问题：我的训练非常慢。如何重写代码以加快深度学习模型的训练？
背景：我用TensorFlow 2.8.1构建了一个CNN，使用自定义损失函数对CIFAR-100图像进行分类。CIFAR数据集包括100个精细类别(例如熊、汽车(的32x32像素RGB图像，这些精细类别被分类为20个粗略类别(例如大型杂食动物、车辆(。我的自定义损失函数是另外两个损失函数的加权和(见下面的代码(。第一个分量是精细标签的交叉熵损失。第二个分量是粗标签的交叉熵损失。我希望这个自定义损失函数将强制对粗略标签进行准确分类，以获得对精细标签更准确的分类(祈祷(。比较器将只是精细标签(基线模型(的交叉熵损失。注意，为了导出粗略(分层(损失分量，我必须将y_true(真精细标签，整数(和y_pred(精细标签的预测softmax概率，向量(分别映射到y_true_coarse_int(真粗略标签，整数，整数(或y_pred_coarse_hot(预测粗略标签，一个热编码向量(。FineInts_to_CoarseInts是一个允许这种映射的python字典。
培训需要>使用自定义损失函数运行5小时，而使用规则交叉熵损失进行精细类的训练需要约1小时。代码在具有32GB CPU和1个GPU的高性能计算集群上运行。
见下文：
# THIS CODE CELL IS TO DEFINE A CUSTOM LOSS FUNCTION
def crossentropy_loss(y_true, y_pred):
return SparseCategoricalCrossentropy()(y_true, y_pred)
def hierarchical_loss(y_true, y_pred):
y_true = tensorflow.cast(y_true, dtype=float)
y_true_reshaped = tensorflow.reshape(y_true, -1)
y_true_coarse_int = [FineInts_to_CoarseInts[K.eval(y_true_reshaped[i])] for i in range(y_true_reshaped.shape[0])]
y_true_coarse_int = tensorflow.cast(y_true_coarse_int, dtype=tensorflow.float32)
y_pred = tensorflow.cast(y_pred, dtype=float)
y_pred_int = tensorflow.argmax(y_pred, axis=1)
y_pred_coarse_int = [FineInts_to_CoarseInts[K.eval(y_pred_int[i])] for i in range(y_pred_int.shape[0])]
y_pred_coarse_int = tensorflow.cast(y_pred_coarse_int, dtype=tensorflow.float32)
y_pred_coarse_hot = to_categorical(y_pred_coarse_int, 20)
return SparseCategoricalCrossentropy()(y_true_coarse_int, y_pred_coarse_hot)
def custom_loss(y_true, y_pred):
H = 0.5
total_loss = (1 - H) * crossentropy_loss(y_true, y_pred) + H * hierarchical_loss(y_true, y_pred)
return total_loss
在模型编译过程中，我不得不将run_eagerly参数设置为True。见下文：
# THIS CODE CELL IS TO COMPILE THE MODEL
model.compile(optimizer="adam", loss=custom_loss, metrics="accuracy", run_eagerly=True)
完整代码如下：
# THIS CODE CELL LOADS THE PACKAGES USED IN THIS NOTEBOOK
# Load core packages for data analysis and visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sn
import sys
!{sys.executable} -m pip install pydot
!{sys.executable} -m pip install graphviz
# Load deep learning packages
import tensorflow
from tensorflow.keras.datasets.cifar100 import load_data
from tensorflow.keras import (Model, layers)
from tensorflow.keras.losses import SparseCategoricalCrossentropy
import tensorflow.keras.backend as K
from tensorflow.keras.utils import (to_categorical, plot_model)
from tensorflow.lookup import (StaticHashTable, KeyValueTensorInitializer)
# Load model evaluation packages
import sklearn
from sklearn.metrics import (confusion_matrix, classification_report)
# Print versions of main ML packages
print("Tensorflow version " + tensorflow.__version__)
print("Scikit learn version " + sklearn.__version__)
# THIS CODE CELL LOADS DATASETS AND CHECKS DATA DIMENSIONS
# There is an option to load the "fine" (100 fine classes) or "coarse" (20 super classes) labels with integer (int) encodings
# We will load both labels for hierarchical classification tasks
(x_train, y_train_fine_int), (x_test, y_test_fine_int) = load_data(label_mode="fine")
(_, y_train_coarse_int), (_, y_test_coarse_int) = load_data(label_mode="coarse")
# EXTRACT DATASET PARAMETERS FOR USE LATER ON
num_fine_classes = 100
num_coarse_classes = 20
input_shape = x_train.shape[1:]
