Question

我正在尝试训练我的6000个训练数据集和1000个验证数据集，但是我有一个问题：程序只是在训练过程中冻结并挂起，而没有任何错误消息。

1970/6000 [========>.....................] - ETA: 1:50:11 - loss: 1.2256 - accuracy: 0.5956
1971/6000 [========>.....................] - ETA: 1:50:08 - loss: 1.2252 - accuracy: 0.5958
1972/6000 [========>.....................] - ETA: 1:50:08 - loss: 1.2248 - accuracy: 0.5960
1973/6000 [========>.....................] - ETA: 1:50:06 - loss: 1.2245 - accuracy: 0.5962
1974/6000 [========>.....................] - ETA: 1:50:04 - loss: 1.2241 - accuracy: 0.5964
1975/6000 [========>.....................] - ETA: 1:50:02 - loss: 1.2243 - accuracy: 0.5961
1976/6000 [========>.....................] - ETA: 1:50:00 - loss: 1.2239 - accuracy: 0.5963
1977/6000 [========>.....................] - ETA: 1:49:58 - loss: 1.2236 - accuracy: 0.5965
1978/6000 [========>.....................] - ETA: 1:49:57 - loss: 1.2241 - accuracy: 0.5962
1979/6000 [========>.....................] - ETA: 1:49:56 - loss: 1.2237 - accuracy: 0.5964
1980/6000 [========>.....................] - ETA: 1:49:55 - loss: 1.2242 - accuracy: 0.5961
1981/6000 [========>.....................] - ETA: 1:49:53 - loss: 1.2252 - accuracy: 0.5958
1982/6000 [========>.....................] - ETA: 1:49:52 - loss: 1.2257 - accuracy: 0.5955

我等待5-6分钟，但似乎什么也没发生。我尝试解决

将steps_per_epoch更改为100并将epoch增大为20
我认为这是功能ReduceLROnPlateau的问题，因此我将添加冷却时间= 1
但是2种解决方案都不能解决这个问题

硬件配置：

I5-8300h
Gtx 1060 6GB

依赖项：

Keras 2.3.1
TensorFlow 2.0.0（GPU版本）

下面提供了代码：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import keras
import tensorflow as tf
from skimage import exposure, color
from keras.optimizers import Adam
from tqdm import tqdm
from keras.models import Model
from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D,Convolution2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint, Callback
from keras import regularizers
from keras.applications.densenet import DenseNet121
from keras_preprocessing.image import ImageDataGenerator
from sklearn.utils import class_weight
from collections import Counter

config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth=True
session = tf.compat.v1.Session(config=config)


# Histogram equalization
def HE(img):
    img_eq = exposure.equalize_hist(img)
    return img_eq



def plotImages(images_arr):
    fig, axes = plt.subplots(1, 5, figsize=(20,20))
    axes = axes.flatten()
    for img, ax in zip( images_arr, axes):
        ax.imshow(img)
        ax.axis('off')
    plt.tight_layout()
    plt.show()

train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    rotation_range=40,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode='nearest',
    preprocessing_function=HE,
)

validation_datagen = ImageDataGenerator(
    rescale=1./255
)
test_datagen = ImageDataGenerator(
    rescale=1./255
)

#get image and label with augmentation
train = train_datagen.flow_from_directory(
'train/train_deep/',
target_size=(224,224),
class_mode='categorical',
shuffle=False,
batch_size = 20,
)

test = test_datagen.flow_from_directory(
    'test_deep/',
    batch_size=1,
    target_size = (224,224),

)

val = validation_datagen.flow_from_directory(
    'train/validate_deep/',
    target_size=(224,224),
    batch_size = 20,
)
#Training
X_train, y_train = next(train)
class_names = ['No DR', 'Mild', 'Moderate', 'Severe', 'Proliferative DR']
counter = Counter(train.classes)
class_weights = class_weight.compute_class_weight(
               'balanced',
                np.unique(train.classes),
                train.classes)

#X_test , y_test = next(test)
#X_test=np.reshape(X_test,(X_test.shape[0],X_test.shape[1],X_test.shape[2]))
#Training parameter
batch_size =32
Epoch = 2


model = DenseNet121(include_top=True, weights=None, input_tensor=None, input_shape=(224,224,3), pooling=None, classes=5)
model.compile(loss='categorical_crossentropy',
              optimizer=Adam(learning_rate=0.01),
              metrics=['accuracy'])
model.summary()
filepath="weights-improvement-{epoch:02d}-{val_loss:.2f}.hdf5"
checkpointer = ModelCheckpoint(filepath,monitor='val_loss', verbose=1, save_best_only=True,save_weights_only=True)
lr_reduction = ReduceLROnPlateau(monitor='val_loss', patience=5, verbose=2, factor=0.2,cooldown=1)
callbacks_list = [checkpointer, lr_reduction]
#Validation
X_val , y_val = next(val)

#history = model.fit(X_train,y_train,epochs=Epoch,validation_data = (X_val,y_val))

history = model.fit_generator(
    train,
    epochs=Epoch,
    steps_per_epoch=6000,
    class_weight=class_weights,
    validation_data=val,
    validation_steps=1000,
    use_multiprocessing = False,
    max_queue_size=100,
    workers = 1,
    callbacks=callbacks_list
)


# Score trained model.
scores = model.evaluate(X_val, y_val, verbose=1)
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])


#predict
test.reset()
pred=model.predict_generator(test,
steps=25,)

print(pred)
for i in pred:
    print(np.argmax(i))

Answer 1

如果您使用Keras <2.0.0（我不建议您使用旧版本），则此代码将很好地工作。

您的错误来自以下事实：您在TensorFlow中使用Keras> 2.0.0或Keras。

代码中的确切错误源于以下几行：

history = model.fit_generator(
    train,
    epochs=Epoch,
    steps_per_epoch=6000, #change this to 6000/32
    class_weight=class_weights,
    validation_data=val,
    validation_steps=1000, #change this to 1000/32
    use_multiprocessing = False,
    max_queue_size=100,
    workers = 1,
    callbacks=callbacks_list
)

参数“ steps_per_epoch”和“ validation_steps”必须等于数据集的长度除以批大小。

Keras ---在fit_generator（）期间冻结训练

1 个答案: