TensorFlow2.0教程-使用keras训练模型
1.一般的模型构造、训练、测试流程
# 模型构造
inputs = keras.Input(shape=(784,), name='mnist_input')
h1 = layers.Dense(64, activation='relu')(inputs)
h1 = layers.Dense(64, activation='relu')(h1)
outputs = layers.Dense(10, activation='softmax')(h1)
model = keras.Model(inputs, outputs)
# keras.utils.plot_model(model, 'net001.png', show_shapes=True) model.compile(optimizer=keras.optimizers.RMSprop(),
loss=keras.losses.SparseCategoricalCrossentropy(),
metrics=[keras.metrics.SparseCategoricalAccuracy()]) # 载入数据
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
x_train = x_train.reshape(60000, 784).astype('float32') /255
x_test = x_test.reshape(10000, 784).astype('float32') /255 x_val = x_train[-10000:]
y_val = y_train[-10000:] x_train = x_train[:-10000]
y_train = y_train[:-10000] # 训练模型
history = model.fit(x_train, y_train, batch_size=64, epochs=3,
validation_data=(x_val, y_val))
print('history:')
print(history.history) result = model.evaluate(x_test, y_test, batch_size=128)
print('evaluate:')
print(result)
pred = model.predict(x_test[:2])
print('predict:')
print(pred)
2.自定义损失和指标
自定义指标只需继承Metric类， 并重写一下函数
_init_(self)，初始化。
update_state(self，y_true，y_pred，sample_weight = None)，它使用目标y_true和模型预测y_pred来更新状态变量。
result(self)，它使用状态变量来计算最终结果。
reset_states(self)，重新初始化度量的状态。
# 这是一个简单的示例，显示如何实现CatgoricalTruePositives指标，该指标计算正确分类为属于给定类的样本数量 class CatgoricalTruePostives(keras.metrics.Metric):
def __init__(self, name='binary_true_postives', **kwargs):
super(CatgoricalTruePostives, self).__init__(name=name, **kwargs)
self.true_postives = self.add_weight(name='tp', initializer='zeros') def update_state(self, y_true, y_pred, sample_weight=None):
y_pred = tf.argmax(y_pred)
y_true = tf.equal(tf.cast(y_pred, tf.int32), tf.cast(y_true, tf.int32)) y_true = tf.cast(y_true, tf.float32) if sample_weight is not None:
sample_weight = tf.cast(sample_weight, tf.float32)
y_true = tf.multiply(sample_weight, y_true) return self.true_postives.assign_add(tf.reduce_sum(y_true)) def result(self):
return tf.identity(self.true_postives) def reset_states(self):
self.true_postives.assign(0.) model.compile(optimizer=keras.optimizers.RMSprop(1e-3),
loss=keras.losses.SparseCategoricalCrossentropy(),
metrics=[CatgoricalTruePostives()]) model.fit(x_train, y_train,
batch_size=64, epochs=3)
# 以定义网络层的方式添加网络loss
class ActivityRegularizationLayer(layers.Layer):
def call(self, inputs):
self.add_loss(tf.reduce_sum(inputs) * 0.1)
return inputs inputs = keras.Input(shape=(784,), name='mnist_input')
h1 = layers.Dense(64, activation='relu')(inputs)
h1 = ActivityRegularizationLayer()(h1)
h1 = layers.Dense(64, activation='relu')(h1)
outputs = layers.Dense(10, activation='softmax')(h1)
model = keras.Model(inputs, outputs)
# keras.utils.plot_model(model, 'net001.png', show_shapes=True) model.compile(optimizer=keras.optimizers.RMSprop(),
loss=keras.losses.SparseCategoricalCrossentropy(),
metrics=[keras.metrics.SparseCategoricalAccuracy()])
model.fit(x_train, y_train, batch_size=32, epochs=1)
# 也可以以定义网络层的方式添加要统计的metric
class MetricLoggingLayer(layers.Layer):
def call(self, inputs):
self.add_metric(keras.backend.std(inputs),
name='std_of_activation',
aggregation='mean') return inputs inputs = keras.Input(shape=(784,), name='mnist_input')
h1 = layers.Dense(64, activation='relu')(inputs)
h1 = MetricLoggingLayer()(h1)
h1 = layers.Dense(64, activation='relu')(h1)
outputs = layers.Dense(10, activation='softmax')(h1)
