Question

我已经建立了一个张量流模型，并且在不同的时期我的验证精度没有任何变化，这使我相信我的设置有问题。下面是我的代码。

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import regularizers
import tensorflow as tf

model = Sequential()
model.add(Conv2D(16, (3, 3), input_shape=(299, 299,3),padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))

model.add(Conv2D(32, (3, 3),padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))

model.add(Conv2D(64, (3, 3),padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))

model.add(Conv2D(64, (3, 3),padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))

# this converts our 3D feature maps to 1D feature vectors
model.add(Flatten())  

model.add(Dense(512))
model.add(Activation('relu'))

model.add(Dropout(0.5))

model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

batch_size=32

# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
        rescale=1./255,
        # shear_range=0.2,
        # zoom_range=0.2,
        horizontal_flip=True)

# this is the augmentation configuration we will use for testing:
# only rescaling
test_datagen = ImageDataGenerator(rescale=1./255)

# this is a generator that will read pictures found in
# subfolers of 'data/train', and indefinitely generate
# batches of augmented image data
train_generator = train_datagen.flow_from_directory(
        'Documents/Training',  # this is the target directory
         target_size=(299, 299),  #all images will be resized to 299
        batch_size=batch_size,
        class_mode='binary')  # since we use binary_crossentropy loss, we need binary labels

# this is a similar generator, for validation data
validation_generator = test_datagen.flow_from_directory(
        'Documents/Dev',
        target_size=(299, 299),
        batch_size=batch_size,
        class_mode='binary')
#w1 =  tf.Variable(tf.truncated_normal([784, 30], stddev=0.1))
model.fit_generator(
        train_generator,
        steps_per_epoch=50 // batch_size,
        verbose = 1,
        epochs=10,
        validation_data=validation_generator,
        validation_steps=8 // batch_size)

在我运行时哪个产生以下输出。就我的体系结构或数据生成步骤而言，我这里还缺少什么？我引用了Tensorflow model accuracy not increasing和accuracy not increasing in tensorflow model都没有用。

Epoch 1/10
3/3 [==============================] - 2s 593ms/step - loss: 0.6719 - accuracy: 0.6250 - val_loss: 0.8198 - val_accuracy: 0.5000
Epoch 2/10
3/3 [==============================] - 2s 607ms/step - loss: 0.6521 - accuracy: 0.6667 - val_loss: 0.8518 - val_accuracy: 0.5000
Epoch 3/10
3/3 [==============================] - 2s 609ms/step - loss: 0.6752 - accuracy: 0.6250 - val_loss: 0.7129 - val_accuracy: 0.5000
Epoch 4/10
3/3 [==============================] - 2s 611ms/step - loss: 0.6841 - accuracy: 0.6250 - val_loss: 0.7010 - val_accuracy: 0.5000
Epoch 5/10
3/3 [==============================] - 2s 608ms/step - loss: 0.6977 - accuracy: 0.5417 - val_loss: 0.6551 - val_accuracy: 0.5000
Epoch 6/10
3/3 [==============================] - 2s 607ms/step - loss: 0.6508 - accuracy: 0.7083 - val_loss: 0.5752 - val_accuracy: 0.5000
Epoch 7/10
3/3 [==============================] - 2s 615ms/step - loss: 0.6596 - accuracy: 0.6875 - val_loss: 0.9326 - val_accuracy: 0.5000
Epoch 8/10
3/3 [==============================] - 2s 604ms/step - loss: 0.7022 - accuracy: 0.6458 - val_loss: 0.6976 - val_accuracy: 0.5000
Epoch 9/10
3/3 [==============================] - 2s 591ms/step - loss: 0.6331 - accuracy: 0.7292 - val_loss: 0.9571 - val_accuracy: 0.5000
Epoch 10/10
3/3 [==============================] - 2s 595ms/step - loss: 0.6085 - accuracy: 0.7292 - val_loss: 0.6029 - val_accuracy: 0.5000
Out[24]: <keras.callbacks.callbacks.History at 0x1ee4e3a8f08>

Answer 1

您正在设置每个时期的训练步骤= 50 // 32 = 1。那么，您只有50张训练图像吗？同样，对于验证，您的步骤= 8 // 32 = 0。您只有8张验证图片吗？当您执行程序时，训练生成器和验证生成器会打印出多少张找到的图像？您将需要更多的图像。尝试将批量大小设置为= 1

Tensorflow模型验证准确性没有增加

1 个答案: