【tensorflow2.0】训练模型的三种⽅法
模型的训练主要有内置fit⽅法、内置tran_on_batch⽅法、⾃定义训练循环。
注：fit_generator⽅法在tf.keras中不推荐使⽤，其功能已经被fit包含。
黄觉徐静蕾import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras import *
# 打印时间分割线
@tf.function
def printbar():
ts = tf.timestamp()
today_ts = ts%(24*60*60)
hour = tf.cast(today_ts//3600+8,tf.int32)%tf.constant(24)
多情的土地minite = tf.cast((today_ts%3600)//60,tf.int32)
second = tf.cast(tf.floor(today_ts%60),tf.int32)
def timeformat(m):
if tf.strings.length(tf.strings.format("{}",m))==1:
return(tf.strings.format("0{}",m))
else:
return(tf.strings.format("{}",m))
timestring = tf.strings.join([timeformat(hour),timeformat(minite),
timeformat(second)],separator = ":")
tf.print("=========="*8,end = "")
tf.print(timestring)
MAX_LEN = 300
BATCH_SIZE = 32
(x_train,y_train),(x_test,y_test) = uters.load_data()
x_train = preprocessing.sequence.pad_sequences(x_train,maxlen=MAX_LEN)
x_test = preprocessing.sequence.pad_sequences(x_test,maxlen=MAX_LEN)
MAX_WORDS = x_train.max()+1
CAT_NUM = y_train.max()+1
ds_train = tf.data.Dataset.from_tensor_slices((x_train,y_train)) \
.shuffle(buffer_size = 1000).batch(BATCH_SIZE) \
.
prefetch(perimental.AUTOTUNE).cache()
ds_test = tf.data.Dataset.from_tensor_slices((x_test,y_test)) \
.shuffle(buffer_size = 1000).batch(BATCH_SIZE) \
.prefetch(perimental.AUTOTUNE).cache()
⼀，内置fit⽅法
该⽅法功能⾮常强⼤, ⽀持对numpy array, tf.data.Dataset以及 Python generator数据进⾏训练。
tf.keras.backend.clear_session()
def create_model():
model = models.Sequential()
model.add(layers.Embedding(MAX_WORDS,7,input_length=MAX_LEN))
model.add(layers.Conv1D(filters = 64,kernel_size = 5,activation = "relu"))
model.add(layers.MaxPool1D(2))
model.add(layers.Conv1D(filters = 32,kernel_size = 3,activation = "relu"))
model.add(layers.MaxPool1D(2))
model.add(layers.Flatten())
model.add(layers.Dense(CAT_NUM,activation = "softmax"))
return(model)
def compile_model(model):
modelpile(optimizer=optimizers.Nadam(),
loss=losses.SparseCategoricalCrossentropy(),
metrics=[metrics.SparseCategoricalAccuracy(),metrics.SparseTopKCategoricalAccuracy(5)])
return(model)
model = create_model()
model.summary()
model = compile_model(model)
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding (Embedding) (None, 300, 7) 216874
_________________________________________________________________
conv1d (Conv1D) (None, 296, 64) 2304
_________________________________________________________________
max_pooling1d (MaxPooling1D) (None, 148, 64) 0
_________________________________________________________________
conv1d_1 (Conv1D) (None, 146, 32) 6176
_________________________________________________________________
max_pooling1d_1 (MaxPooling1 (None, 73, 32) 0
_________________________________________________________________
flatten (Flatten) (None, 2336) 0
_________________________________________________________________
dense (Dense) (None, 46) 107502
=================================================================
Total params: 332,856
Trainable params: 332,856
Non-trainable params: 0
_________________________________________________________________
history = model.fit(ds_train,validation_data = ds_test,epochs = 10)
Epoch 1/10
281/281 [==============================] - 8s 28ms/step - loss: 1.9854 - sparse_categorical_accuracy: 0.4876 - sparse_top_k_categorical_accuracy: 0.7488 - val_loss: 1.6438 - val_sparse_categorical_accuracy: 0.5841 - val_sparse_to Epoch 2/10
281/281 [==============================] - 8s 28ms/step - loss: 1.4446 - sparse_categorical_accuracy: 0.6294 - sparse_top_k_categorical_accuracy: 0.8037 - val_loss: 1.5316 - val_sparse_categorical_accuracy: 0.6126 - val_sparse_to Epoch 3/10
281/281 [==============================] - 8s 28ms/step - loss: 1.1883 - sparse_categorical_accuracy: 0.6906 - sparse_top_k_categorical_accuracy: 0.8549 - val_loss: 1.6185 - val_sparse_categorical_accuracy: 0.6278 - val_sparse_to Epoch 4/10
281/281 [==============================] - 8s 28ms/step - loss: 0.9406 - sparse_categorical_accuracy: 0.7546 - sparse_top_k_categorical_accuracy: 0.9057 - val_loss: 1.7211 - val_sparse_categorical_accuracy: 0.6153 - val_sparse_to Epoch 5/10
