TensorFlow2.0学习笔记-4.模型的自定义
4.模型的自定义
4.1.自定义层
使用的主要数据结构是Layer
实现自定义层的最佳方法是扩展tf.keras.layers.Layer类并实现：
• __init__ ：可以在其中进行所有与输入无关的初始化，定义相关的层
• build： 知道输入张量的形状并可以进行其余的初始化
• call： 在这里进行前向传播
注意：不一定需要在build中创建变量时，也可以在__init__中创建它们。
tf.keras.Model和tf.keras.layers.Layer有什么区别和联系？
• 通过继承 tf.keras.Model 编写自己的模型类
• 通过继承 tf.keras.layers.Layer 编写自己的层
• tf.keras中的模型和层都是继承tf.Module实现的
• tf.keras.Model继承tf.keras.layers.Layer实现的
tf.Module： 定位为一个轻量级的状态容器，因为可以收集变量，所以这个类型可以用来建模，配合tf.GradientTape使用。
自定义一个线性回归模型
使用鸢尾花数据集
from sklearn import datasets
iris = datasets.load_iris()
data = iris.data
target = iris.target
data.shape # x
(150, 4)
target.shape #y
(150,)
方法1：最基础的方法
import tensorflow as tf
#自定义全连接层
class Linear(tf.keras.layers.Layer):
def __init__(self, units=1, input_dim=4):
super(Linear, self).__init__() #
w_init = tf.random_normal_initializer()
self.w = tf.Variable(initial_value=w_init(shape=(input_dim, units), dtype='float32'), trainable=True)
b_init = tf.zeros_initializer()
self.b = tf.Variable(initial_value=b_init(shape=(units,),dtype='float32'),trainable=True)
def call(self, inputs):
return tf.matmul(inputs, self.w) + self.b
x = tf.constant(data) #(150,4)
linear_layer = Linear(units = 1, input_dim=4) #()
y = linear_layer(x)
print(y.shape) #(150,1)
(150, 1)
方法2：使用self.add_weight创建变量
class Linear(tf.keras.layers.Layer):
def __init__(self, units=1, input_dim=4):
super(Linear, self).__init__()
self.w = self.add_weight(shape=(input_dim, units),
initializer='random_normal',
trainable=True)
self.b = self.add_weight(shape=(units,),
initializer='zeros',
trainable=True)
def call(self, inputs):
return tf.matmul(inputs, self.w) + self.b
x = tf.constant(data)
linear_layer = Linear(units = 1, input_dim=4)
y = linear_layer(x)
print(y.shape)
(150, 1)
方法三：build函数中创建变量
class Linear(tf.keras.layers.Layer):
def __init__(self, units=32):
super(Linear, self).__init__()
self.units = units
def build(self, input_shape): #(150,4)
self.w = self.add_weight(shape=(input_shape[-1], self.units),
initializer='random_normal',
trainable=True)
self.b = self.add_weight(shape=(self.units,),
initializer='random_normal',
trainable=True)
super(Linear,self).build(input_shape)
def call(self, inputs):
return tf.matmul(inputs, self.w) + self.b
x = tf.constant(data) #150*4
linear_layer = Linear(units = 1)
y = linear_layer(x)
print(y.shape)
(150, 1)
添加不可训练的参数
class Linear(tf.keras.layers.Layer):
def __init__(self, units=32):
super(Linear, self).__init__()
self.units = units
def build(self, input_shape):
self.w = self.add_weight(shape=(input_shape[-1], self.units),
initializer='random_normal',
trainable=True)
self.b = self.add_weight(shape=(self.units,),
initializer='random_normal',
trainable=False)
super(Linear,self).build(input_shape)
