)                330
=================================================================
Total params: 244,522
Trainable params: 244,522
Non-trainable params: 0
_________________________________________________________________
78/78 [==============================] - 0s 3ms/step - loss: 0.1899 - accuracy: 0.9758
tf.Tensor([7 2 1 ... 4 5 6], shape=(10000,), dtype=int64)
tf.Tensor([7 2 1 ... 4 5 6], shape=(10000,), dtype=int64)
0.9737


### 三、自定义层或网络

#### 1.keras.Sequential

# 构建多层网络
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)])
# 建立网络参数
network.build(input_shape=(None, 28 * 28))


#### 2.keras.Model / keras.layers.Layer

• __init__

• call()


• __init__

• call()

• Model:compile / fit / evaluate


#### 3.自定义层

# 自定义Dense层
class MyDense(layers.Layer):
# 初始化方法
def __init__(self,inp_dim,outp_dim):
# 调用母类的初始化
super(MyDense,self).__init__()
# 当两个容器拼接时，会把这两个variable交给上面的容器来管理，统一管理，不需要人为管理参数
# 这个函数在母类中实现，所以可以直接调用

def call(self,inputs,training = None):
out = inputs @	self.kernel + self.bias
return out


#### 4.自定义网络

# 自定义Dense层
class MyDense(layers.Layer):
# 初始化方法
def __init__(self,inp_dim,outp_dim):
# 调用母类的初始化
super(MyDense,self).__init__()
# 当两个容器拼接时，会把这两个variable交给上面的容器来管理，统一管理，不需要人为管理参数
# 这个函数在母类中实现，所以可以直接调用

def call(self,inputs,training = None):
out = inputs @	self.kernel + self.bias
return out

# 利用自定义层，创建自定义网络(5层)
class MyModel(keras.Model):
def __init__(self):
super(MyModel,self).__init__()
self.fc1 = MyDense(28*28,256)
self.fc2 = MyDense(256,128)
self.fc3 = MyDense(128,64)
self.fc4 = MyDense(64,32)
self.fc5 = MyDense(32,10)

# 定义前向传播
def call(self,inputs,training = None):
x = self.fc1(inputs)
x = tf.nn.relu(x)

x = self.fc2(x)
x = tf.nn.relu(x)

x = self.fc3(x)
x = tf.nn.relu(x)
x = self.fc4(x)
x = tf.nn.relu(x)
x = self.fc5(x)
return x


#### 5.自定义网络实战—手写数字识别

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

import tensorflow as tf
from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics
from tensorflow import keras

# 数据预处理
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
# 数据集加载
(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)

# 构建多层网络
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)])
network.build(input_shape=(None, 28 * 28))
network.summary()

# 自定义构建多层网络
# 自定义层
class MyDense(layers.Layer):

def __init__(self, inp_dim, outp_dim):
super(MyDense, self).__init__()

def call(self, inputs, training=None):
out = inputs @ self.kernel + self.bias

return out

# 自定义网络
class MyModel(keras.Model):

def __init__(self):
super(MyModel, self).__init__()

self.fc1 = MyDense(28 * 28, 256)
self.fc2 = MyDense(256, 128)
self.fc3 = MyDense(128, 64)
self.fc4 = MyDense(64, 32)
self.fc5 = MyDense(32, 10)

def call(self, inputs, training=None):
x = self.fc1(inputs)
x = tf.nn.relu(x)
x = self.fc2(x)
x = tf.nn.relu(x)
x = self.fc3(x)
x = tf.nn.relu(x)
x = self.fc4(x)
x = tf.nn.relu(x)
x = self.fc5(x)

return x

network = MyModel()

loss=tf.losses.CategoricalCrossentropy(from_logits=True),
metrics=['accuracy']
)

network.fit(db, epochs=5, validation_data=ds_val,
validation_freq=2)

network.evaluate(ds_val)

sample = next(iter(ds_val))
x = sample[0]
y = sample[1]  # one-hot
pred = network.predict(x)  # [b, 10]
# convert back to number
y = tf.argmax(y, axis=1)
pred = tf.argmax(pred, axis=1)

print(pred)
print(y)



#### 6.自定义网络实战—CIFAR10

import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics

# 数据预处理
def preprocess(x,y):
# [-1,1]
x = 2 * tf.cast(x,dtype=tf.float32) / 255. - 1
y = tf.cast(y,dtype=tf.int32)
return x,y

batchsz = 128
# 数据集的加载
# x[b,32,32,3]  y[b,1]

# 消去[b,1]的1这个维度
y = tf.squeeze(y)
y_val = tf.squeeze(y_val)

y = tf.one_hot(y,depth=10)
y_val = tf.one_hot(y_val,depth=10)
print('datasets:',x.shape,y.shape,x.min(),x.max())
# datasets: (50000, 32, 32, 3) (50000, 10) 0 255

# 构建两个数据集
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)

sample = next(iter(train_db))
print('batch:',sample[0].shape,sample[1].shape)

# 创建自己的层
# replace standard layers.Dense
class MyDense(layers.Layer):
def __init__(self,inp_dim,outp_dim):
super(MyDense,self).__init__()

# 构建前向传播
def call(self,input,training = None):
x = input @ self.kernel
return x

# 构建自定义网络(5层)
class MyNetwork(keras.Model):
def __init__(self):
super(MyNetwork,self).__init__()

# 优化-使参数变大-但容易造成过拟合
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)

def call(self,inputs,training=None):
"""

:param inputs: [b,32,32,3]
:param training:
:return:
"""
# 打平操作
x = tf.reshape(inputs,[-1,32*32*3])
x = self.fc1(x)
x = tf.nn.relu(x)
x = self.fc2(x)
x = tf.nn.relu(x)
x = self.fc3(x)
x = tf.nn.relu(x)
x = self.fc4(x)
x = tf.nn.relu(x)
# x[b,32]->[b,10]
x = self.fc5(x)
return x

network = MyNetwork()
loss = tf.losses.CategoricalCrossentropy(from_logits=True),
metrics = ['accuracy'])

network.fit(train_db,epochs=15,validation_data = test_db,validation_freq=1)

# 保存模型权值
network.evaluate(test_db)
network.save_weights('ckpt/weights.ckpt')
del network
print('saved to ckpt/weights.ckpt')

network = MyNetwork()
loss = tf.losses.CategoricalCrossentropy(from_logits=True),
metrics = ['accuracy'])

# 加载模型权值
network.evaluate(test_db)

Epoch 14/15
1/391 [..............................] - ETA: 2:59 - loss: 0.6248 - accuracy: 0.8047
8/391 [..............................] - ETA: 24s - loss: 0.6025 - accuracy: 0.7744
14/391 [>.............................] - ETA: 15s - loss: 0.5613 - accuracy: 0.7952
20/391 [>.............................] - ETA: 11s - loss: 0.5669 - accuracy: 0.7969
26/391 [>.............................] - ETA: 9s - loss: 0.5580 - accuracy: 0.8029
32/391 [=>............................] - ETA: 8s - loss: 0.5757 - accuracy: 0.7932
38/391 [=>............................] - ETA: 7s - loss: 0.5719 - accuracy: 0.7926
44/391 [==>...........................] - ETA: 6s - loss: 0.5721 - accuracy: 0.7933
50/391 [==>...........................] - ETA: 5s - loss: 0.5669 - accuracy: 0.7962
56/391 [===>..........................] - ETA: 5s - loss: 0.5710 - accuracy: 0.7939
62/391 [===>..........................] - ETA: 5s - loss: 0.5740 - accuracy: 0.7941
68/391 [====>.........................] - ETA: 4s - loss: 0.5731 - accuracy: 0.7945
75/391 [====>.........................] - ETA: 4s - loss: 0.5753 - accuracy: 0.7922
81/391 [=====>........................] - ETA: 4s - loss: 0.5745 - accuracy: 0.7936
88/391 [=====>........................] - ETA: 4s - loss: 0.5727 - accuracy: 0.7936
94/391 [======>.......................] - ETA: 3s - loss: 0.5742 - accuracy: 0.7927
101/391 [======>.......................] - ETA: 3s - loss: 0.5736 - accuracy: 0.7932
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391/391 [==============================] - 4s 10ms/step - loss: 0.5697 - accuracy: 0.7956 - val_loss: 1.9200 - val_accuracy: 0.5195
Epoch 15/15
1/391 [..............................] - ETA: 2:55 - loss: 0.6455 - accuracy: 0.7812
8/391 [..............................] - ETA: 24s - loss: 0.5190 - accuracy: 0.8135
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391/391 [==============================] - 4s 10ms/step - loss: 0.5200 - accuracy: 0.8126 - val_loss: 2.0124 - val_accuracy: 0.5189
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79/79 [==============================] - 0s 6ms/step - loss: 2.0124 - accuracy: 0.5189
saved to ckpt/weights.ckpt
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73/79 [==========================>...] - ETA: 0s - loss: 1.9814 - accuracy: 0.5205
79/79 [==============================] - 1s 7ms/step - loss: 2.0124 - accuracy: 0.5189


