CODEWITHSUNDEEP
pythontensorflowkerasdeep-learning
Noel Saade
Noel Saade

Reputation: 55

AttributeError: 'Model' object has no attribute '_name' during input layer concatenation

I cannot understand what is wrong. I need to have two sets of inputs so i divided them to give each one a name (serving purposes) and then concatenated them to link them to the next layer.

layer_input1  = tf.keras.Input(shape=(None, 1), name='layer1')
layer_input2  = tf.keras.Input(shape=(None, 1), name='layer2')

layer_input = tf.keras.layers.concatenate([layer_input1, layer_input2], name='inputs')
fc_1 = tf.keras.layers.Dense(2,
                             activation='relu')(layer_input)
fc_1 = tf.keras.layers.Dropout(0.5)(fc_1)
fc_2 = tf.keras.layers.Dense(10,
                             activation='relu')(fc_1)
output_layer = tf.keras.layers.Dense(1,
                             activation='relu', name='predictions')(fc_2)
model = tf.keras.Model(inputs=layer_input, outputs=output_layer)

AttributeError                            Traceback (most recent call last)
<ipython-input-430-b567199137e0> in <module>()
     10 output_layer = tf.keras.layers.Dense(1,
     11                              activation='relu', name='predictions')(fc_2)
---> 12 model = tf.keras.Model(inputs=layer_input, outputs=output_layer)

AttributeError: 'Model' object has no attribute '_name'

Upvotes: 1

Views: 1423

Answers (1)

Sharky
Sharky

Reputation: 4543

Just set your input layers as model inputs. 

model = tf.keras.Model(inputs=[layer_input1, layer_input2], outputs=output_layer)

Note that concatenate is an operation, not Layer object. But even if you wrap it as Layer with Lambda it won't possess some attributes of keras.layers.Input

Upvotes: 1

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