def call(self, inputs):
return tf.matmul(inputs, self.w) + self.b
x = tf.constant(data)
linear_layer = Linear(units = 1)
y = linear_layer(x)
print(y.shape)
(150, 1)
# 打印所有参数、不可训练参数、可训练参数
print('weight:', linear_layer.weights)
print('non-trainable weight:', linear_layer.non_trainable_weights)
print('trainable weight:', linear_layer.trainable_weights)
weight: [<tf.Variable 'linear_4/Variable:0' shape=(4, 1) dtype=float32, numpy=
array([[ 0.00276536],
[-0.07950259],
[ 0.01646506],
[ 0.00197834]], dtype=float32)>, <tf.Variable 'linear_4/Variable:0' shape=(1,) dtype=float32, numpy=array([0.00255805], dtype=float32)>]
non-trainable weight: [<tf.Variable 'linear_4/Variable:0' shape=(1,) dtype=float32, numpy=array([0.00255805], dtype=float32)>]
trainable weight: [<tf.Variable 'linear_4/Variable:0' shape=(4, 1) dtype=float32, numpy=
array([[ 0.00276536],
[-0.07950259],
[ 0.01646506],
[ 0.00197834]], dtype=float32)>]
自定义层的注意事项
如果需要保存模型，则在自定义网络层时需要重写get_config 方法
我们主要看传入__init__接口时有哪些配置参数，然后在get_config内一一的将它们转为字典键值并且返回使用
get_config的作用：获取该层的参数配置，以便模型保存时使用
自定义层的biuld 中创建初始矩阵时， 需要添加name属性
我们在实现自定义网络层时，最好统一在初始化时传入可变参数**kwargs，这是因为在model推理时，有时我们需要对所有构成该模型的网络层进行统一的传参。
import tensorflow as tf
#Dense
class MyDense(tf.keras.layers.Layer):
def __init__(self, units=32, **kwargs):
self.units = units
super(MyDense, self).__init__(**kwargs)
#build方法一般定义Layer需要被训练的参数。
def build(self, input_shape):
self.w = self.add_weight(shape=(input_shape[-1], self.units),
initializer='random_normal',
trainable=True,
name='w')
self.b = self.add_weight(shape=(self.units,),
initializer='random_normal',
trainable=True,
name='b')
super(MyDense,self).build(input_shape) # 相当于设置self.built = True
#call方法一般定义正向传播运算逻辑，__call__方法调用了它。
def call(self, inputs):
return tf.matmul(inputs, self.w) + self.b
#如果要让自定义的Layer可以序列化，需要自定义get_config方法。
def get_config(self):
config = super(MyDense, self).get_config()
config.update({'units': self.units})
return config
from sklearn import datasets
iris = datasets.load_iris()
data = iris.data
labels = iris.target
#网络 函数式构建的网络
inputs = tf.keras.Input(shape=(4,))
x = MyDense(units=16)(inputs)
x = tf.nn.tanh(x)
x = MyDense(units=3)(x) #0,1,2
predictions = tf.nn.softmax(x)
model = tf.keras.Model(inputs=inputs, outputs=predictions)
import numpy as np
data = np.concatenate((data,labels.reshape(150,1)),axis=-1)
np.random.shuffle(data)
labels = data[:,-1]
data = data[:,:4]
#优化器 Adam
#损失函数 交叉熵损失函数
#评估函数 #acc
model.compile(optimizer=tf.keras.optimizers.Adam(),
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
#keras
model.fit(data, labels, batch_size=32, epochs=100,shuffle=True)
Train on 150 samples
Epoch 1/100
150/150 [==============================] - 1s 7ms/sample - loss: 1.0939 - sparse_categorical_accuracy: 0.5333
...
Epoch 100/100
150/150 [==============================] - 0s 133us/sample - loss: 0.6751 - sparse_categorical_accuracy: 0.9733
model.save('keras_model_tf_version.h5')
当我们自定义网络层并且有效保存模型后，希望使用tf.keras.models.load_model进行模型加载时， 首先，建立一个字典，该字典的键是自定义网络层时设定该层的名字，其值为
该自定义网络层的类名，该字典将用于加载模型时使用！
然后，在tf.keras.models.load_model内传入custom_objects告知如何解析重建自定义网络层。
_custom_objects = {
"MyDense" : MyDense,
}
new_model = tf.keras.models.load_model("keras_model_tf_version.h5",custom_objects=_custom_objects)
y_pred = new_model.predict(data)
np.argmax(y_pred,axis=1)
array([2, 1, 0, 0, 0, 2, 1, 0, 0, 0, 2, 0, 2, 0, 0, 2, 0, 0, 2, 2, 0, 0,
0, 2, 0, 0, 2, 0, 2, 0, 2, 2, 2, 1, 2, 2, 2, 0, 1, 1, 1, 1, 2, 1,
2, 2, 1, 0, 2, 0, 2, 1, 1, 1, 2, 2, 1, 1, 0, 0, 1, 0, 1, 1, 2, 1,
1, 2, 2, 0, 1, 1, 0, 2, 0, 1, 0, 0, 2, 2, 1, 1, 2, 2, 1, 2, 0, 0,
1, 0, 2, 2, 0, 2, 0, 1, 2, 1, 1, 2, 2, 0, 1, 0, 1, 0, 0, 1, 0, 1,
1, 0, 2, 2, 1, 2, 1, 0, 1, 1, 0, 2, 1, 0, 0, 1, 0, 2, 2, 1, 1, 2,
1, 2, 1, 2, 2, 0, 2, 2, 0, 2, 2, 2, 2, 0, 1, 0, 1, 0], dtype=int64)
labels
array([2., 1., 0., 0., 0., 2., 1., 0., 0., 0., 2., 0., 2., 0., 0., 2., 0.,
0., 2., 2., 0., 0., 0., 2., 0., 0., 1., 0., 2., 0., 2., 2., 2., 1.,
1., 2., 2., 0., 1., 1., 1., 1., 2., 1., 2., 2., 1., 0., 2., 0., 2.,
1., 1., 1., 2., 2., 1., 1., 0., 0., 1., 0., 1., 1., 2., 1., 1., 2.,
2., 0., 1., 1., 0., 2., 0., 1., 0., 0., 2., 2., 1., 1., 2., 2., 1.,
2., 0., 0., 1., 0., 2., 2., 0., 2., 0., 1., 2., 1., 1., 2., 2., 0.,
1., 0., 1., 0., 0., 1., 0., 1., 1., 0., 2., 2., 1., 2., 1., 0., 1.,
1., 0., 2., 1., 0., 0., 1., 0., 2., 2., 1., 1., 1., 1., 1., 1., 2.,
2., 0., 2., 2., 0., 2., 2., 2., 2., 0., 1., 0., 1., 0.])
4.2.损失函数
常用损失函数：
• mean_squared_error（平方差误差损失，用于回归，简写为 mse, 类实现形式为MeanSquaredError 和 MSE）
• binary_crossentropy(二元交叉熵，用于二分类，类实现形式为 BinaryCrossentropy)
• categorical_crossentropy(类别交叉熵，用于多分类，要求label为onehot编码，类实现形式为 CategoricalCrossentropy)
• sparse_categorical_crossentropy(稀疏类别交叉熵，用于多分类，要求label为序号编码形式，类实现形式为 SparseCategoricalCrossentropy)
自定义损失函数，两种方法自定义函数：
函数的实现形式
def MeanSquaredError(y_true, y_pred):
return tf.reduce_mean(tf.square(y_pred - y_true))
类的实现形式：
class MeanSquaredError(tf.keras.losses.Loss):
def call(self, y_true, y_pred):
return tf.reduce_mean(tf.square(y_pred - y_true))
MNIST数据集案例
MNIST是一个入门级的计算机视觉数据集，它包含各种手写数字图片，如图：
它也包含每一张图片对应的标签，告诉我们这个是数字几。比如，第一行这10张图片的标签分别是0， 4， 1， 9， 2，1， 3， 1， 4， 3。
每一张图都是由（28， 28， 1）的矩阵组成：
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model
import numpy as np
mnist = np.load("mnist.npz")
x_train, y_train, x_test, y_test = mnist['x_train'],mnist['y_train'],mnist['x_test'],mnist['y_test']
x_train.shape
(60000, 28, 28)
x_test.shape
(10000, 28, 28)
x_train, x_test = x_train / 255.0, x_test / 255.0
# 数据可视化
import matplotlib.pyplot as plt
fig, ax = plt.subplots(
nrows=2,
ncols=5,
sharex=True,
sharey=True, )
ax = ax.flatten()
for i in range(10):
img = x_train[y_train == i][0].reshape(28, 28)
ax[i].imshow(img, cmap='Greys', interpolation='nearest')
ax[0].set_xticks([])
ax[0].set_yticks([])
plt.tight_layout()
plt.show()
# Add a channels dimension
x_train = x_train[..., tf.newaxis]
