使用Keras序列模型的cifar10資料集的準確度為零

Conv2D盡管使用了多個and ，我的準確率對于所有 15 個時期都是零Max Pooling Layers。我正在ImageDataGenerator使用Data Augmentation.

完整代碼如下：

# importing all the required libraries
import tensorflow as tf
from tensorflow.keras.layers import Dense, Conv2D, Flatten, MaxPool2D, Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt

# Loading the Data from the in built library
(train_images, train_labels), (test_images, test_labels) = cifar10.load_data()

# Normalize the Pixel Data
train_images = train_images/255.0
test_images = test_images/255.0

# Instantiate the Image Data Generator Class with the Data Augmentation
datagen = ImageDataGenerator(width_shift_range = 0.2, height_shift_range = 0.2, 
                             rotation_range = 20, horizontal_flip = True, 
                             vertical_flip = True, validation_split = 0.2)

# Apply the Data Augmentation to the Training Images
datagen.fit(train_images)

# Create the Generator for the Training Images
train_gen = datagen.flow(train_images, train_labels, batch_size = 32, 
                         subset = 'training')

# Create the Generator for the Validation Images
val_gen = datagen.flow(train_images, train_labels, batch_size = 8, 
                         subset = 'validation')

num_classes = 10

# One Hot Encoding of Labels using to_categorical
train_labels = to_categorical(train_labels, num_classes)
test_labels = to_categorical(test_labels, num_classes)

img_height = 32
img_width = 32

# Building the Keras Model
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
#model.add(Dropout(rate = 0.2))
model.add(Dense(units = num_classes, activation = 'softmax'))
model.summary()

model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', 
              metrics = ['accuracy'])

steps_per_epoch = len(train_images) * 0.8//32

history = model.fit(train_gen, validation_data = val_gen, 
          steps_per_epoch = steps_per_epoch, epochs = 15)

uj5u.com熱心網友回復：

你的問題是你運行了這段代碼

train_gen = datagen.flow(train_images, train_labels, batch_size = 32, 
                         subset = 'training')

# Create the Generator for the Validation Images
val_gen = datagen.flow(train_images, train_labels, batch_size = 8, 
                         subset = 'validation')

但只有在此之后，您才將標簽轉換為分類。所以拿代碼

num_classes = 10

# One Hot Encoding of Labels using to_categorical
train_labels = to_categorical(train_labels, num_classes)
test_labels = to_categorical(test_labels, num_classes)

并將其放在 train_gen 和 val_gen 代碼之前。在更好的一點上，您有代碼

datagen.fit(train_images)

如果您將任何引數 featurewise_center、samplewise_center、featurewise_std_normalization 或 samplewise_std_normalization 設定為 true，則只需要擬合生成器。

uj5u.com熱心網友回復：

將您的標簽轉換為.flow.

...
# One Hot Encoding of Labels using to_categorical
train_labels = to_categorical(train_labels, num_classes)
test_labels = to_categorical(test_labels, num_classes)

# Create the Generator for the Training Images
train_gen = datagen.flow(train_images, train_labels, batch_size = 32, 
                         subset = 'training')

# Create the Generator for the Validation Images
val_gen = datagen.flow(train_images, train_labels, batch_size = 8, 
                         subset = 'validation')
...

標籤：Python 张量流 机器学习 喀拉斯 图像分类

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