model = keras.Model(inputs, outputs)
# keras.utils.plot_model(model, 'net001.png', show_shapes=True) model.compile(optimizer=keras.optimizers.RMSprop(),
loss=keras.losses.SparseCategoricalCrossentropy(),
metrics=[keras.metrics.SparseCategoricalAccuracy()])
model.fit(x_train, y_train, batch_size=32, epochs=1)
# 也可以直接在model上面加
# 也可以以定义网络层的方式添加要统计的metric
class MetricLoggingLayer(layers.Layer):
def call(self, inputs):
self.add_metric(keras.backend.std(inputs),
name='std_of_activation',
aggregation='mean') return inputs inputs = keras.Input(shape=(784,), name='mnist_input')
h1 = layers.Dense(64, activation='relu')(inputs)
h2 = layers.Dense(64, activation='relu')(h1)
outputs = layers.Dense(10, activation='softmax')(h2)
model = keras.Model(inputs, outputs) model.add_metric(keras.backend.std(inputs),
name='std_of_activation',
aggregation='mean')
model.add_loss(tf.reduce_sum(h1)*0.1) # keras.utils.plot_model(model, 'net001.png', show_shapes=True) model.compile(optimizer=keras.optimizers.RMSprop(),
loss=keras.losses.SparseCategoricalCrossentropy(),
metrics=[keras.metrics.SparseCategoricalAccuracy()])
model.fit(x_train, y_train, batch_size=32, epochs=1)
处理使用validation_data传入测试数据，还可以使用validation_split划分验证数据
ps:validation_split只能在用numpy数据训练的情况下使用
model.fit(x_train, y_train, batch_size=32, epochs=1, validation_split=0.2)
3.使用tf.data构造数据
def get_compiled_model():
inputs = keras.Input(shape=(784,), name='mnist_input')
h1 = layers.Dense(64, activation='relu')(inputs)
h2 = layers.Dense(64, activation='relu')(h1)
outputs = layers.Dense(10, activation='softmax')(h2)
model = keras.Model(inputs, outputs)
model.compile(optimizer=keras.optimizers.RMSprop(),
loss=keras.losses.SparseCategoricalCrossentropy(),
metrics=[keras.metrics.SparseCategoricalAccuracy()])
return model
model = get_compiled_model()
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
train_dataset = train_dataset.shuffle(buffer_size=1024).batch(64) val_dataset = tf.data.Dataset.from_tensor_slices((x_val, y_val))
val_dataset = val_dataset.batch(64) # model.fit(train_dataset, epochs=3)
# steps_per_epoch 每个epoch只训练几步
# validation_steps 每次验证，验证几步
model.fit(train_dataset, epochs=3, steps_per_epoch=100,
validation_data=val_dataset, validation_steps=3)
4.样本权重和类权重
“样本权重”数组是一个数字数组，用于指定批处理中每个样本在计算总损失时应具有多少权重。 它通常用于不平衡的分类问题（这个想法是为了给予很少见的类更多的权重）。 当使用的权重是1和0时，该数组可以用作损失函数的掩码（完全丢弃某些样本对总损失的贡献）。
“类权重”dict是同一概念的更具体的实例：它将类索引映射到应该用于属于该类的样本的样本权重。 例如，如果类“0”比数据中的类“1”少两倍，则可以使用class_weight = {0：1.，1：0.5}。
# 增加第5类的权重
import numpy as np
# 样本权重
model = get_compiled_model()
class_weight = {i:1.0 for i in range(10)}
class_weight[5] = 2.0
print(class_weight)
model.fit(x_train, y_train,
class_weight=class_weight,
batch_size=64,
epochs=4)
# 类权重
model = get_compiled_model()
sample_weight = np.ones(shape=(len(y_train),))
sample_weight[y_train == 5] = 2.0
model.fit(x_train, y_train,
sample_weight=sample_weight,
batch_size=64,
epochs=4)
5.多输入多输出模型
image_input = keras.Input(shape=(32, 32, 3), name='img_input')
timeseries_input = keras.Input(shape=(None, 10), name='ts_input') x1 = layers.Conv2D(3, 3)(image_input)
x1 = layers.GlobalMaxPooling2D()(x1) x2 = layers.Conv1D(3, 3)(timeseries_input)
x2 = layers.GlobalMaxPooling1D()(x2) x = layers.concatenate([x1, x2]) score_output = layers.Dense(1, name='score_output')(x)
class_output = layers.Dense(5, activation='softmax', name='class_output')(x) model = keras.Model(inputs=[image_input, timeseries_input],
outputs=[score_output, class_output])
keras.utils.plot_model(model, 'multi_input_output_model.png'
, show_shapes=True)
# 可以为模型指定不同的loss和metrics
model.compile(
optimizer=keras.optimizers.RMSprop(1e-3),
loss=[keras.losses.MeanSquaredError(),
keras.losses.CategoricalCrossentropy()]) # 还可以指定loss的权重
model.compile(
optimizer=keras.optimizers.RMSprop(1e-3),
loss={'score_output': keras.losses.MeanSquaredError(),
'class_output': keras.losses.CategoricalCrossentropy()},
metrics={'score_output': [keras.metrics.MeanAbsolutePercentageError(),
keras.metrics.MeanAbsoluteError()],
'class_output': [keras.metrics.CategoricalAccuracy()]},
loss_weight={'score_output': 2., 'class_output': 1.}) # 可以把不需要传播的loss置0
model.compile(
optimizer=keras.optimizers.RMSprop(1e-3),
loss=[None, keras.losses.CategoricalCrossentropy()]) # Or dict loss version
model.compile(
optimizer=keras.optimizers.RMSprop(1e-3),
loss={'class_output': keras.losses.CategoricalCrossentropy()})
6.使用回调
Keras中的回调是在训练期间（在epoch开始时，batch结束时，epoch结束时等）在不同点调用的对象，可用于实现以下行为：
在培训期间的不同时间点进行验证（超出内置的每个时期验证）
定期检查模型或超过某个精度阈值
在训练似乎平稳时改变模型的学习率
在训练似乎平稳时对顶层进行微调
在培训结束或超出某个性能阈值时发送电子邮件或即时消息通知等等。
可使用的内置回调有
ModelCheckpoint：定期保存模型。
EarlyStopping：当训练不再改进验证指标时停止培训。
TensorBoard：定期编写可在TensorBoard中显示的模型日志（更多细节见“可视化”）。
CSVLogger：将丢失和指标数据流式传输到CSV文件。
等等
6.1回调使用
model = get_compiled_model() callbacks = [
keras.callbacks.EarlyStopping(
# Stop training when `val_loss` is no longer improving
monitor='val_loss',
# "no longer improving" being defined as "no better than 1e-2 less"
min_delta=1e-2,
# "no longer improving" being further defined as "for at least 2 epochs"
patience=2,
verbose=1)
]
model.fit(x_train, y_train,
epochs=20,
batch_size=64,
callbacks=callbacks,
validation_split=0.2)
# checkpoint模型回调
model = get_compiled_model()
check_callback = keras.callbacks.ModelCheckpoint(
filepath='mymodel_{epoch}.h5',
save_best_only=True,
monitor='val_loss',
verbose=1
) model.fit(x_train, y_train,
epochs=3,
batch_size=64,
callbacks=[check_callback],
validation_split=0.2)
# 动态调整学习率
initial_learning_rate = 0.1
lr_schedule = keras.optimizers.schedules.ExponentialDecay(
initial_learning_rate,
decay_steps=10000,
decay_rate=0.96,
staircase=True
)
optimizer = keras.optimizers.RMSprop(learning_rate=lr_schedule)
# 使用tensorboard
tensorboard_cbk = keras.callbacks.TensorBoard(log_dir='./full_path_to_your_logs')
model.fit(x_train, y_train,
epochs=5,
batch_size=64,
callbacks=[tensorboard_cbk],
validation_split=0.2)
6.2创建自己的回调方法
class LossHistory(keras.callbacks.Callback):
def on_train_begin(self, logs):
self.losses = []
def on_epoch_end(self, batch, logs):
self.losses.append(logs.get('loss'))
print('\nloss:',self.losses[-1]) model = get_compiled_model() callbacks = [
LossHistory()
]
model.fit(x_train, y_train,
epochs=3,
batch_size=64,
callbacks=callbacks,
validation_split=0.2)
7.自己构造训练和验证循环
# Get the model.
inputs = keras.Input(shape=(784,), name='digits')
x = layers.Dense(64, activation='relu', name='dense_1')(inputs)
x = layers.Dense(64, activation='relu', name='dense_2')(x)
outputs = layers.Dense(10, activation='softmax', name='predictions')(x)
model = keras.Model(inputs=inputs, outputs=outputs) # Instantiate an optimizer.
optimizer = keras.optimizers.SGD(learning_rate=1e-3)
# Instantiate a loss function.
loss_fn = keras.losses.SparseCategoricalCrossentropy() # Prepare the training dataset.
batch_size = 64
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
train_dataset = train_dataset.shuffle(buffer_size=1024).batch(batch_size) # 自己构造循环
for epoch in range(3):
print('epoch: ', epoch)
for step, (x_batch_train, y_batch_train) in enumerate(train_dataset):
# 开一个gradient tape, 计算梯度
with tf.GradientTape() as tape:
logits = model(x_batch_train) loss_value = loss_fn(y_batch_train, logits)
grads = tape.gradient(loss_value, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables)) if step % 200 == 0:
print('Training loss (for one batch) at step %s: %s' % (step, float(loss_value)))
print('Seen so far: %s samples' % ((step + 1) * 64))
# 训练并验证
# Get model
inputs = keras.Input(shape=(784,), name='digits')
x = layers.Dense(64, activation='relu', name='dense_1')(inputs)
x = layers.Dense(64, activation='relu', name='dense_2')(x)
outputs = layers.Dense(10, activation='softmax', name='predictions')(x)
model = keras.Model(inputs=inputs, outputs=outputs) # Instantiate an optimizer to train the model.
optimizer = keras.optimizers.SGD(learning_rate=1e-3)
# Instantiate a loss function.
loss_fn = keras.losses.SparseCategoricalCrossentropy() # Prepare the metrics.
train_acc_metric = keras.metrics.SparseCategoricalAccuracy()
val_acc_metric = keras.metrics.SparseCategoricalAccuracy() # Prepare the training dataset.
batch_size = 64
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
train_dataset = train_dataset.shuffle(buffer_size=1024).batch(batch_size) # Prepare the validation dataset.
val_dataset = tf.data.Dataset.from_tensor_slices((x_val, y_val))
val_dataset = val_dataset.batch(64) # Iterate over epochs.
for epoch in range(3):
print('Start of epoch %d' % (epoch,)) # Iterate over the batches of the dataset.
for step, (x_batch_train, y_batch_train) in enumerate(train_dataset):
with tf.GradientTape() as tape:
logits = model(x_batch_train)
loss_value = loss_fn(y_batch_train, logits)
grads = tape.gradient(loss_value, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables)) # Update training metric.
train_acc_metric(y_batch_train, logits) # Log every 200 batches.
if step % 200 == 0:
print('Training loss (for one batch) at step %s: %s' % (step, float(loss_value)))
print('Seen so far: %s samples' % ((step + 1) * 64)) # Display metrics at the end of each epoch.
train_acc = train_acc_metric.result()
print('Training acc over epoch: %s' % (float(train_acc),))
# Reset training metrics at the end of each epoch
train_acc_metric.reset_states() # Run a validation loop at the end of each epoch.
for x_batch_val, y_batch_val in val_dataset:
val_logits = model(x_batch_val)
# Update val metrics
val_acc_metric(y_batch_val, val_logits)
val_acc = val_acc_metric.result()
val_acc_metric.reset_states()
print('Validation acc: %s' % (float(val_acc),))
## 添加自己构造的loss, 每次只能看到最新一次训练增加的loss
class ActivityRegularizationLayer(layers.Layer): def call(self, inputs):
self.add_loss(1e-2 * tf.reduce_sum(inputs))
return inputs inputs = keras.Input(shape=(784,), name='digits')
x = layers.Dense(64, activation='relu', name='dense_1')(inputs)
# Insert activity regularization as a layer
x = ActivityRegularizationLayer()(x)
x = layers.Dense(64, activation='relu', name='dense_2')(x)
outputs = layers.Dense(10, activation='softmax', name='predictions')(x) model = keras.Model(inputs=inputs, outputs=outputs)
logits = model(x_train[:64])
print(model.losses)
logits = model(x_train[:64])
logits = model(x_train[64: 128])
logits = model(x_train[128: 192])
print(model.losses)
# 将loss添加进求导中
optimizer = keras.optimizers.SGD(learning_rate=1e-3) for epoch in range(3):
print('Start of epoch %d' % (epoch,)) for step, (x_batch_train, y_batch_train) in enumerate(train_dataset):
with tf.GradientTape() as tape:
logits = model(x_batch_train)
loss_value = loss_fn(y_batch_train, logits) # Add extra losses created during this forward pass:
loss_value += sum(model.losses) grads = tape.gradient(loss_value, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables)) # Log every 200 batches.
if step % 200 == 0:
print('Training loss (for one batch) at step %s: %s' % (step, float(loss_value)))
print('Seen so far: %s samples' % ((step + 1) * 64))
如果还有问题未能得到解决，搜索887934385交流群，进入后下载资料工具安装包等。最后，感谢观看！
TensorFlow2.0教程-使用keras训练模型的更多相关文章
- 基于tensorflow2.0 使用tf.keras实现Fashion MNIST
本次使用的是2.0测试版,正式版估计会很快就上线了 tf2好像更新了蛮多东西 虽然教程不多 还是找了个试试 的确简单不少,但是还是比较喜欢现在这种写法 老样子先导入库 import tensorflo ...
- 一文上手Tensorflow2.0之tf.keras（三）
系列文章目录: Tensorflow2.0 介绍 Tensorflow 常见基本概念 从1.x 到2.0 的变化 Tensorflow2.0 的架构 Tensorflow2.0 的安装(CPU和GPU ...
- 用keras构建自己的网络层 TensorFlow2.0教程
1.构建一个简单的网络层 from __future__ import absolute_import, division, print_function import tensorflow as t ...
- 『TensorFlow2.0正式版』TF2.0+Keras速成教程·零：开篇简介与环境准备
此篇教程参考自TensorFlow 2.0 + Keras Crash Course,在原文的基础上进行了适当的总结与改编,以适应于国内开发者的理解与使用,水平有限,如果写的不对的地方欢迎大家评论指出 ...
- 『TensorFlow2.0正式版教程』极简安装TF2.0正式版（CPU&GPU）教程
0 前言 TensorFlow 2.0,今天凌晨,正式放出了2.0版本. 不少网友表示,TensorFlow 2.0比PyTorch更好用,已经准备全面转向这个新升级的深度学习框架了. 本篇文章就 ...
- TensorFlow2.0（11）：tf.keras建模三部曲
.caret, .dropup > .btn > .caret { border-top-color: #000 !important; } .label { border: 1px so ...
- TensorFlow2.0提示Cannot find reference 'keras' in __init__.py
使用TensorFlow2.0导入from tensorflow.keras import layers会出现Cannot find reference 'keras' in __init__.py提 ...
- TensorFlow模型部署到服务器---TensorFlow2.0
前言 当一个TensorFlow模型训练出来的时候,为了投入到实际应用,所以就需要部署到服务器上.由于我本次所做的项目是一个javaweb的图像识别项目.所有我就想去寻找一下java调用Tenso ...
- TensorFlow2.0（9）：TensorBoard可视化
.caret, .dropup > .btn > .caret { border-top-color: #000 !important; } .label { border: 1px so ...
随机推荐
- Step by Step Process of Migrating non-CDBs and PDBs Using ASM for File Storage (Doc ID 1576755.1)
Step by Step Process of Migrating non-CDBs and PDBs Using ASM for File Storage (Doc ID 1576755.1) AP ...
- element-ui 中Switch的用法
在element-ui中,如果你想知道Switch是开还是关,使用事件 @change="getchange(value2)" 它会输出true或者false.true代表的是开, ...
- 浅谈python中selenium库调动webdriver驱动浏览器的实现原理
最近学web自动化时用到selenium库,感觉很神奇,遂琢磨了一下,写了点心得. 当我们输入以下三行代码并执行时,会发现新打开了一个浏览器窗口并访问了百度首页,然而这是怎么做到的呢? from se ...
- IO 多路复用详解
转自:https://blog.csdn.net/sehanlingfeng/article/details/78920423
- 字典树(Trie)详解
详解字典树(Trie) 本篇随笔简单讲解一下信息学奥林匹克竞赛中的较为常用的数据结构--字典树.字典树也叫Trie树.前缀树.顾名思义,它是一种针对字符串进行维护的数据结构.并且,它的用途超级广泛.建 ...
- laravel实现多模块
一.这里使用Caffienate Modules 网址:modules maintained by caffeinated 二.根据自己的版本选择包的版本 三.在项目composer.json文件中加 ...
- PAT 1011 World Cup Betting 查找元素
With the 2010 FIFA World Cup running, football fans the world over were becoming increasingly excite ...
- 通过idea将maven工程转为web项目
前言 吐槽一下网上的各种转换教程..说的真的是吵来吵去,有用的东西极少.特此自己写一篇好使的. 转换过程 建好的maven工程 建好的maven工程长这个鬼样子~~,没有使用骨架.就是普通的建立方式. ...
- Spring Boot 2 快速教程：WebFlux 快速入门（二）
摘要: 原创出处 https://www.bysocket.com 「公众号:泥瓦匠BYSocket 」欢迎关注和转载,保留摘要,谢谢! 02:WebFlux 快速入门实践 文章工程: JDK 1.8 ...
- 目前为止最简洁的C#文件夹Copy代码,不接受反驳
private static void CopyEntireDir(string sourcePath, string destPath) { foreach (string dirPath in D ...