x_test = x_test[..., tf.newaxis]
y_train = tf.one_hot(y_train,depth=10)
y_test = tf.one_hot(y_test,depth=10)
train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(32)
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)
class MyModel(Model):
def __init__(self):
super(MyModel, self).__init__()
self.conv1 = Conv2D(32, 3, activation='relu')
self.flatten = Flatten()
self.d1 = Dense(128, activation='relu')
self.d2 = Dense(10, activation='softmax')
def call(self, x):
x = self.conv1(x)
x = self.flatten(x)
x = self.d1(x)
return self.d2(x)
model = MyModel()
loss_object = tf.keras.losses.CategoricalCrossentropy()
optimizer = tf.keras.optimizers.Adam()
# 选择衡量指标来度量模型的损失值（loss）和准确率（accuracy）
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.CategoricalAccuracy(name='train_accuracy')
test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.CategoricalAccuracy(name='test_accuracy')
@tf.function
def train_step(images, labels):
with tf.GradientTape() as tape:
predictions = model(images)
loss = loss_object(labels, predictions)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
train_loss(loss)
train_accuracy(labels, predictions)
@tf.function
def test_step(images, labels):
predictions = model(images)
t_loss = loss_object(labels, predictions)
test_loss(t_loss)
test_accuracy(labels, predictions)
EPOCHS = 5
for epoch in range(EPOCHS):
# 在下一个epoch开始时，重置评估指标
train_loss.reset_states()
train_accuracy.reset_states()
test_loss.reset_states()
test_accuracy.reset_states()
for images, labels in train_ds:
train_step(images, labels)
for test_images, test_labels in test_ds:
test_step(test_images, test_labels)
template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'
print(template.format(epoch + 1,
train_loss.result(),
train_accuracy.result() * 100,
test_loss.result(),
test_accuracy.result() * 100))
Epoch 1, Loss: 1.0062854290008545, Accuracy: 88.1866683959961, Test Loss: 0.899224042892456, Test Accuracy: 97.05000305175781
Epoch 2, Loss: 0.8931533098220825, Accuracy: 97.54166412353516, Test Loss: 0.8904874920845032, Test Accuracy: 97.68000030517578
Epoch 3, Loss: 0.8832218647003174, Accuracy: 98.30166625976562, Test Loss: 0.8857290744781494, Test Accuracy: 98.02999877929688
Epoch 4, Loss: 0.879651665687561, Accuracy: 98.54666900634766, Test Loss: 0.8816375732421875, Test Accuracy: 98.4000015258789
Epoch 5, Loss: 0.8753460049629211, Accuracy: 98.89666748046875, Test Loss: 0.8853973746299744, Test Accuracy: 98.06999969482422
4.3.评估函数
常用评估函数：
回归相关评估函数
• tf.keras.metrics.MeanSquaredError （平方差误差，用于回归，可以简写为MSE，函数形式为mse）
• tf.keras.metrics.MeanAbsoluteError (绝对值误差，用于回归，可以简写为MAE，函数形式为mae)
• tf.keras.metrics.MeanAbsolutePercentageError (平均百分比误差，用于回归，可以简写为MAPE，函数形式为mape)
• tf.keras.metrics.RootMeanSquaredError (均方根误差，用于回归)
分类相关评估函数
• tf.keras.metrics.Accuracy (准确率，用于分类，可以用字符串"Accuracy"表示，Accuracy=(TP+TN)/(TP+TN+FP+FN)，要求y_true和y_pred都为类别序号编码)
• tf.keras.metrics.AUC (ROC曲线(TPR vs FPR)下的面积，用于二分类，直观解释为随机抽取一个正样本和一个负样本，正样本的预测值大于负样本的概率)
• tf.keras.metrics.Precision (精确率，用于二分类， Precision = TP/(TP+FP))
• tf.keras.metrics.Recall (召回率，用于二分类， Recall = TP/(TP+FN))
