keras tensorflow load_weights fails - tensorflow

Question

I am using keras 1.2 with tensorflow 1.0.0 backend.
I have a function that loads a pre-calibrated model from json and then loads its weights from a hdf5 file.
def load():
model = model_from_json(open(model_path).read())
model.load_weights(model_weights_path)
This function, more precisely the call to load_weights results in the following exception:
RuntimeError: The Session graph is empty. Add operations to the graph before calling run()
I wonder if that is due to these lines that I put in the beginning of my module to set the tensorflow seed for reproducibility:
tf.set_random_seed(123) # To set Tensorflow seed
sess = tf.Session()
keras.backend.set_session(sess)
It seems the keras session does not automatically set the loaded model as the graph associated to the session, hence failing to initialize the weights.
Any explanation and workaround to avoid the exception?

Answer 1

I pretty much am using the same code as you and it works for me.
from keras.models import Sequential
from keras.models import Model
from keras.layers import Dense, Dropout, Activation, Flatten, Input, GlobalAveragePooling2D
from keras.optimizers import RMSprop
from keras.utils import np_utils
from keras.models import model_from_json
from keras.layers import Convolution2D, MaxPooling2D
from keras.layers.pooling import AveragePooling2D
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import ZeroPadding2D
from keras.engine.topology import Merge
from keras.layers import merge
from keras.optimizers import Adam
from keras import backend as K
from keras.layers.pooling import MaxPooling2D
from keras.layers.convolutional import ZeroPadding2D
import PIL
import inception
import tensorflow as tf
import keras
import glob
import pandas as pd
import pickle
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
# load json and create model
json_file = open('model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
# load weights into new model
model.load_weights("model.h5")
print("Loaded model from disk")
model.summary()
model.compile(Adam(lr=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])
score = model.predict(transfer_values_test)

Answer 2

Indeed it seems that Keras doesn't respect the session set by set_session when loading models.
Try forcing Keras to use a particular session by Tensorflow's context manager:
def load():
with sess.as_default():
model = model_from_json(open(model_path).read())
model.load_weights(model_weights_path)''
If Keras still complains, predefine a graph (graph=tf.Graph()) and force model.load_weights to use it by introducing an additional with statement:
def load():
with graph.as_default():
with sess.as_default():
model = model_from_json(open(model_path).read())
model.load_weights(model_weights_path)''