# THIS CODE CELL PROVIDES THE CODE TO LINK INTEGER LABELS TO MEANINGFUL WORD LABELS
# Fine and coarse labels are provided as integers. We will want to link them both to meaningful world labels.
# CREATE A DICTIONARY TO MAP THE 20 COARSE LABELS TO THE 100 FINE LABELS
# This mapping comes from https://keras.io/api/datasets/cifar100/
# Except "computer keyboard" should just be "keyboard" for the encoding to work
CoarseLabels_to_FineLabels = {
"aquatic mammals": ["beaver", "dolphin", "otter", "seal", "whale"],
"fish": ["aquarium fish", "flatfish", "ray", "shark", "trout"],
"flowers": ["orchids", "poppies", "roses", "sunflowers", "tulips"],
"food containers": ["bottles", "bowls", "cans", "cups", "plates"],
"fruit and vegetables": ["apples", "mushrooms", "oranges", "pears", "sweet peppers"],
"household electrical devices": ["clock", "keyboard", "lamp", "telephone", "television"],
"household furniture": ["bed", "chair", "couch", "table", "wardrobe"],
"insects": ["bee", "beetle", "butterfly", "caterpillar", "cockroach"],
"large carnivores": ["bear", "leopard", "lion", "tiger", "wolf"],
"large man-made outdoor things": ["bridge", "castle", "house", "road", "skyscraper"],
"large natural outdoor scenes": ["cloud", "forest", "mountain", "plain", "sea"],
"large omnivores and herbivores": ["camel", "cattle", "chimpanzee", "elephant", "kangaroo"],
"medium-sized mammals": ["fox", "porcupine", "possum", "raccoon", "skunk"],
"non-insect invertebrates": ["crab", "lobster", "snail", "spider", "worm"],
"people": ["baby", "boy", "girl", "man", "woman"],
"reptiles": ["crocodile", "dinosaur", "lizard", "snake", "turtle"],
"small mammals": ["hamster", "mouse", "rabbit", "shrew", "squirrel"],
"trees": ["maple", "oak", "palm", "pine", "willow"],
"vehicles 1": ["bicycle", "bus", "motorcycle", "pickup" "truck", "train"],
"vehicles 2": ["lawn-mower", "rocket", "streetcar", "tank", "tractor"]
}
# CREATE A DICTIONARY TO MAP THE INTEGER-ENCODED COARSE LABEL TO THE WORD LABEL
# Create list of Course Labels
CoarseLabels = list(CoarseLabels_to_FineLabels.keys())
# The target variable in CIFER100 is encoded such that the coarse class is assigned an integer based on its alphabetical order
# The CoarseLabels list is already alphabetized, so no need to sort
CoarseInts_to_CoarseLabels = dict(enumerate(CoarseLabels))
# CREATE A DICTIONARY TO MAP THE WORD LABEL TO THE INTEGER-ENCODED COARSE LABEL
CoarseLabels_to_CoarseInts = dict(zip(CoarseLabels, range(20)))
# CREATE A DICTIONARY TO MAP THE 100 FINE LABELS TO THE 20 COARSE LABELS
FineLabels_to_CoarseLabels = {}
for CoarseLabel in CoarseLabels:
for FineLabel in CoarseLabels_to_FineLabels[CoarseLabel]:
FineLabels_to_CoarseLabels[FineLabel] = CoarseLabel
# CREATE A DICTIONARY TO MAP THE INTEGER-ENCODED FINE LABEL TO THE WORD LABEL
# Create a list of the Fine Labels
FineLabels = list(FineLabels_to_CoarseLabels.keys())
# The target variable in CIFER100 is encoded such that the fine class is assigned an integer based on its alphabetical order