• tf.keras.metrics.TopKCategoricalAccuracy(多分类TopK准确率，要求y_true(label)为onehot编码形式)
• tf.keras.metrics.CategoricalAccuracy（分类准确率，与Accuracy含义相同，要求y_true(label)为onehot编码形式）
• tf.keras.metrics. SparseCategoricalAccuracy (稀疏分类准确率，与Accuracy含义相同，要求y_true(label)为序号编码形式)
更多参考：
https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/metrics
案例：
import tensorflow as tf
m=tf.keras.metrics.Accuracy()
m.update_state([1,2,3,4],[0,2,3,4])
# 可简写为
# m([1,2,3,4],[0,2,3,1])
print('Final result: ',m.result().numpy())# Final result: 0.75
m.update_state([1,2,3,4],[0,2,3,1])
print('Final result: ',m.result().numpy())
Final result: 0.75
Final result: 0.625
m.reset_states()
m.update_state([1,2,3,4],[0,2,3,4])
print('Final result: ',m.result().numpy())
Final result: 0.75
自定义评估函数
两种实现形式： 基于类的实现和基于函数的实现，大部分使用基于类的实现
自定义评估指标需要继承 tf.keras.metrics.Metric 类，并重写 __init__ 、update_state 和 result 三个方法。
• __init__():所有状态变量都应通过以下方法在此方法中创建self.add_weight()
• update_state(): 对状态变量进行所有更新
• result(): 根据状态变量计算并返回指标值。
案例：
class SparseCategoricalAccuracy_(tf.keras.metrics.Metric):
def __init__(self, name='SparseCategoricalAccuracy', **kwargs):
super(SparseCategoricalAccuracy_, self).__init__(name=name, **kwargs)
self.total = self.add_weight(name='total', dtype=tf.int32, initializer=tf.zeros_initializer())
self.count = self.add_weight(name='count', dtype=tf.int32, initializer=tf.zeros_initializer())
def update_state(self, y_true, y_pred,sample_weight=None):
values = tf.cast(tf.equal(y_true, tf.argmax(y_pred, axis=-1, output_type=tf.int32)), tf.int32)
self.total.assign_add(tf.shape(y_true)[0])
self.count.assign_add(tf.reduce_sum(values))
def result(self):
return self.count / self.total
def reset_states(self):
# The state of the metric will be reset at the start of each epoch.
self.total.assign(0)
self.count.assign(0)
# 利用自定义评估函数进行评估
s = SparseCategoricalAccuracy_()
# s.reset_states()
s.update_state(tf.constant([2, 1]), tf.constant([[0.1, 0.9, 0.8], [0.05, 0.95, 0]]))
print('Final result: ', s.result().numpy()) # Final result: 0.5
Final result: 0.5
# 利用官方评估函数进行评估
m = tf.keras.metrics.SparseCategoricalAccuracy()
m.update_state([2,1], [[0.1, 0.9, 0.8], [0.05, 0.95, 0]])
print('Final result: ', m.result().numpy()) # Final result: 0.5
Final result: 0.5
class CatgoricalTruePositives(tf.keras.metrics.Metric):
def __init__(self, name='categorical_true_positives', **kwargs):
super(CatgoricalTruePositives, self).__init__(name=name, **kwargs)
self.true_positives = self.add_weight(name='tp', initializer='zeros')
def update_state(self, y_true, y_pred, sample_weight=None):
y_pred = tf.argmax(y_pred,axis=-1)
values = tf.equal(tf.cast(y_true, 'int32'), tf.cast(y_pred, 'int32'))
values = tf.cast(values, 'float32')
if sample_weight is not None:
sample_weight = tf.cast(sample_weight, 'float32')
values = tf.multiply(values, sample_weight)
self.true_positives.assign_add(tf.reduce_sum(values))
def result(self):
return self.true_positives
def reset_states(self):
# The state of the metric will be reset at the start of each epoch.
self.true_positives.assign(0.)
