浅谈keras的扩展性：自定义keras
1. 自定义keras
keras是一种深度学习的API，能够快速实现你的实验。keras也集成了很多预训练的模型，可以实现很多常规的任务，如图像分类。TensorFlow 2.0之后tensorflow本身也变的很keras化。
另一方面，keras表现出高度的模块化和封装性，所以有的人会觉得keras不易于扩展， 比如实现一种新的Loss，新的网络层结构； 其实可以通过keras的基础模块进行快速的扩展，实现更新的算法。
本文就keras的扩展性，总结了对layer，model和loss的自定义。
2. 自定义keras layers
layers是keras中重要的组成部分，网络结构中每一个组成都要以layers来表现。keras提供了很多常规的layer，如Convolution layers，pooling layers， activation layers， dense layers等， 我们可以通过继承基础layers来扩展自定义的layers。
2.1 base layer
layer实了输入tensor和输出tensor的操作类，以下为base layer的5个方法，自定义layer只要重写这些方法就可以了。
- init(): 定义自定义layer的一些属性
- build(self, input_shape)： 定义layer需要的权重weights
- call(self, args, *kwargs)：layer具体的操作，会在调用自定义layer自动执行
- get_config(self)：layer初始化的配置，是一个字典dictionary。
- compute_output_shape(self,input_shape)：计算输出tensor的shape
2.2 例子
# 标准化层
class InstanceNormalize(Layer):
def __init__(self, **kwargs):
super(InstanceNormalize, self).__init__(**kwargs)
self.epsilon = 1e-3
def call(self, x, mask=None):
mean, var = tf.nn.moments(x, [1, 2], keep_dims=True)
return tf.div(tf.subtract(x, mean), tf.sqrt(tf.add(var, self.epsilon)))
def compute_output_shape(self,input_shape):
return input_shape
# 调用
inputs = keras.Input(shape=(None, None, 3))
x = InstanceNormalize()(inputs)
# 可以通过add_weight() 创建权重
class SimpleDense(Layer):
def __init__(self, units=32):
super(SimpleDense, 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=True)
def call(self, inputs):
return tf.matmul(inputs, self.w) + self.b
# 调用
inputs = keras.Input(shape=(None, None, 3))
x = SimpleDense(units=64)(inputs)
3. 自定义keras model
我们在定义完网络结构时，会把整个工作流放在keras.Model， 进行compile(), 然后通过fit()进行训练过程。执行fit()的时候，执行每个batch size data的时候，都会调用Model中train_step(self, data)
from keras.models import Sequential
from keras.layers import Dense, Activation
model = Sequential()
model.add(Dense(units=64, input_dim=100))
model.add(Activation("relu"))
model.add(Dense(units=10))
model.add(Activation("softmax"))
model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5, batch_size=32)
当你需要自己控制训练过程的时候，可以重写Model的train_step(self, data)方法
class CustomModel(keras.Model):
def train_step(self, data):
# Unpack the data. Its structure depends on your model and
# on what you pass to `fit()`.
x, y = data
with tf.GradientTape() as tape:
y_pred = self(x, training=True) # Forward pass
# Compute the loss value
# (the loss function is configured in `compile()`)
loss = self.compiled_loss(y, y_pred, regularization_losses=self.losses)
# Compute gradients
trainable_vars = self.trainable_variables
gradients = tape.gradient(loss, trainable_vars)
# Update weights
self.optimizer.apply_gradients(zip(gradients, trainable_vars))
# Update metrics (includes the metric that tracks the loss)
self.compiled_metrics.update_state(y, y_pred)
# Return a dict mapping metric names to current value
return {
m.name: m.result() for m in self.metrics}
import numpy as np
# Construct and compile an instance of CustomModel
inputs = keras.Input(shape=(32,))
outputs = keras.layers.Dense(1)(inputs)
model = CustomModel(inputs, outputs)
model.compile(optimizer="adam", loss="mse", metrics=["mae"])
# Just use `fit` as usual
x = np.random.random((1000, 32))
y = np.random.random((1000, 1))
model.fit(x, y, epochs=3)
4. 自定义keras loss
keras实现了交叉熵等常见的loss，自定义loss对于使用keras来说是比较常见，实现各种魔改loss，如focal loss。
我们来看看keras源码中对loss实现
def categorical_crossentropy(y_true, y_pred):
return K.categorical_crossentropy(y_true, y_pred)
def mean_squared_error(y_true, y_pred):
return K.mean(K.square(y_pred - y_true), axis=-1)
可以看出输入是groud true y_true和预测值y_pred， 返回为计算loss的函数。自定义loss可以参照如此模式即可。
def focal_loss(weights=None, alpha=0.25, gamma=2):
r"""Compute focal loss for predictions.
Multi-labels Focal loss formula:
FL = -alpha * (z-p)^gamma * log(p) -(1-alpha) * p^gamma * log(1-p)
,which alpha = 0.25, gamma = 2, p = sigmoid(x), z = target_tensor.
# https://github.com/ailias/Focal-Loss-implement-on-Tensorflow/blob/master/focal_loss.py
Args:
prediction_tensor: A float tensor of shape [batch_size, num_anchors,
num_classes] representing the predicted logits for each class
target_tensor: A float tensor of shape [batch_size, num_anchors,
num_classes] representing one-hot encoded classification targets
weights: A float tensor of shape [batch_size, num_anchors]
alpha: A scalar tensor for focal loss alpha hyper-parameter
gamma: A scalar tensor for focal loss gamma hyper-parameter
Returns:
loss: A (scalar) tensor representing the value of the loss function
"""
def _custom_loss(y_true, y_pred):
sigmoid_p = tf.nn.sigmoid(y_pred)
zeros = array_ops.zeros_like(sigmoid_p, dtype=sigmoid_p.dtype)
# For poitive prediction, only need consider front part loss, back part is 0;
# target_tensor > zeros <=> z=1, so poitive coefficient = z - p.
pos_p_sub = array_ops.where(y_true > zeros, y_true - sigmoid_p, zeros)
# For negative prediction, only need consider back part loss, front part is 0;
# target_tensor > zeros <=> z=1, so negative coefficient = 0.
neg_p_sub = array_ops.where(y_true > zeros, zeros, sigmoid_p)
per_entry_cross_ent = - alpha * (pos_p_sub ** gamma) * tf.log(tf.clip_by_value(sigmoid_p, 1e-8, 1.0)) \
- (1 - alpha) * (neg_p_sub ** gamma) * tf.log(
tf.clip_by_value(1.0 - sigmoid_p, 1e-8, 1.0))
return tf.reduce_sum(per_entry_cross_ent)
return _custom_loss
5. 总结
本文分享了keras的扩展功能，扩展功能其实也是实现Keras模块化的一种继承实现。
总结如下：
- 继承Layer实现自定义layer， 记住
bulid()call() - 继续Model实现
train_step定义训练过程，记住梯度计算tape.gradient(loss, trainable_vars)，权重更新optimizer.apply_gradients, 计算evaluatecompiled_metrics.update_state(y, y_pred) - 魔改loss，记住groud true
y_true和预测值y_pred输入，返回loss function
