Error loading a saved model?

There is my model:

data_dim = 25*88
timesteps = 40

secen = Sequential()
secen.add(Convolution2D(32, 3, 3, activation='relu', border_mode='same', input_shape=(1, 25, 88)))
secen.add(MaxPooling2D((2, 2)))
secen.add(Dropout(0.25))
secen.add(Convolution2D(32, 3, 3, activation='relu', border_mode='same'))
secen.add(MaxPooling2D((2, 2)))
secen.add(Dropout(0.25))
secen.add(Convolution2D(32, 3, 3, activation='relu', border_mode='same'))
secen.add(MaxPooling2D((2, 2)))
secen.add(Dropout(0.25))
secen.add(Flatten())

secde = Sequential()
secde.add(Convolution2D(32, 3, 3,border_mode='same', input_shape=(1, 25, 11)))
secde.add(Activation('tanh'))
secde.add(UpSampling2D(size=(1, 2)))
secde.add(Convolution2D(32, 3, 3, border_mode='same'))
secde.add(Activation('tanh'))
secde.add(UpSampling2D(size=(1, 2)))
secde.add(Convolution2D(32, 3, 3, border_mode='same'))
secde.add(Activation('tanh'))
secde.add(UpSampling2D(size=(1, 2)))
secde.add(Convolution2D(1, 3, 3, border_mode='same'))
secde.add(Activation('sigmoid'))

music_input = Input(shape=(timesteps, 1, 25, 88))

encoded_frame_sequence = TimeDistributed(secen)(music_input)
encoded_music = LSTM(data_dim/8,return_sequences=True)(encoded_frame_sequence)
encoded_music=Dropout(0.25)(encoded_music)
encoded_music=Reshape((timesteps, 1, 25,11 ))(encoded_music)
decode_music=TimeDistributed(secde)(encoded_music)

model=Model(input=music_input,output=decode_music)

model.compile(optimizer='rmsprop',
              loss='binary_crossentropy')

I fitted the model.However when I load it from a .h5 file,i got an error.

model = load_model('midinet_0110.h5')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Anaconda2\Lib\site-packages\keras\models.py", line 179, in load_model
    model.optimizer.set_weights(optimizer_weight_values)
  File "C:\Anaconda2\Lib\site-packages\keras\optimizers.py", line 90, in set_weights
    'provided weight shape ' + str(w.shape))
ValueError: Optimizer weight shape (275L, 275L) not compatible with provided weight shape (1056, 275)

I guess it may caused by the complicacy of my model?

[dustinandrews]

Reproduced with Keras 2.0.5 on Tensorflow with the Adam optimizer. I’m using an Inception V4 model.

[cogitae]

I had same problems with save_model + load_model on trained model with Adam optimizer on Theano. Keras 1.1.1 The problem was that the optimizer weights were not loaded in the same order so there was an assertion on weight shapes. I could solve locally this issue modifying Adam code to set names for each weights it used so that weights matched between save and load. In optimizers.py :

#        self.iterations = K.variable(0)
#TS 20170208 bugfix : save_model + load_model not working after training
        self.iterations = K.variable(0, name='Adam_iter')

#TS 20170208 bugfix : save_model + load_model not working after training
#        ms = [K.zeros(shape) for shape in shapes]
        ms = [K.zeros(shape,name='Adam_p_ms_'+ str(p.name)) for p,shape in zip(params,shapes)]
#        vs = [K.zeros(shape) for shape in shapes]
        vs = [K.zeros(shape,name='Adam_p_vs_'+ str(p.name)) for p,shape in zip(params,shapes)]

I haven’t seen yet if there is a naming convention that should be used and if this is the correct fix, probably all optimizers need it.