281/281 [==============================] - 8s 29ms/step - loss: 0.7207 - sparse_categorical_accuracy: 0.8108 - sparse_top_k_categorical_accuracy: 0.9404 - val_loss: 1.9749 - val_sparse_categorical_accuracy: 0.6233 - val_sparse_to Epoch 6/10
281/281 [==============================] - 8s 28ms/step - loss: 0.5558 - sparse_categorical_accuracy: 0.8540 - sparse_top_k_categorical_accuracy: 0.9643 - val_loss: 2.2560 - val_sparse_categorical_accuracy: 0.6269 - val_sparse_to Epoch 7/10
281/281 [==============================] - 8s 28ms/step - loss: 0.4438 - sparse_categorical_accuracy: 0.8916 - sparse_top_k_categorical_accuracy: 0.9781 - val_loss: 2.4731 - val_sparse_categorical_accuracy: 0.6238 - val_sparse_to Epoch 8/10
281/281 [==============================] - 8s 29ms/step - loss: 0.3710 - sparse_categorical_accuracy: 0.9086 - sparse_top_k_categorical_accuracy: 0.9837 - val_loss: 2.6960 - val_sparse_categorical_accuracy: 0.6175 - val_sparse_to Epoch 9/10
281/281 [==============================] - 8s 28ms/step - loss: 0.3201 - sparse_categorical_accuracy: 0.9203 - sparse_top_k_categorical_accuracy: 0.9894 - val_loss: 3.1160 - val_sparse_categorical_accuracy: 0.6193 - va
Epoch 10/10
281/281 [==============================] - 8s 28ms/step - loss: 0.2827 - sparse_categorical_accuracy: 0.9262 - sparse_top_k_categorical_accuracy: 0.9922 - val_loss: 2.9516 - val_sparse_categorical_accuracy: 0.6264 - val_sparse_to ⼆，内置train_on_batch⽅法
该内置⽅法相⽐较fit⽅法更加灵活，可以不通过回调函数⽽直接在批次层次上更加精细地控制训练的过程。
tf.keras.backend.clear_session()
def create_model():
model = models.Sequential()
model.add(layers.Embedding(MAX_WORDS,7,input_length=MAX_LEN))
model.add(layers.Conv1D(filters = 64,kernel_size = 5,activation = "relu"))
model.add(layers.MaxPool1D(2))
model.add(layers.Conv1D(filters = 32,kernel_size = 3,activation = "relu"))
model.add(layers.MaxPool1D(2))
model.add(layers.Flatten())
model.add(layers.Dense(CAT_NUM,activation = "softmax"))
return(model)
def compile_model(model):
modelpile(optimizer=optimizers.Nadam(),
loss=losses.SparseCategoricalCrossentropy(),
metrics=[metrics.SparseCategoricalAccuracy(),metrics.SparseTopKCategoricalAccuracy(5)])
return(model)
model = create_model()
model.summary()
model = compile_model(model)
Model: "sequential"
_________________________________________________________________
hiromiLayer (type) Output Shape Param #
=================================================================
embedding (Embedding) (None, 300, 7) 216874
_________________________________________________________________
conv1d (Conv1D) (None, 296, 64) 2304
_________________________________________________________________
max_pooling1d (MaxPooling1D) (None, 148, 64) 0
_________________________________________________________________
conv1d_1 (Conv1D) (None, 146, 32) 6176
_________________________________________________________________
max_pooling1d_1 (MaxPooling1 (None, 73, 32) 0
_________________________________________________________________
flatten (Flatten) (None, 2336) 0
_________________________________________________________________
dense (Dense) (None, 46) 107502
=================================================================
Total params: 332,856
Trainable params: 332,856
Non-trainable params: 0
_________________________________________________________________
def train_model(model,ds_train,ds_valid,epoches):
for epoch in tf.range(1,epoches+1):
# 在后期降低学习率
if epoch == 5:
model.optimizer.lr.assign(model.optimizer.lr/2.0)
tf.print("Lowering optimizer \n\n")
for x, y in ds_train:
train_result = ain_on_batch(x, y)
for x, y in ds_valid:
valid_result = st_on_batch(x, y,reset_metrics=False)
if epoch%1 ==0:
printbar()
tf.print("epoch = ",epoch)
print("train:",dict(ics_names,train_result)))
print("valid:",dict(ics_names,valid_result)))
print("")
train_model(model,ds_train,ds_test,10)
================================================================================11:49:43
epoch = 1
train: {'loss': 2.0567171573638916, 'sparse_categorical_accuracy': 0.4545454680919647, 'sparse_top_k_categorical_accuracy': 0.6818181872367859}
valid: {'loss': 1.6894209384918213, 'sparse_categorical_accuracy': 0.5605521202087402, 'sparse_top_k_categorical_accuracy': 0.7617987394332886}
