keras multiple input model doesn't work - keras

Question

i have bulit a model to distinguish cats from dogs using kaggle's cats_vs_dogs data set. I have tried two ways to do it. For the first one, I used three existing models(ResNet50, Xception InceptionV3)to extract features, i put the traing data through these models's convolutional base,predict and concatenate the results, then use them for a standalone densely-connected classifier.The result is pretty good, after five epoches training, val_acc became 99.58%. Then i want to use data augmentation and fine-tuing, so i extended the those three models by adding layers on top, and running he whole thing end-to-end on the input data. The strange thing is the second way got good result in the traing but lousy one in the validation, and the val_acc is always a constant(0.5). i feel very confused, how come these two ways have such different results.
here is my code
from keras.models import *
from keras.layers import *
from keras.applications import *
from keras.preprocessing.image import *
res_net_input = Input((224, 224, 3), name='res_net')
res_net_base_model = ResNet50(input_tensor=res_net_input, weights='imagenet', include_top=False)
for layers in res_net_base_model.layers:
layers.trainable = False
xception_input = Input((299, 299, 3), name='xception')
xception_base_model = Xception(input_tensor=xception_input, weights='imagenet', include_top=False)
for layers in xception_base_model.layers:
layers.trainable = False
inception_input = Input((299, 299, 3), name='inception')
inception_base_model = InceptionV3(input_tensor=inception_input, weights='imagenet', include_top=False)
for layers in inception_base_model.layers:
layers.trainable = False
res_result = GlobalAveragePooling2D()(res_net_base_model.output)
xcp_result = GlobalAveragePooling2D()(xception_base_model.output)
icp_result = GlobalAveragePooling2D()(inception_base_model.output)
concatenated = concatenate([res_result, xcp_result, icp_result], axis=1)
x = Dropout(0.5)(concatenated)
x = Dense(1, activation='sigmoid')(x)
model = Model([res_net_base_model.input, xception_base_model.input, inception_base_model.input], x)
model.compile(optimizer='adadelta',
loss='binary_crossentropy',
metrics=['accuracy'])
train_imgen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
rotation_range=5.,
horizontal_flip = True)
validation_imgen = ImageDataGenerator(rescale = 1./255)
def generate_generator_multiple(generator,dir1, batch_size, img_size1, img_size2, img_size3):
genX1 = generator.flow_from_directory(dir1,
target_size = (img_size1[0],img_size1[1]),
class_mode = 'binary',
batch_size = batch_size,
shuffle=False,
)
genX2 = generator.flow_from_directory(dir1,
target_size = (img_size2[0],img_size2[1]),
class_mode = 'binary',
batch_size = batch_size,
shuffle=False,
seed=7)
genX3 = generator.flow_from_directory(dir1,
target_size = (img_size3[0],img_size3[1]),
class_mode = 'binary',
batch_size = batch_size,
shuffle=False,
seed=7)
while True:
X1i = genX1.next()
X2i = genX2.next()
X3i = genX3.next()
yield [X1i[0], X2i[0],X3i[0]], X1i[1]
tain_generator = generate_generator_multiple(train_imgen , '/output/keras/dog_vs_cat_full/train', 100, (224,224), (299, 299), (299, 299))
validation_generator = generate_generator_multiple(validation_imgen,'/output/keras/dog_vs_cat_full/validation', 100, (224,224), (299, 299), (299, 299))
history=model.fit_generator(tain_generator,
steps_per_epoch=200,
epochs = 5,
validation_data = validation_generator,
validation_steps = 50,
shuffle=False)