demonstrate fail to load with multiple output metrics in dictionary

keras_test_case_load_model.py

 keras_bug.py                                                                                  << buffers 
from keras.layers import Input, Dense
from keras.models import Model, load_model
import numpy as np

input_layer = Input(shape=(5,))
hidden = Dense(5)(input_layer)
output1 = Dense(1, name='output1')(hidden)
output2 = Dense(1, name='output2')(hidden)

m = Model(inputs=input_layer, outputs=[output1, output2])

metrics = {'output1': ['mse', 'binary_accuracy'], 'output2': ['mse', 'binary_accuracy']}
loss = {'output1': 'mse', 'output2': 'mse'}

m.compile(loss=loss, optimizer='sgd', metrics=metrics)

# assure that model is working
X = np.array([[1,1,1,1,1]])
m.predict(X)

# save model
m.save('saved_model.h5')

model = load_model('saved_model.h5')