================================================================================11:49:53
epoch = 2
train: {'loss': 1.4644863605499268, 'sparse_categorical_accuracy': 0.6363636255264282, 'sparse_top_k_categorical_accuracy': 0.7727272510528564}
valid: {'loss': 1.5152910947799683, 'sparse_categorical_accuracy': 0.6157613396644592, 'sparse_top_k_categorical_accuracy': 0.7938557267189026}
================================================================================11:50:01
epoch = 3
train: {'loss': 1.0017579793930054, 'sparse_categorical_accuracy': 0.7727272510528564, 'sparse_top_k_categorical_accuracy': 0.9545454382896423}
valid: {'loss': 1.5588842630386353, 'sparse_categorical_accuracy': 0.622885**********, 'sparse_top_k_categorical_accuracy': 0.8058770895004272}
================================================================================11:50:10
epoch = 4
train: {'loss': 0.6004871726036072, 'sparse_categorical_accuracy': 0.9090909361839294, 'sparse_top_k_categorical_accuracy': 1.0}
valid: {'loss': 1.7447566986083984, 'sparse_categorical_accuracy': 0.6233303546905518, 'sparse_top_k_categorical_accuracy': 0.8174532651901245}
Lowering optimizer
================================================================================11:50:19
epoch = 5
train: {'loss': 0.3866238594055176, 'sparse_categorical_accuracy': 0.9545454382896423, 'sparse_top_k_categorical_accuracy': 1.0}
valid: {'loss': 1.8871253728866577, 'sparse_categorical_accuracy': 0.6308993697166443, 'sparse_top_k_categorical_accuracy': 0.816117525100708}
================================================================================11:50:28
epoch = 6
train: {'loss': 0.27341774106025696, 'sparse_categorical_accuracy': 0.9545454382896423, 'sparse_top_k_categorical_accuracy': 1.0}
valid: {'loss': 2.0595862865448, 'sparse_categorical_accuracy': 0.6273375153541565, 'sparse_top_k_categorical_accuracy': 0.8089937567710876}
================================================================================11:50:37
epoch = 7
train: {'loss': 0.1923554539680481, 'sparse_categorical_accuracy': 0.9545454382896423, 'sparse_top_k_categorical_accuracy': 1.0}
valid: {'loss': 2.2238168716430664, 'sparse_categorical_accuracy': 0.6251112818717957, 'sparse_top_k_categorical_accuracy': 0.8085485100746155}
================================================================================11:50:46
epoch = 8
train: {'loss': 0.12688547372817993, 'sparse_categorical_accuracy': 0.9545454382896423, 'sparse_top_k_categorical_accuracy': 1.0}
valid: {'loss': 2.3778438568115234, 'sparse_categorical_accuracy': 0.6175423264503479, 'sparse_top_k_categorical_accuracy': 0.8072128295898438}
================================================================================11:50:55
epoch = 9
train: {'loss': 0.08024053275585175, 'sparse_categorical_accuracy': 0.9545454382896423, 'sparse_top_k_categorical_accuracy': 1.0}
valid: {'loss': 2.501840829849243, 'sparse_categorical_accuracy': 0.6135351657867432, 'sparse_top_k_categorical_accuracy': 0.8081033229827881} ================================================================================11:51:04
epoch = 10
train: {'loss': 0.05211604759097099, 'sparse_categorical_accuracy': 1.0, 'sparse_top_k_categorical_accuracy': 1.0}
valid: {'loss': 2.61771559715271, 'sparse_categorical_accuracy': 0.6126446723937988, 'sparse_top_k_categorical_accuracy': 0.8085485100746155}三，⾃定义训练循环
⾃定义训练循环⽆需编译模型，直接利⽤优化器根据损失函数反向传播迭代参数，拥有最⾼的灵活性。
tf.keras.backend.clear_session()
def create_model():
model = models.Sequential()
model.add(layers.Embedding(MAX_WORDS,7,input_length=MAX_LEN))
model.add(layers.Conv1D(filters = 64,kernel_size = 5,activation = "relu"))
model.add(layers.MaxPool1D(2))
model.add(layers.Conv1D(filters = 32,kernel_size = 3,activation = "relu"))
model.add(layers.MaxPool1D(2))
model.add(layers.Flatten())
model.add(layers.Dense(CAT_NUM,activation = "softmax"))
return(model)
model = create_model()
model.summary()
optimizer = optimizers.Nadam()
loss_func = losses.SparseCategoricalCrossentropy()
train_loss = metrics.Mean(name='train_loss')
train_metric = metrics.SparseCategoricalAccuracy(name='train_accuracy')
valid_loss = metrics.Mean(name='valid_loss')
