Question

我正在尝试使用Keras构建基于this文章的图像分割任务的CNN。因为我的数据集很小，所以我想使用Keras ImageDataGenerator并将其提供给fit_generator()。所以，我关注了Keras网站上的example。但是，由于压缩图像和掩码生成器不起作用，我按照answer创建了自己的生成器。

我的输入数据大小为(701,256,1)，我的问题是二进制（前景，背景）。对于每个图像，我都有一个相同形状的标签。

现在，我正面临一个维度问题。这也在answer中提到过，但我不确定如何解决它。

错误：

 ValueError: Error when checking target: expected dense_3 to have 2 dimensions, but got array with shape (2, 704, 256, 1)

我正在粘贴我在这里的所有代码：

import numpy
import pygpu
import theano
import keras

from keras.models import Model, Sequential
from keras.layers import Input, Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D, Reshape
from keras.layers import BatchNormalization
from keras.preprocessing.image import ImageDataGenerator

from keras.utils import np_utils
from keras import backend as K

def superGenerator(image_gen, label_gen):
    while True:
      x = image_gen.next()
      y = label_gen.next()
      yield x[0], y[0]


img_height = 704
img_width = 256

train_data_dir = 'Dataset/Train/Images'
train_label_dir = 'Dataset/Train/Labels'
validation_data_dir = 'Dataset/Validation/Images'
validation_label_dir = 'Dataset/Validation/Labels'
n_train_samples = 1000
n_validation_samples = 500
epochs = 50
batch_size = 2

input_shape = (img_height, img_width,1)
target_shape = (img_height, img_width)

model = Sequential()

model.add(Conv2D(80,(28,28), input_shape=input_shape))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))

model.add(Conv2D(96,(18,18)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))

model.add(Conv2D(128,(13,13)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))


model.add(Conv2D(160,(8,8)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Flatten())

model.add(Dense(1024, activation='relu'))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.25))

model.add(Dense(2, activation='softmax'))

model.summary()

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

data_gen_args = dict(
    rescale=1./255,
    horizontal_flip=True,
    vertical_flip=True
    )

train_datagen = ImageDataGenerator(**data_gen_args)
train_label_datagen = ImageDataGenerator(**data_gen_args)
test_datagen = ImageDataGenerator(**data_gen_args)
test_label_datagen = ImageDataGenerator(**data_gen_args)

seed = 1

train_image_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=target_shape,
    color_mode='grayscale',
    batch_size=batch_size,
    class_mode = 'binary',
    seed=seed)
train_label_generator = train_label_datagen.flow_from_directory(
    train_label_dir,
    target_size=target_shape,
    color_mode='grayscale',
    batch_size=batch_size,
    class_mode = 'binary',
    seed=seed)

validation_image_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=target_shape,
    color_mode='grayscale',
    batch_size=batch_size,
    class_mode = 'binary',
    seed=seed)

validation_label_generator = test_label_datagen.flow_from_directory(
    validation_label_dir,
    target_size=target_shape,
    color_mode='grayscale',
    batch_size=batch_size,
    class_mode = 'binary',
    seed=seed)

train_generator = superGenerator(train_image_generator, train_label_generator,batch_size)
test_generator = superGenerator(validation_image_generator, validation_label_generator,batch_size)

model.fit_generator(
    train_generator,
    steps_per_epoch= n_train_samples // batch_size,
    epochs=50,
    validation_data=test_generator,
    validation_steps=n_validation_samples // batch_size)

model.save_weights('first_try.h5')

我是Keras（和CNNs）的新手，所以任何帮助都会非常感激。

Answer 1

确定。我做了一些橡皮擦调试并阅读了一些文章。当然，维度是一个问题。 This简单的答案为我做了。我的标签形状与输入图像相同，因此模型的输出也应该是该形状。我用Conv2DTranspose来解决这个问题。

Keras CNN维度问题

1 个答案: