Keras CNN-LSTM RuntimeError

Question

Я пытаюсь использовать следующую модель Inceptionv3 base CNN и LSTM для решения проблемы регрессии. Мои входные данные - изображения с непрерывными целевыми значениями. Я хотел бы передать последовательность изображений в CNN, а затем в слой LSTM. Однако я получаю

RuntimeError: You must compile your model before using it 

сообщение. Есть идеи, что может быть причиной? Я пытался найти это в github и на нескольких страницах, но у меня ничего не получилось.

from keras.applications.inception_v3 import InceptionV3
from keras.models import Sequential, Model
from keras.preprocessing.image import ImageDataGenerator
from keras.layers import Dense, Activation, Flatten, Dropout, BatchNormalization, Conv2D, MaxPooling2D, GlobalAveragePooling2D, LSTM, TimeDistributed, Input
from keras.optimizers import SGD, RMSprop
from keras.callbacks import EarlyStopping, ModelCheckpoint, CSVLogger, ReduceLROnPlateau
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np


# custom R2-score metrics for keras backend
#https://www.kaggle.com/c/mercedes-benz-greener-manufacturing/discussion/34019
from keras import backend as K

def r2_keras(y_true, y_pred):
    SS_res =  K.sum(K.square(y_true - y_pred))
    SS_tot = K.sum(K.square(y_true - K.mean(y_true)))
    return ( 1 - SS_res/(SS_tot + K.epsilon()) )  

train_data_dir = '...'
test_data_dir = '...'

train_df = pd.read_csv('...')
valid_df = pd.read_csv('...')

filepath_loss = '...'
filepath_csv = '...'

datagen=ImageDataGenerator(rescale=1./255.,)


img_width, img_height = 380, 380
frames = 5
channels = 3

pictures = Input(shape=(frames, img_width, img_height, channels))

train_generator=datagen.flow_from_dataframe(
dataframe=train_df,
directory=train_data_dir,
x_col="block_heights",
y_col="weighted_prices",
has_ext=False,      #x_col column doesnt has the file extensions
#subset="training",     if validation split is set in ImageDataGenerator
batch_size=16,
seed=42,
shuffle=False,
class_mode="other",  #for regression other should be used
target_size=(img_width, img_height))

valid_generator=datagen.flow_from_dataframe(
dataframe=valid_df,
directory=train_data_dir,
x_col="block_heights",
y_col="weighted_prices",
has_ext=False,              #x_col column doesnt has the file extensions
#subset="validation",      if validation split is set in ImageDataGenerator
batch_size=16,
seed=42,
shuffle=False,
class_mode="other",
target_size=(img_width, img_height))  

conv_base = InceptionV3(weights=None, include_top=False, input_shape=(img_width,img_height,3))
conv_base.trainable = True

model = Sequential()
model.add(TimeDistributed(conv_base))
model.add(TimeDistributed(Flatten()))
model.add(LSTM(10, return_sequences=True))
model.add(Dense(512, activation='relu'))
model.add(Dense(1, activation='linear'))

#error at callbacks if the learning rate is explicitly set somewhere
rms = RMSprop(lr=0.1, rho=0.9, epsilon=None, decay=0.0)

model.compile(loss='mse', optimizer=rms, metrics=['mae', r2_keras])

STEP_SIZE_TRAIN=train_generator.n//train_generator.batch_size
STEP_SIZE_VALID=valid_generator.n//valid_generator.batch_size

callbacks = [EarlyStopping(monitor='val_loss', patience=10, verbose=1, mode='auto'),
            ReduceLROnPlateau(monitor='val_loss', factor=0.02, patience=3, min_lr=0.001),
            ModelCheckpoint(filepath_loss, monitor='val_loss', verbose=1, save_best_only=True, mode='min'),
            CSVLogger(filepath_csv, separator = ",", append = False)]


history = model.fit_generator(generator=train_generator,steps_per_epoch=STEP_SIZE_TRAIN, validation_data=valid_generator, validation_steps=STEP_SIZE_VALID, epochs=50, callbacks=callbacks)