AttributeError: 'Node' object has no attribute 'output_masks'

[Ranjeet Singh]

I'm getting above mentioned error on this line of the code -
slice_n = Lambda(get_slice, output_shape=input_shape, arguments={'idx':i,'parts':gpu_count})(x)

Here is my model

def build_model(shape, params):

input_dim, hidden_units, num_classes = shape
audio_sequence = Input(shape=(None,input_dim),dtype='float32',name='audio')

encode = _Encoding(input_dim, hidden_units, hidden_units, params['kernel_size'], params['dropout'], params['L2'])
hide = _Hidden(2*hidden_units, params['dropout'], params['L2'])
attend = _Attention(2*hidden_units)
classify = _Classifier(2*hidden_units, num_classes, params['dropout'], params['L2'])

encoding = encode(audio_sequence)
hidden = hide(encoding)
attention_weight = attend(hidden)
align = _align(encoding, attention_weight)
scores = classify(align)

model = Model(inputs=[audio_sequence], outputs=[scores])
model.compile(optimizer=Adam(lr=params['lr']),
              loss='categorical_crossentropy',
              metrics=['accuracy'])
return model

class _Encoding(object):

def __init__(self, input_dim, num_filters, hidden_units, kernel_size, dropout=0.0, L2=10**-5):

    self.model = Sequential()
    self.model.add(Conv1D(num_filters,
                          kernel_size,
                          padding='same',
                          activation='relu',
                          kernel_regularizer=l2(L2),
                          input_shape=(None, input_dim)
                          ))
    self.model.add(Bidirectional(LSTM(hidden_units,
                                      dropout=dropout,
                                      recurrent_dropout=dropout,
                                      return_sequences=True,
                                      unroll=False,
                                      )))

def __call__(self, sequence):
    return self.model(sequence)

class _Hidden(object):

def __init__(self, hidden_units, dropout=0.0, L2=10**-5):

    self.model = Sequential()
    self.model.add(Dropout(dropout, input_shape=(hidden_units,)))
    self.model.add(Dense(hidden_units,
                         activation='tanh',
                         name='attend',
                         kernel_initializer='he_normal',
                         kernel_regularizer=l2(L2),
                         ))
    self.model = TimeDistributed(self.model)

def __call__(self, encoding):
    return self.model(encoding)

class _Attention(object):

def __init__(self, input_dim):

    self.model = Sequential()
    self.model.add(Dense(1,
                         activation=None,
                         use_bias=False,
                         kernel_initializer='he_normal',
                         input_shape=(input_dim,),
                         ))
    self.model = TimeDistributed(self.model)

    self.output = Sequential()
    self.output.add(Flatten())
    self.output.add(Activation('softmax'))

def __call__(self, encoding):
    return self.output(self.model(encoding))

def _align(encoding, attention_weight):

return Dot((1,1), normalize=False)([encoding, attention_weight])

class _Classifier(object):

def __init__(self, hidden_units, output_dim, dropout=0.0, L2=10**-5):

    self.model = Sequential()
    self.model.add(Dropout(dropout, input_shape=(hidden_units,)))
    self.model.add(Dense(hidden_units,
                         activation='relu',
                         name='classifier_hidden',
                         kernel_initializer='he_normal',
                         kernel_regularizer=l2(L2),
                         ))
    self.model.add(Dense(output_dim,
                         activation='softmax',
                         name='scores',
                         kernel_initializer='he_normal',
                         kernel_regularizer=l2(L2),
                         ))

def __call__(self, align):
    return self.model(align)

def test_build():

shape = (26, 100, 5)
params = {}
params['dropout'] = 0.0
params['L2'] = 10**-5
params['kernel_size'] = 5
params['lr'] = 0.001
return build_model(shape, params)

model = test_build()
Can please someone help me ?