### 四、模型的加载与保存

最干净、最轻量级的模型，只保存网络参数，适用于有源代码的情况下
• save / load entire model
最简单粗暴，将模型的所有状态都保存下来，可以用来恢复
• save_model
模型的一种保存模式，与pyt中的ONNX模式相对应，适用于工厂环境部署
python代码可以用C++来解析

import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

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()

loss=tf.losses.CategoricalCrossentropy(from_logits=True),
metrics=['accuracy']
)

network.fit(db, epochs=3, validation_data=ds_val, validation_freq=2)

network.evaluate(ds_val)

# 保存模型的参数
network.save_weights('weights.ckpt')
print('saved weights.')
del network

# 构建多层网络
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)])
loss=tf.losses.CategoricalCrossentropy(from_logits=True),
metrics=['accuracy']
)
# 加载模型的参数
network.evaluate(ds_val)

Epoch 2/3
1/469 [..............................] - ETA: 13:50 - loss: 0.1488 - accuracy: 0.9688
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469/469 [==============================] - 3s 7ms/step - loss: 0.1344 - accuracy: 0.9629 - val_loss: 0.1209 - val_accuracy: 0.9648
Epoch 3/3
1/469 [..............................] - ETA: 14:16 - loss: 0.1254 - accuracy: 0.9609
20/469 [>.............................] - ETA: 42s - loss: 0.1014 - accuracy: 0.9695
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459/469 [============================>.] - ETA: 0s - loss: 0.1083 - accuracy: 0.9700
469/469 [==============================] - 3s 6ms/step - loss: 0.1082 - accuracy: 0.9701
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70/79 [=========================>....] - ETA: 0s - loss: 0.1389 - accuracy: 0.9667
79/79 [==============================] - 0s 5ms/step - loss: 0.1372 - accuracy: 0.9664
saved weights.
1/79 [..............................] - ETA: 6s - loss: 0.0620 - accuracy: 0.9844
11/79 [===>..........................] - ETA: 0s - loss: 0.1625 - accuracy: 0.9616
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78/79 [============================>.] - ETA: 0s - loss: 0.1388 - accuracy: 0.9663
79/79 [==============================] - 1s 6ms/step - loss: 0.1372 - accuracy: 0.9664


#### 2.save / load entire model

import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics

# 数据预处理
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
# 数据集加载
(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()

loss=tf.losses.CategoricalCrossentropy(from_logits=True),
metrics=['accuracy']
)

network.fit(db, epochs=3, validation_data=ds_val, validation_freq=2)

network.evaluate(ds_val)

# 保存整个模型
network.save('model.h5')
print('saved total model.')
del network

# 加载整个模型

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)

datasets: (60000, 28, 28) (60000,) 0 255
(128, 784) (128, 10)
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
dense (Dense)                (None, 256)               200960
_________________________________________________________________
dense_1 (Dense)              (None, 128)               32896
_________________________________________________________________
dense_2 (Dense)              (None, 64)                8256
_________________________________________________________________
dense_3 (Dense)              (None, 32)                2080
_________________________________________________________________
dense_4 (Dense)              (None, 10)                330
=================================================================
Total params: 244,522
Trainable params: 244,522
Non-trainable params: 0
_________________________________________________________________
Epoch 1/3
469/469 [==============================] - 1s 2ms/step - loss: 0.2723 - accuracy: 0.9182
Epoch 2/3
469/469 [==============================] - 1s 3ms/step - loss: 0.1363 - accuracy: 0.9628 - val_loss: 0.1280 - val_accuracy: 0.9637
Epoch 3/3
469/469 [==============================] - 1s 2ms/step - loss: 0.1101 - accuracy: 0.9692
79/79 [==============================] - 0s 3ms/step - loss: 0.1372 - accuracy: 0.9673
saved total model.
79/79 [==============================] - 0s 1ms/step - loss: 0.1372 - accuracy: 0.9673


#### 3.save_model

tf.saved_model.save(m,'/tmp/saved_model/')

f = imported.signatures['serving_default']
print(f(x = tf.ones([1,28,28,3])))


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