TensorFlow搭建模型方式总结
文章目录
- 引言
- 基于Keras Sequential API搭建模型
- 基于Keras 函数API搭建模型
-
- 简单实例
- 多输入实例
- 多输出实例
- 子类化API
- 参考
引言
TensorFlow提供了多种API，使得入门者和专家可以根据自己的需求选择不同的API搭建模型。
基于Keras Sequential API搭建模型
Sequential适用于线性堆叠的方式搭建模型，即每层只有一个输入和输出。
import tensorflow as tf
# 导入手写数字数据集
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# 数据标准化
x_train, x_test = x_train/255, x_test/255
# 使用Sequential搭建模型
# 方式一
model = tf.keras.models.Sequential([
# 加入CNN层(2D), 使用了3个卷积核, 卷积核的尺寸为3X3, 步长为1, 输入图像的维度为28X28X1
tf.keras.layers.Conv2D(3, kernel_size=3, strides=1, input_shape=(28, 28, 1)),
# 加入激活函数
tf.keras.layers.Activation('relu'),
# 加入2X2池化层, 步长为2
tf.keras.layers.MaxPool2D(pool_size=2, strides=2),
# 把图像数据平铺
tf.keras.layers.Flatten(),
# 加入全连接层, 设置神经元为128个, 设置relu激活函数
tf.keras.layers.Dense(128, activation='relu'),
# 加入全连接层(输出层), 设置输出数量为10, 设置softmax激活函数
tf.keras.layers.Dense(10, activation='softmax')
])
# 方式二
model2 = tf.keras.models.Sequential()
model2.add(tf.keras.layers.Conv2D(3, kernel_size=3, strides=1, input_shape=(28, 28, 1)))
model2.add(tf.keras.layers.Activation('relu'))
model2.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))
model2.add(tf.keras.layers.Flatten())
model2.add(tf.keras.layers.Dense(128, activation='relu'))
model2.add(tf.keras.layers.Dense(10, activation='softmax'))
# 模型概览
model.summary()
"""
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 26, 26, 3) 30
activation (Activation) (None, 26, 26, 3) 0
max_pooling2d (MaxPooling2D (None, 13, 13, 3) 0
)
flatten (Flatten) (None, 507) 0
dense (Dense) (None, 128) 65024
dense_1 (Dense) (None, 10) 1290
=================================================================
Total params: 66,344
Trainable params: 66,344
"""
# 编译 为模型加入优化器, 损失函数, 评估指标
model.compile(
optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy']
)
# 训练模型, 2个epoch, batch size为100
model.fit(x_train, y_train, epochs=2, batch_size=100)
基于Keras 函数API搭建模型
由于Sequential是线性堆叠的，只有一个输入和输出，但是当我们需要搭建多输入模型时，如输入图片、文本描述等，这几类信息可能需要分别使用CNN，RNN模型提取信息，然后汇总信息到最后的神经网络中预测输出。或者是多输出任务，如根据音乐预测音乐类型和发行时间。亦或是一些非线性的拓扑网络结构模型，如使用残差链接、Inception等。上述这些情况的网络都不是线性搭建，要搭建如此复杂的网络，需要使用函数API来搭建。
简单实例
import tensorflow as tf
# 导入手写数字数据集
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# 数据标准化
x_train, x_test = x_train/255, x_test/255
input_tensor = tf.keras.layers.Input(shape=(28, 28, 1))
# CNN层(2D), 使用了3个卷积核, 卷积核的尺寸为3X3, 步长为1, 输入图像的维度为28X28X1
x = tf.keras.layers.Conv2D(3, kernel_size=3, strides=1)(input_tensor)
# 激活函数
x = tf.keras.layers.Activation('relu')(x)
# 2X2池化层, 步长为2
x = tf.keras.layers.MaxPool2D(pool_size=2, strides=2)(x)
# 把图像数据平铺
x = tf.keras.layers.Flatten()(x)
# 全连接层, 设置神经元为128个, 设置relu激活函数
x = tf.keras.layers.Dense(128, activation='relu')(x)
# 全连接层(输出层), 设置输出数量为10, 设置softmax激活函数
output = tf.keras.layers.Dense(10, activation='softmax')(x)
model = tf.keras.models.Model(input_tensor, output)
# 模型概览
model.summary()
"""
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 28, 28, 1)] 0
conv2d (Conv2D) (None, 26, 26, 3) 30
activation (Activation) (None, 26, 26, 3) 0
max_pooling2d (MaxPooling2D (None, 13, 13, 3) 0
)
flatten (Flatten) (None, 507) 0
dense (Dense) (None, 128) 65024
dense_1 (Dense) (None, 10) 1290
=================================================================
Total params: 66,344