y_pred = tf.nn.softmax(tf.random.uniform((4,3)))
tf.argmax(y_pred,axis=-1)
<tf.Tensor: id=19, shape=(4,), dtype=int64, numpy=array([1, 2, 1, 0], dtype=int64)>
y_true = tf.constant([2,0,0,0])
m=CatgoricalTruePositives()
m.update_state(y_true,y_pred)
print('Final result: ',m.result().numpy())
Final result: 1.0
自定义评估函数在MNIST数据集中的使用
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model
import numpy as np
mnist = np.load("mnist.npz")
x_train, y_train, x_test, y_test = mnist['x_train'],mnist['y_train'],mnist['x_test'],mnist['y_test']
x_train, x_test = x_train / 255.0, x_test / 255.0
import matplotlib.pyplot as plt
fig, ax = plt.subplots(
nrows=2,
ncols=5,
sharex=True,
sharey=True, )
ax = ax.flatten()
for i in range(10):
img = x_train[y_train == i][0].reshape(28, 28)
ax[i].imshow(img, cmap='Greys', interpolation='nearest')
ax[0].set_xticks([])
ax[0].set_yticks([])
plt.tight_layout()
plt.show()
# Add a channels dimension
x_train = x_train[..., tf.newaxis]
x_test = x_test[..., tf.newaxis]
train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(32)
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)
class MyModel(Model):
def __init__(self):
super(MyModel, self).__init__()
self.conv1 = Conv2D(32, 3, activation='relu')
self.flatten = Flatten()
self.d1 = Dense(128, activation='relu')
self.d2 = Dense(10, activation='softmax')
def call(self, x):
x = self.conv1(x)
x = self.flatten(x)
x = self.d1(x)
return self.d2(x)
#返回的是一个正确的个数
class CatgoricalTruePositives(tf.keras.metrics.Metric):
def __init__(self, name='categorical_true_positives', **kwargs):
super(CatgoricalTruePositives, self).__init__(name=name, **kwargs)
self.true_positives = self.add_weight(name='tp', initializer='zeros')
def update_state(self, y_true, y_pred, sample_weight=None):
y_pred = tf.argmax(y_pred,axis=-1)
values = tf.equal(tf.cast(y_true, 'int32'), tf.cast(y_pred, 'int32'))
values = tf.cast(values, 'float32')
if sample_weight is not None:
sample_weight = tf.cast(sample_weight, 'float32')
values = tf.multiply(values, sample_weight)
self.true_positives.assign_add(tf.reduce_sum(values))
def result(self):
return self.true_positives
def reset_states(self):
self.true_positives.assign(0.)
model = MyModel()
loss_object = tf.keras.losses.SparseCategoricalCrossentropy() #损失函数
optimizer = tf.keras.optimizers.Adam() #优化器
#评估函数
train_loss = tf.keras.metrics.Mean(name='train_loss') #loss
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy') #准确率
train_tp = CatgoricalTruePositives(name="train_tp") #返回正确的个数
test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')
test_tp = CatgoricalTruePositives(name='test_tp')
@tf.function
def train_step(images, labels):
with tf.GradientTape() as tape:
predictions = model(images)
loss = loss_object(labels, predictions)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
#评估函数的结果
train_loss(loss)
train_accuracy(labels, predictions)
train_tp(labels, predictions)
@tf.function
def test_step(images, labels):
predictions = model(images)
t_loss = loss_object(labels, predictions)
test_loss(t_loss)
test_accuracy(labels, predictions)
test_tp(labels, predictions)
EPOCHS = 5
for epoch in range(EPOCHS):
# 在下一个epoch开始时，重置评估指标
train_loss.reset_states()
train_accuracy.reset_states()
train_tp.reset_states()
test_loss.reset_states()
test_accuracy.reset_states()
test_tp.reset_states()
for images, labels in train_ds:
train_step(images, labels)
for test_images, test_labels in test_ds:
test_step(test_images, test_labels)
template = 'Epoch {}, Loss: {}, Accuracy: {}, TP: {},Test Loss: {}, Test Accuracy: {}, Test TP:{}'