valid_metric = metrics.SparseCategoricalAccuracy(name='valid_accuracy')
@tf.function
def train_step(model, features, labels):
with tf.GradientTape() as tape:
predictions = model(features,training = True)
loss = loss_func(labels, predictions)
gradients = adient(loss, ainable_variables)
optimizer.apply_gradients(zip(gradients, ainable_variables))
train_loss.update_state(loss)
train_metric.update_state(labels, predictions)
@tf.function
def valid_step(model, features, labels):
等俺有钱了 李春波predictions = model(features)
batch_loss = loss_func(labels, predictions)
valid_loss.update_state(batch_loss)
valid_metric.update_state(labels, predictions)
def train_model(model,ds_train,ds_valid,epochs):
for epoch in tf.range(1,epochs+1):
for features, labels in ds_train:
train_step(model,features,labels)
for features, labels in ds_valid:
valid_step(model,features,labels)
logs = 'Epoch={},Loss:{},Accuracy:{},Valid Loss:{},Valid Accuracy:{}'
if epoch%1 ==0:
printbar()
tf.print(tf.strings.format(logs,
(epoch,sult(),sult(),sult(),sult())))
tf.print("")
set_states()
set_states()
set_states()
set_states()
train_model(model,ds_train,ds_test,10)
Model: "sequential"
_________________________________________________________________
丁子高爸爸Layer (type) Output Shape Param #
=================================================================
embedding (Embedding) (None, 300, 7) 216874
_________________________________________________________________
conv1d (Conv1D) (None, 296, 64) 2304
_________________________________________________________________
max_pooling1d (MaxPooling1D) (None, 148, 64) 0
_________________________________________________________________
conv1d_1 (Conv1D) (None, 146, 32) 6176
_________________________________________________________________
max_pooling1d_1 (MaxPooling1 (None, 73, 32) 0
_________________________________________________________________
flatten (Flatten) (None, 2336) 0
_________________________________________________________________
dense (Dense) (None, 46) 107502
=================================================================
Total params: 332,856
Trainable params: 332,856
Non-trainable params: 0
_________________________________________________________________
================================================================================11:52:04
Epoch=1,Loss:2.02564383,Accuracy:0.464707196,Valid Loss:1.68035507,Valid Accuracy:0.55921638克里斯蒂娜 里奇
================================================================================11:52:11
Epoch=2,Loss:1.48306167,Accuracy:0.612781107,Valid Loss:1.52322364,Valid Accuracy:0.606411397
================================================================================11:52:18
Epoch=3,Loss:1.20491719,Accuracy:0.677243352,Valid Loss:1.56225574,Valid Accuracy:0.624666095
================================================================================11:52:25
Epoch=4,Loss:0.944778264,Accuracy:0.749387681,Valid Loss:1.7202934,Valid Accuracy:0.620658934
================================================================================11:52:32
Epoch=5,Loss:0.701866329,Accuracy:0.817635298,Valid Loss:1.97179747,Valid Accuracy:0.61843276
================================================================================11:52:39
Epoch=6,Loss:0.531810164,Accuracy:0.866844773,Valid Loss:2.25338316,Valid Accuracy:0.605075717
================================================================================11:52:46 Epoch=7,Loss:0.425013304,Accuracy:0.896236897,Valid Loss:2.47035336,Valid Accuracy:0.601068556
================================================================================11:52:53 Epoch=8,Loss:0.355143964,Accuracy:0.915609,Valid Loss:2.67822,Valid Accuracy:0.591718614
================================================================================11:53:00 Epoch=9,Loss:0.30812338,Accuracy:0.92785573,Valid Loss:2.86121941,Valid Accuracy:0.583704352
================================================================================11:53:07 Epoch=10,Loss:0.275565386,Accuracy:0.934535742,Valid Loss:2.99354172,Valid Accuracy:0.579252
参考：
开源电⼦书地址：lyhue1991.github.io/eat_tensorflow2_in_30_days/
GitHub 项⽬地址：github/lyhue1991/eat_tensorflow2_in_30_days
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