文章目录

  • 一、metrics
    • 1.实战
  • 二、compile&fit&Evaluate&Predict
    • 1.compile—编译模型
    • 2.fit—训练模型
    • 3.Evaluate—评估模型
    • 4.predict—预测
  • 三、自定义层或网络
    • 1.keras.Sequential
    • 2.keras.Model / keras.layers.Layer
    • 3.自定义层
    • 4.自定义网络
    • 5.自定义网络实战—手写数字识别
    • 6.自定义网络实战—CIFAR10
  • 四、模型的加载与保存
    • 1.save / load weights
    • 2.save / load entire model
    • 3.save_model

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, ybatchsz = 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
optimizer = optimizers.Adam(lr=0.01)# 评价指标-acc,loss
acc_meter = metrics.Accuracy()
loss_meter = metrics.Mean()for step, (x, y) in enumerate(db):with tf.GradientTape() as tape:# [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)grads = tape.gradient(loss, network.trainable_variables)optimizer.apply_gradients(zip(grads, network.trainable_variables))if step % 100 == 0:print(step, 'loss:', loss_meter.result().numpy())loss_meter.reset_states()# evaluateif step % 500 == 0:total, total_correct = 0., 0acc_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 typecorrect = tf.equal(pred, y)# bool tensor => int tensor => numpytotal_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

指定训练时loss（损失）、optimizer（优化器）和metrics（评价指标）的选择

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, ybatchsz = 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
network.compile(optimizer=optimizers.Adam(lr=0.01),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, ybatchsz = 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
network.compile(optimizer=optimizers.Adam(lr=0.01),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, ybatchsz = 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
network.compile(optimizer=optimizers.Adam(lr=0.01),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, ybatchsz = 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
network.compile(optimizer=optimizers.Adam(lr=0.01),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

继承keras.layers.Layer来实现自定义层，自己的逻辑在call()方法中

  • __init__

  • call()

继承keras.Model来实现自定义网络，中间有一个小类继承keras.layers.Layer

  • __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__()# self.add_variable作用是在创建这两个Variable时，同时告诉类这两个variable是需要创建的# 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self.kernel = self.add_variable('w',[inp_dim,outp_dim])self.bias = self.add_variable('b',[outp_dim])def call(self,inputs,training = None):out = inputs @   self.kernel + self.biasreturn 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__()# self.add_variable作用是在创建这两个Variable时，同时告诉类这两个variable是需要创建的# 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self.kernel = self.add_variable('w',[inp_dim,outp_dim])self.bias = self.add_variable('b',[outp_dim])def call(self,inputs,training = None):out = inputs @   self.kernel + self.biasreturn 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, ybatchsz = 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__()self.kernel = self.add_variable('w', [inp_dim, outp_dim])self.bias = self.add_variable('b', [outp_dim])def call(self, inputs, training=None):out = inputs @ self.kernel + self.biasreturn 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 xnetwork = MyModel()network.compile(optimizer=optimizers.Adam(lr=0.01),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 osos.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. - 1y = tf.cast(y,dtype=tf.int32)return x,ybatchsz = 128
# 数据集的加载
# x[b,32,32,3]  y[b,1]
(x,y),(x_val,y_val) = datasets.cifar10.load_data()# 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__()self.kernel = self.add_variable('w',[inp_dim,outp_dim])# self.bias = self.add_variable('b',[outp_dim])# 构建前向传播def call(self,input,training = None):x = input @ self.kernelreturn 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 xnetwork = MyNetwork()
network.compile(optimizer = optimizers.Adam(lr = 1e-3),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()
network.compile(optimizer = optimizers.Adam(lr = 1e-3),loss = tf.losses.CategoricalCrossentropy(from_logits=True),metrics = ['accuracy'])# 加载模型权值
network.load_weights('ckpt/weights.ckpt')
print('load weights from file')
network.evaluate(test_db)

Epoch 14/151/391 [..............................] - ETA: 2:59 - loss: 0.6248 - accuracy: 0.80478/391 [..............................] - ETA: 24s - loss: 0.6025 - accuracy: 0.7744 14/391 [>.............................] - ETA: 15s - loss: 0.5613 - accuracy: 0.795220/391 [>.............................] - ETA: 11s - loss: 0.5669 - accuracy: 0.796926/391 [>.............................] - ETA: 9s - loss: 0.5580 - accuracy: 0.8029 32/391 [=>............................] - ETA: 8s - loss: 0.5757 - accuracy: 0.793238/391 [=>............................] - ETA: 7s - loss: 0.5719 - accuracy: 0.792644/391 [==>...........................] - ETA: 6s - loss: 0.5721 - accuracy: 0.793350/391 [==>...........................] - ETA: 5s - loss: 0.5669 - accuracy: 0.796256/391 [===>..........................] - ETA: 5s - loss: 0.5710 - accuracy: 0.793962/391 [===>..........................] - ETA: 5s - loss: 0.5740 - accuracy: 0.794168/391 [====>.........................] - ETA: 4s - loss: 0.5731 - accuracy: 0.794575/391 [====>.........................] - ETA: 4s - loss: 0.5753 - accuracy: 0.792281/391 [=====>........................] - ETA: 4s - loss: 0.5745 - accuracy: 0.793688/391 [=====>........................] - ETA: 4s - loss: 0.5727 - accuracy: 0.793694/391 [======>.......................] - ETA: 3s - loss: 0.5742 - accuracy: 0.7927
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376/391 [===========================>..] - ETA: 0s - loss: 0.5684 - accuracy: 0.7957
389/391 [============================>.] - ETA: 0s - loss: 0.5698 - accuracy: 0.7956
391/391 [==============================] - 4s 10ms/step - loss: 0.5697 - accuracy: 0.7956 - val_loss: 1.9200 - val_accuracy: 0.5195
Epoch 15/151/391 [..............................] - ETA: 2:55 - loss: 0.6455 - accuracy: 0.78128/391 [..............................] - ETA: 24s - loss: 0.5190 - accuracy: 0.8135 15/391 [>.............................] - ETA: 14s - loss: 0.5051 - accuracy: 0.816122/391 [>.............................] - ETA: 10s - loss: 0.4930 - accuracy: 0.822429/391 [=>............................] - ETA: 8s - loss: 0.4935 - accuracy: 0.8217 36/391 [=>............................] - ETA: 7s - loss: 0.4941 - accuracy: 0.823843/391 [==>...........................] - ETA: 6s - loss: 0.4999 - accuracy: 0.821250/391 [==>...........................] - ETA: 5s - loss: 0.5044 - accuracy: 0.818157/391 [===>..........................] - ETA: 5s - loss: 0.5097 - accuracy: 0.817764/391 [===>..........................] - ETA: 4s - loss: 0.5112 - accuracy: 0.817471/391 [====>.........................] - ETA: 4s - loss: 0.5097 - accuracy: 0.816878/391 [====>.........................] - ETA: 4s - loss: 0.5115 - accuracy: 0.817285/391 [=====>........................] - ETA: 4s - loss: 0.5161 - accuracy: 0.814892/391 [======>.......................] - ETA: 3s - loss: 0.5176 - accuracy: 0.814599/391 [======>.......................] - ETA: 3s - loss: 0.5187 - accuracy: 0.8149
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391/391 [==============================] - 4s 10ms/step - loss: 0.5200 - accuracy: 0.8126 - val_loss: 2.0124 - val_accuracy: 0.51891/79 [..............................] - ETA: 0s - loss: 1.6155 - accuracy: 0.5625
10/79 [==>...........................] - ETA: 0s - loss: 1.8749 - accuracy: 0.5273
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72/79 [==========================>...] - ETA: 0s - loss: 1.9786 - accuracy: 0.5215
79/79 [==============================] - 0s 6ms/step - loss: 2.0124 - accuracy: 0.5189
saved to ckpt/weights.ckpt
load weights from file1/79 [..............................] - ETA: 5s - loss: 1.6155 - accuracy: 0.5625
10/79 [==>...........................] - ETA: 0s - loss: 1.8749 - accuracy: 0.5273
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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

模型的加载与保存一共有三个模式，分别为：

  • save / load weights
    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

1.save / load weights

import osos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'import tensorflow as tf
from tensorflow.keras import datasets, layers, optimizers, Sequential, metricsdef 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, ybatchsz = 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)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)])
network.compile(optimizer=optimizers.Adam(lr=0.01),loss=tf.losses.CategoricalCrossentropy(from_logits=True),metrics=['accuracy'])
# 加载模型的参数
network.load_weights('weights.ckpt')
print('loaded weights!')
network.evaluate(ds_val)

Epoch 2/31/469 [..............................] - ETA: 13:50 - loss: 0.1488 - accuracy: 0.968819/469 [>.............................] - ETA: 43s - loss: 0.1262 - accuracy: 0.9634  39/469 [=>............................] - ETA: 20s - loss: 0.1342 - accuracy: 0.961957/469 [==>...........................] - ETA: 13s - loss: 0.1324 - accuracy: 0.963576/469 [===>..........................] - ETA: 10s - loss: 0.1379 - accuracy: 0.962393/469 [====>.........................] - ETA: 8s - loss: 0.1348 - accuracy: 0.9632
110/469 [======>.......................] - ETA: 6s - loss: 0.1370 - accuracy: 0.9618
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450/469 [===========================>..] - ETA: 0s - loss: 0.1342 - accuracy: 0.9629
466/469 [============================>.] - ETA: 0s - loss: 0.1343 - accuracy: 0.9629
469/469 [==============================] - 3s 7ms/step - loss: 0.1344 - accuracy: 0.9629 - val_loss: 0.1209 - val_accuracy: 0.9648
Epoch 3/31/469 [..............................] - ETA: 14:16 - loss: 0.1254 - accuracy: 0.960920/469 [>.............................] - ETA: 42s - loss: 0.1014 - accuracy: 0.9695  39/469 [=>............................] - ETA: 21s - loss: 0.1063 - accuracy: 0.970060/469 [==>...........................] - ETA: 13s - loss: 0.1006 - accuracy: 0.970382/469 [====>.........................] - ETA: 9s - loss: 0.1041 - accuracy: 0.9690
105/469 [=====>........................] - ETA: 7s - loss: 0.1089 - accuracy: 0.9676
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441/469 [===========================>..] - ETA: 0s - loss: 0.1089 - accuracy: 0.9697
459/469 [============================>.] - ETA: 0s - loss: 0.1083 - accuracy: 0.9700
469/469 [==============================] - 3s 6ms/step - loss: 0.1082 - accuracy: 0.97011/79 [..............................] - ETA: 0s - loss: 0.0620 - accuracy: 0.9844
11/79 [===>..........................] - ETA: 0s - loss: 0.1625 - accuracy: 0.9616
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79/79 [==============================] - 0s 5ms/step - loss: 0.1372 - accuracy: 0.9664
saved weights.
loaded 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 osos.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, ybatchsz = 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()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)network.evaluate(ds_val)# 保存整个模型
network.save('model.h5')
print('saved total model.')
del networkprint('loaded model from file.')
# 加载整个模型
network = tf.keras.models.load_model('model.h5', compile=False)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.
loaded model from file.
79/79 [==============================] - 0s 1ms/step - loss: 0.1372 - accuracy: 0.9673

3.save_model

tf.saved_model.save(m,'/tmp/saved_model/')imported = tf.saved_model.load(path)
f = imported.signatures['serving_default']
print(f(x = tf.ones([1,28,28,3])))

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