print(template.format(epoch + 1,
train_loss.result(),
train_accuracy.result() * 100,
train_tp.result(),
test_loss.result(),
test_accuracy.result() * 100,
test_tp.result()))
Epoch 1, Loss: 0.1417243927717209, Accuracy: 95.7750015258789, TP: 57465.0,Test Loss: 0.06078110635280609, Test Accuracy: 97.94999694824219, Test TP:9795.0
Epoch 2, Loss: 0.04365191608667374, Accuracy: 98.64833068847656, TP: 59189.0,Test Loss: 0.052343666553497314, Test Accuracy: 98.29000091552734, Test TP:9829.0
Epoch 3, Loss: 0.023991659283638, Accuracy: 99.23333740234375, TP: 59540.0,Test Loss: 0.05575888603925705, Test Accuracy: 98.22000122070312, Test TP:9822.0
Epoch 4, Loss: 0.014321192167699337, Accuracy: 99.52832794189453, TP: 59717.0,Test Loss: 0.056586846709251404, Test Accuracy: 98.3699951171875, Test TP:9837.0
Epoch 5, Loss: 0.00959738902747631, Accuracy: 99.67166900634766, TP: 59803.0,Test Loss: 0.05430058762431145, Test Accuracy: 98.5199966430664, Test TP:9852.0
自定义评估函数加入model.fit中
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model
import numpy as np
mnist = np.load("mnist.npz")
x_train, y_train, x_test, y_test = mnist['x_train'],mnist['y_train'],mnist['x_test'],mnist['y_test']
x_train, x_test = x_train / 255.0, x_test / 255.0
import matplotlib.pyplot as plt
fig, ax = plt.subplots(
nrows=2,
ncols=5,
sharex=True,
sharey=True, )
ax = ax.flatten()
for i in range(10):
img = x_train[y_train == i][0].reshape(28, 28)
ax[i].imshow(img, cmap='Greys', interpolation='nearest')
ax[0].set_xticks([])
ax[0].set_yticks([])
plt.tight_layout()
plt.show()
# Add a channels dimension
x_train = x_train[..., tf.newaxis]
x_test = x_test[..., tf.newaxis]
# 使用model.fit最好使用one_hot，不要使用Sparse
y_train = tf.one_hot(y_train,depth=10)
y_test = tf.one_hot(y_test,depth=10)
train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(32)
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).shuffle(100).batch(32)
class MyModel(Model):
def __init__(self):
super(MyModel, self).__init__()
self.conv1 = Conv2D(32, 3, activation='relu')
self.flatten = Flatten()
self.d1 = Dense(128, activation='relu')
self.d2 = Dense(10, activation='softmax')
def call(self, x):
x = self.conv1(x)
x = self.flatten(x)
x = self.d1(x)
return self.d2(x)
#返回的是一个正确的个数
class CatgoricalTruePositives(tf.keras.metrics.Metric):
def __init__(self, name='categorical_true_positives', **kwargs):
super(CatgoricalTruePositives, self).__init__(name=name, **kwargs)
self.true_positives = self.add_weight(name='tp', initializer='zeros')
def update_state(self, y_true, y_pred, sample_weight=None):
y_pred = tf.argmax(y_pred,axis=-1)
y_true = tf.argmax(y_true,axis=-1)
values = tf.equal(tf.cast(y_true, 'int32'), tf.cast(y_pred, 'int32'))
values = tf.cast(values, 'float32')
if sample_weight is not None:
sample_weight = tf.cast(sample_weight, 'float32')
values = tf.multiply(values, sample_weight)
self.true_positives.assign_add(tf.reduce_sum(values))
def result(self):
return self.true_positives
def reset_states(self):
self.true_positives.assign(0.)
model = MyModel()
model.compile(optimizer = tf.keras.optimizers.Adam(0.001), #优化器
loss = tf.keras.losses.CategoricalCrossentropy(), #损失函数
metrics = [tf.keras.metrics.CategoricalAccuracy(),
CatgoricalTruePositives(),
]
) #评估函数
model.fit(train_ds, epochs=5,validation_data=test_ds)
Epoch 1/5
1875/1875 [==============================] - 179s 96ms/step - loss: 0.1335 - categorical_accuracy: 0.9598 - categorical_true_positives: 57587.0000 - val_loss: 0.0000e+00 - val_categorical_accuracy: 0.0000e+00 - val_categorical_true_positives: 0.0000e+00
...
Epoch 5/5
1875/1875 [==============================] - 182s 97ms/step - loss: 0.0096 - categorical_accuracy: 0.9968 - categorical_true_positives: 59809.0000 - val_loss: 0.0631 - val_categorical_accuracy: 0.9847 - val_categorical_true_positives: 9847.0000
4.4.TensorBoard
TensorBoard是一个在深度学习中很好的可视化训练过程和模型结构的工具，那么，要怎么才能在TensorFlow2.0中使用它呢？
在TensorFlow2.0中，训练一个神经网络模型主要有两种方式：
• 使用tf.keras模块的Model.fit()；
• 使用tf.GradientTape()求解梯度，这样可以自定义训练过程。
对于这两种方案，都可以使用TensorBoard
Keras在回调函数中内置Tensorboard函数：
tf.keras.callbacks.TensorBoard(
log_dir='logs',
histogram_freq=0,
write_graph=True,
write_images=False,
update_freq='epoch',
profile_batch=2,
embeddings_freq=0,
embeddings_metadata=None
)
|
参数 |
解释 |
|
log_dir |
保存TensorBoard要解析的日志文件的目录的路径。 |
|
histogram_freq |
频率（在epoch中），计算模型层的**和权重直方图。如果设置为0，则不会计算直方图。必须为直方图可视化指定验证数据（或拆分）。 |
|
write_graph |
是否在TensorBoard中可视化图像。当write_graph设置为True时，日志文件可能会变得非常大。 |
|
write_images |
是否在TensorBoard中编写模型权重以显示为图像。 |
|
update_freq |
‘batch’ 或’ epoch’ 或整数。使用‘batch’ 时，在每个batch后将损失和指标(评估函数)写入TensorBoard。这同样适用’ epoch’ 。如果使用整数，比方说1000，回调将会在每1000个样本后将指标和损失写入TensorBoard。请注意，过于频繁地写入TensorBoard会降低您的训练速度。 |
|
profile_batch |
分析批次以采样计算特征。 profile_batch必须是非负整数或正整数对的逗号分隔字符串。一对正整数表示要分析的批次范 |
|
embeddings_freq |
可视化嵌入层的频率（以epoch为单位）。如果设置为0，则嵌入将不可见。 |
|
embeddings_metadata |
字典，它将层名称映射到文件名，该嵌入层的元数据保存在该文件名中。 |
下面以在MNIST数据集上训练一个图像分类模型为例介绍。
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model
import numpy as np
import datetime
print(tf.__version__)
print(np.__version__)
2.0.0
1.19.0
mnist = np.load("mnist.npz")
x_train, y_train, x_test, y_test = mnist['x_train'],mnist['y_train'],mnist['x_test'],mnist['y_test']
x_train, x_test = x_train / 255.0, x_test / 255.0
# Add a channels dimension
x_train = x_train[..., tf.newaxis]
x_test = x_test[..., tf.newaxis]
class MyModel(Model):
def __init__(self):
super(MyModel, self).__init__()
self.conv1 = Conv2D(32, 3, activation='relu')
self.flatten = Flatten()
self.d1 = Dense(128, activation='relu')
self.d2 = Dense(10, activation='softmax')
@tf.function
def call(self, x):
x = self.conv1(x)
x = self.flatten(x)
x = self.d1(x)
return self.d2(x)
model = MyModel()
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir="keras_logv1",
histogram_freq=1,
profile_batch = 100000000)
model.fit(x=x_train,
y=y_train,
epochs=20,
validation_data=(x_test, y_test),
callbacks=[tensorboard_callback])
Train on 60000 samples, validate on 10000 samples
Epoch 1/20
60000/60000 [==============================] - 6s 104us/sample - loss: 0.1355 - accuracy: 0.9589 - val_loss: 0.0645 - val_accuracy: 0.9798
...
Epoch 20/20
60000/60000 [==============================] - 7s 119us/sample - loss: 0.0020 - accuracy: 0.9994 - val_loss: 0.1073 - val_accuracy: 0.9832
执行完成后，可以在cmd中通过命令启动客户端：
tensorboard --bind_all --logdir D:\...\keras_logv1
浏览器访问：http://DESKTOP-QBI0CUK:6006/
Tensorboard界面解释：
Scalars : 显示了如何将loss与每个时间段改变。还可以使用它来跟踪训练速度，学习率和其他标量值。
Graphs： 进行可视化模型。在这种情况下，将显示层的Keras图，这可以帮助你确保模型正确构建。
Distributions 和 Histograms ：显示张量随时间的分布。