使用keras ImageGenerator训练多输入模型

Question

我已经建立了一个模型其中两个分支然后合并成一个单一的。对于模型的培训，我想使用ImageGenerator来设计图像数据，但不知道如何为混合输入类型工作。有人知道如何在keras中处理这个问题吗？ 任何帮助将不胜感激！

最佳， 尼克

MODEL 的第一branchen拍摄图像作为输入：

img_model = Sequential() 
img_model.add(Convolution2D(4, 9,9, border_mode='valid', input_shape=(1, 120, 160))) 
img_model.add(Activation('relu')) 
img_model.add(MaxPooling2D(pool_size=(2, 2))) 
img_model.add(Dropout(0.5)) 
img_model.add(Flatten()) 

的第二分支取辅助数据作为输入：

aux_model = Sequential() 
aux_model.add(Dense(3, input_dim=3)) 

那么那些得到合并到最终的模型：

model = Sequential() 
model.add(Merge([img_model, aux_model], mode='concat')) 
model.add(Dropout(0.5)) 
model.add(Dense(5)) 
model.add(Activation('softmax')) 
model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy']) 

培训/问题： 我试图做这显然失败的情况如下：

datagen = ImageDataGenerator(
      featurewise_center=False, # set input mean to 0 over the dataset 
      samplewise_center=False, # set each sample mean to 0 
      featurewise_std_normalization=False, # divide inputs by std of the dataset 
      samplewise_std_normalization=False, # divide each input by its std 
      zca_whitening=False, # apply ZCA whitening 
      rotation_range=10, #180, # randomly rotate images in the range (degrees, 0 to 180) 
      width_shift_range=0.1, # randomly shift images horizontally (fraction of total width) 
      height_shift_range=0.1, # randomly shift images vertically (fraction of total height) 
      horizontal_flip=False, # randomly flip images 
      vertical_flip=False) # randomly flip images 

model.fit_generator(datagen.flow([X,I], Y, batch_size=64), 
       samples_per_epoch=X.shape[0], 
       nb_epoch=20, 
       validation_data=([Xval, Ival], Yval)) 

这将产生以下错误信息：

Traceback (most recent call last): 
    File "importdata.py", line 139, in <module> 
    model.fit_generator(datagen.flow([X,I], Y, batch_size=64), 
    File "/usr/local/lib/python3.5/dist-packages/keras/preprocessing/image.py", line 261, in flow 
    save_to_dir=save_to_dir, save_prefix=save_prefix, save_format=save_format) 
    File "/usr/local/lib/python3.5/dist-packages/keras/preprocessing/image.py", line 454, in __init__ 
    'Found: X.shape = %s, y.shape = %s' % (np.asarray(X).shape, np.asarray(y).shape)) 
    File "/usr/local/lib/python3.5/dist-packages/numpy/core/numeric.py", line 482, in asarray 
    return array(a, dtype, copy=False, order=order) 
ValueError: could not broadcast input array from shape (42700,1,120,160) into shape (42700) 

Answer 1

我想我有办法让m做这个工作。假设我们有多个输入模型。

#declare a final model with multiple inputs. 
# final_model ... 

train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2) 
train_generator = train_datagen.flow_from_directory(train_data_dir, target_size=(224, 224), batch_size=32, class_mode='binary') 

# NOTE: the zip combining multiple image generators with on the fly augmentation. 
final_generator = zip(train_generator, train_generator)  
final_model.fit_generator(final_generator, samples_per_epoch=nb_train_samples, nb_epoch=nb_epoch, validation_data=test_generator, nb_val_samples=nb_validation_samples)