# Sort the fine class list.
FineLabels.sort()
FineInts_to_FineLabels = dict(enumerate(FineLabels))
# CREATE A DICTIONARY TO MAP THE INTEGER-ENCODED FINE LABELS TO THE INTEGER-ENCODED COARSE LABELS
b = list(dict(sorted(FineLabels_to_CoarseLabels.items())).values())
FineInts_to_CoarseInts = dict(zip(range(100), [CoarseLabels_to_CoarseInts[i] for i in b]))
#Tensor version of dictionary
#fine_to_coarse = tensorflow.constant(list((FineInts_to_CoarseInts).items()), dtype=tensorflow.int8)
# THIS CODE CELL IS TO BUILD A FUNCTIONAL MODEL
inputs = layers.Input(shape=input_shape)
x = layers.BatchNormalization()(inputs)
x = layers.Conv2D(64, (3, 3), padding='same', activation="relu")(x)
x = layers.MaxPooling2D(pool_size=(2, 2))(x)
x = layers.Dropout(0.30)(x)
x = layers.Conv2D(256, (3, 3), padding='same', activation="relu")(x)
x = layers.MaxPooling2D(pool_size=(2, 2))(x)
x = layers.Dropout(0.30)(x)
x = layers.Conv2D(256, (3, 3), padding='same', activation="relu")(x)
x = layers.MaxPooling2D(pool_size=(2, 2))(x)
x = layers.Dropout(0.30)(x)
x = layers.Conv2D(1024, (3, 3), padding='same', activation="relu")(x)
x = layers.MaxPooling2D(pool_size=(2, 2))(x)
x = layers.Dropout(0.30)(x)
x = layers.GlobalAveragePooling2D()(x)
x = layers.BatchNormalization()(x)
x = layers.Dropout(0.30)(x)
x = layers.Dense(512, activation = "relu")(x)
x = layers.BatchNormalization()(x)
x = layers.Dropout(0.30)(x)
output_fine = layers.Dense(num_fine_classes, activation="softmax", name="output_fine")(x)
model = Model(inputs=inputs, outputs=output_fine)
# THIS CODE CELL IS TO DEFINE A CUSTOM LOSS FUNCTION
def crossentropy_loss(y_true, y_pred):
return SparseCategoricalCrossentropy()(y_true, y_pred)
def hierarchical_loss(y_true, y_pred):
y_true = tensorflow.cast(y_true, dtype=float)
y_true_reshaped = tensorflow.reshape(y_true, -1)
y_true_coarse_int = [FineInts_to_CoarseInts[K.eval(y_true_reshaped[i])] for i in range(y_true_reshaped.shape[0])]
y_true_coarse_int = tensorflow.cast(y_true_coarse_int, dtype=tensorflow.float32)
y_pred = tensorflow.cast(y_pred, dtype=float)
y_pred_int = tensorflow.argmax(y_pred, axis=1)
y_pred_coarse_int = [FineInts_to_CoarseInts[K.eval(y_pred_int[i])] for i in range(y_pred_int.shape[0])]
y_pred_coarse_int = tensorflow.cast(y_pred_coarse_int, dtype=tensorflow.float32)
y_pred_coarse_hot = to_categorical(y_pred_coarse_int, 20)
return SparseCategoricalCrossentropy()(y_true_coarse_int, y_pred_coarse_hot)
def custom_loss(y_true, y_pred):
H = 0.5
total_loss = (1 - H) * crossentropy_loss(y_true, y_pred) + H * hierarchical_loss(y_true, y_pred)
return total_loss
# THIS CODE CELL IS TO COMPILE THE MODEL
model.compile(optimizer="adam", loss=crossentropy_loss, metrics="accuracy", run_eagerly=False)
# THIS CODE CELL IS TO TRAIN THE MODEL
history = model.fit(x_train, y_train_fine_int, epochs=200, validation_split=0.25, batch_size=100)
# THIS CODE CELL IS TO VISUALIZE THE TRAINING
history_frame = pd.DataFrame(history.history)
history_frame.to_csv("history.csv")
history_frame.loc[:, ["accuracy", "val_accuracy"]].plot()
history_frame.loc[:, ["loss", "val_loss"]].plot()
plt.show()
# THIS CODE CELL IS TO EVALUATE THE MODEL ON AN INDEPENDENT DATASET
score = model.evaluate(x_test, y_test_fine_int, verbose=0)
print("Test loss:", score[0])
print("Test accuracy:", score[1])
量化
- 量化是将数字类型
float32转换为int8的技术。这意味着你的模型尺寸会更小 - 训练前量化和训练后量化有两种类型
- 试着在训练前应用量化，并让我知道结果
请参阅本视频了解量化