Keras CNN維度問題

Question

我想基於this文章基於圖像分割任務使用Keras構建CNN。由於我的數據集很小，我想使用Keras ImageDataGenerator並將其送到fit_generator()。所以，我沿着Keras網站上的example。但是，由於壓縮圖像和掩膜生成器不起作用，我遵循此answer並創建了我自己的生成器。

我的輸入數據大小爲(701,256,1)，我的問題是二進制（前景，背景）。對於每張圖片，我都有一個相同形狀的標籤。

現在，我正面臨維度問題。 answer中也提到了這一點，但我不確定如何解決它。

錯誤：

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

我粘貼整個代碼我這裏有：

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') 

我是新來Keras（和細胞神經網絡），所以任何幫助將是非常讚賞。

Answer 1

好的。我做了一些rubberduck調試，並閱讀了更多的文章。當然，維度是一個問題。 This簡單的回答爲我做了。 我的標籤與輸入圖像的形狀相同，因此模型的輸出也應具有該形狀。我用Conv2DTranspose來解決這個問題。