python - 具有数据生成器的多输入 keras 神经网络模型

Question

我有两张图片。一只用于左眼，一只用于右眼。我想在神经网络中一次喂它们。

from sklearn.model_selection import train_test_split
XL_train, XL_val, yL_train, yL_val = train_test_split(XL, y, test_size=0.33, random_state=42)
XR_train, XR_val, yR_train, yR_val = train_test_split(XR, y, test_size=0.33, random_state=42)

XL 包含左眼 (3500, 224, 224, 3)
图像 XR 包含右眼图像(3500, 224, 224, 3)

我已经创建了数据生成器并将我的图像转换如下

XR_generator = train_datagen.flow(XR_train, yR_train, batch_size=BATCH_SIZE)
XL_generator = train_datagen.flow(XL_train, yL_train, batch_size=BATCH_SIZE)
vR_generator = val_datagen.flow(XR_val, yR_val, batch_size=BATCH_SIZE)
vL_generator = val_datagen.flow(XL_val, yL_val, batch_size=BATCH_SIZE)

使用 resnet 作为模型

import keras
left_input=Input(shape=XL.shape[1::])
right_input=Input(shape=XR.shape[1::])

left_model = ResNet50(include_top=False,input_tensor=left_input)
for layer in left_model.layers:
        layer.name = layer.name + '_left'
        layer.trainable = True


right_model = ResNet50(include_top=False,input_tensor=right_input)
for layer in right_model.layers:
        layer.name = layer.name + '_right'
        layer.trainable = True

使用 resnet 附加的层

x = keras.layers.concatenate([left_model.output, right_model.output])
x=  keras.layers.Flatten()(x)
x = keras.layers.Dropout(0.2)(x)
x = keras.layers.Dense(512)(x)
x = keras.layers.BatchNormalization()(x)
x = keras.layers.Activation('relu')(x)
x = keras.layers.Dense(512)(x)
x = keras.layers.BatchNormalization()(x)
x = keras.layers.Activation('relu')(x)
x = keras.layers.Dropout(0.2)(x)
out = keras.layers.Dense(8, activation='sigmoid')(x)
model = keras.models.Model(inputs=[left_input, right_input], outputs=out)

编译模型并拟合模型如下

history=model.fit_generator(generator=[XL_generator,XR_generator],
                        steps_per_epoch=steps_train,
                        validation_data=[vL_generator,vR_generator],
                        validation_steps=steps_valid,
                        epochs=10,
                        )

我收到以下错误。
AttributeError：“NumpyArrayIterator”对象没有属性“ndim”

更新

def multi_train_gen(gen,XR_train,XL_train,yR_train,yL_train):
  XR_generator = train_datagen.flow(XR_train, yR_train, batch_size=BATCH_SIZE)
  XL_generator = train_datagen.flow(XL_train, yL_train, batch_size=BATCH_SIZE)
  while True:
    X1i = XR_generator.next()
    X2i = XL_generator.next()
    yield [X1i[0], X2i[0]], X2i[1] 

def multi_val_gen(gen,XR_val,XL_val,yR_val,yL_val):
  vR_generator = val_datagen.flow(XR_val, yR_val, batch_size=BATCH_SIZE)
  vL_generator = val_datagen.flow(XL_val, yL_val, batch_size=BATCH_SIZE)

  while True:
    X1i = vR_generator.next()
    X2i = vL_generator.next()
    yield [X1i[0], X2i[0]], X2i[1] 

训练生成器

train_gen=multi_train_gen(train_datagen,XR_train,XL_train,yR_train,yL_train)    

验证生成器

val_gen=multi_val_gen(val_datagen,XR_val,XL_val,yR_val,yL_val)    

但问题不在于我无法访问课程。我希望我可以使用 scikit learn 的分类报告

标签： pythonkerasdeep-learningconv-neural-networkdata-generation