Trainable params: 66,344
Non-trainable params: 0
_________________________________________________________________
"""
# 编译 为模型加入优化器, 损失函数, 评估指标
model.compile(
optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy']
)
# 训练模型, 2个epoch, batch size为100
model.fit(x_train, y_train, epochs=2, batch_size=100)
多输入实例
import tensorflow as tf
# 输入1
input_tensor1 = tf.keras.layers.Input(shape=(28,))
x1 = tf.keras.layers.Dense(16, activation='relu')(input_tensor1)
output1 = tf.keras.layers.Dense(32, activation='relu')(x1)
# 输入2
input_tensor2 = tf.keras.layers.Input(shape=(28,))
x2 = tf.keras.layers.Dense(16, activation='relu')(input_tensor2)
output2 = tf.keras.layers.Dense(32, activation='relu')(x2)
# 合并输入1和输入2
concat = tf.keras.layers.concatenate([output1, output2])
# 顶层分类模型
output = tf.keras.layers.Dense(10, activation='relu')(concat)
model = tf.keras.models.Model([input_tensor1, input_tensor2], output)
# 编译
model.compile(
optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy']
)
多输出实例
import tensorflow as tf
# 输入
input_tensor = tf.keras.layers.Input(shape=(28,))
x = tf.keras.layers.Dense(16, activation='relu')(input_tensor)
output = tf.keras.layers.Dense(32, activation='relu')(x)
# 多个输出
output1 = tf.keras.layers.Dense(10, activation='relu')(output)
output2 = tf.keras.layers.Dense(1, activation='sigmoid')(output)
model = tf.keras.models.Model(input_tensor, [output1, output2])
# 编译
model.compile(
optimizer='adam',
loss=['sparse_categorical_crossentropy', 'binary_crossentropy'],
metrics=['accuracy']
)
子类化API
相较于上述使用高阶API，使用子类化API的方式来搭建模型，可以根据需求对模型中的任何一部分进行修改。
import tensorflow as tf
# 导入手写数字数据集
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# 数据标准化
x_train, x_test = x_train / 255, x_test / 255
train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(buffer_size=10).batch(32)
test_data = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)
class MyModel(tf.keras.Model):
def __init__(self):
super(MyModel, self).__init__()
self.flatten = tf.keras.layers.Flatten()
self.hidden_layer1 = tf.keras.layers.Dense(16, activation='relu')
self.hidden_layer2 = tf.keras.layers.Dense(10, activation='softmax')
# 定义模型
def call(self, x):
h = self.flatten(x)
h = self.hidden_layer1(h)
y = self.hidden_layer2(h)
return y
model = MyModel()
# 损失函数 和 优化器
loss_function = tf.keras.losses.SparseCategoricalCrossentropy()
optimizer = tf.keras.optimizers.Adam()
# 评估指标
train_loss = tf.keras.metrics.Mean() # 一个epoch的loss
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy() # 一个epoch的准确率
test_loss = tf.keras.metrics.Mean()
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()
@tf.function
def train_step(x, y):
with tf.GradientTape() as tape:
y_pre = model(x)
loss = loss_function(y, y_pre)
grad = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(grad, model.trainable_variables))
train_loss(loss)
train_accuracy(y, y_pre)
@tf.function
def test_step(x, y):
y_pre = model(x)
te_loss = loss_function(y, y_pre)
test_loss(te_loss)
test_accuracy(y, y_pre)
epoch = 2
for i in range(epoch):
# 重置评估指标
train_loss.reset_states()
train_accuracy.reset_states()
# 按照batch size 进行训练
for x, y in train_data:
train_step(x, y)
print(f'epoch {i+1} train loss {train_loss.result()} train accuracy {train_accuracy.result()}')
参考
TensorFlow官方文档