我最近开始在一个项目中利用 Keras 的 flow_from_dataframe() 功能,并决定使用 MNIST 数据集对其进行测试。我有一个包含 png 格式的 MNIST 样本的目录,以及一个数据框,其中每一列的绝对目录和另一列中的标签。
我还在使用迁移学习,导入 VGG16 作为基础,并在 10 的 softmax 层之前添加我自己的 512 个节点的 relu 密集层和 0.5 个 dropout。(对于数字 0-9)。我使用 rmsprop (lr=1e-4) 作为优化器。
当我启动我的环境时,它会从 Git 调用最新版本的 keras_preprocessing,它支持绝对目录和大写文件扩展名。
我的问题是我的训练准确率非常高,而验证准确率非常低。在我的最后一个 epoch (10) 时,我的训练准确度为 0.94,验证准确度为 0.01。
我想知道我的脚本是否存在根本问题?使用另一个数据集,我什至在 epoch 4 之后获得了我的训练和验证损失值的 NaN。(我检查了相关列,没有任何空值!)
这是我的代码。如果有人可以浏览它,看看是否有什么东西跳出来,我将非常感激。
import pandas as pd
import numpy as np
import keras
from keras_preprocessing.image import ImageDataGenerator
from keras import applications
from keras import optimizers
from keras.models import Model
from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D
from keras import backend as k
from keras.callbacks import ModelCheckpoint, CSVLogger
from keras.applications.vgg16 import VGG16, preprocess_input
# INITIALIZE MODEL
img_width, img_height = 32, 32
model = VGG16(weights = 'imagenet', include_top=False, input_shape = (img_width, img_height, 3))
# freeze all layers
for layer in model.layers:
layer.trainable = False
# Adding custom Layers
x = model.output
x = Flatten()(x)
x = Dense(512, activation='relu')(x)
x = Dropout(0.5)(x)
predictions = Dense(10, activation="softmax")(x)
# creating the final model
model_final = Model(input = model.input, output = predictions)
# compile the model
rms = optimizers.RMSprop(lr=1e-4)
#adadelta = optimizers.Adadelta(lr=0.001, rho=0.5, epsilon=None, decay=0.0)
model_final.compile(loss = "categorical_crossentropy", optimizer = rms, metrics=["accuracy"])
# LOAD AND DEFINE SOURCE DATA
train = pd.read_csv('MNIST_train.csv', index_col=0)
val = pd.read_csv('MNIST_test.csv', index_col=0)
nb_train_samples = 60000
nb_validation_samples = 10000
batch_size = 60
epochs = 10
# Initiate the train and test generators
train_datagen = ImageDataGenerator()
test_datagen = ImageDataGenerator()
train_generator = train_datagen.flow_from_dataframe(dataframe=train,
directory=None,
x_col='train_samples',
y_col='train_labels',
has_ext=True,
target_size = (img_height,
img_width),
batch_size = batch_size,
class_mode = 'categorical',
color_mode = 'rgb')
validation_generator = test_datagen.flow_from_dataframe(dataframe=val,
directory=None,
x_col='test_samples',
y_col='test_labels',
has_ext=True,
target_size = (img_height,
img_width),
batch_size = batch_size,
class_mode = 'categorical',
color_mode = 'rgb')
# GET CLASS INDICES
print('****************')
for cls, idx in train_generator.class_indices.items():
print('Class #{} = {}'.format(idx, cls))
print('****************')
# DEFINE CALLBACKS
path = './chk/epoch_{epoch:02d}-valLoss_{val_loss:.2f}-valAcc_{val_acc:.2f}.hdf5'
chk = ModelCheckpoint(path, monitor = 'val_acc', verbose = 1, save_best_only = True, mode = 'max')
logger = CSVLogger('./chk/training_log.csv', separator = ',', append=False)
nPlus = 1
samples_per_epoch = nb_train_samples * nPlus
# Train the model
model_final.fit_generator(train_generator,
steps_per_epoch = int(samples_per_epoch/batch_size),
epochs = epochs,
validation_data = validation_generator,
validation_steps = int(nb_validation_samples/batch_size),
callbacks = [chk, logger])