Concatenate input tensor with multiple of neg 1 tensors

Question

Similar Posts: Firstly, these 2 posts are similar if not the same. I tried to implement these in vain. So I\'m missing something probably because of my inexperi

Answer 1

I found the solution for a model that takes in

# a batch of size 1, for a tensor of shape =[3]
d1 = numpy.array([[1, 2, 3]])

and produces as output

out = my_model.predict( [ d1 ] )
print(out)
# [[ 1.  2.  3. -1. -1. -1. -1. -1. -1. -1. -1. -1. -1. -1. -1.]]

The big lesson is that keras.layers want as input, the output of other keras.layers. So functionality from keras.backend like tile must be wrapped in keras.layers.Lambda before feeding them as input to keras.layer.

Thank you to

• Stackoverflow: How to use tile function in Keras?
• github issue: AttributeError: 'NoneType' object has no attribute '_inbound_nodes'
• Stackoverflow: Using Subtract layer in Keras

Here is the solution:

# instantiate a Keras tensor ... as per document https://keras.io/layers/core/
keras_tensor_input = keras.layers.Input( shape=[num_letters], dtype='float32' )
print("keras_tensor_input = ", keras_tensor_input)
# keras_tensor_input =  Tensor("input_1:0", shape=(?, 3), dtype=float32)

# https://stackoverflow.com/questions/53865471/using-subtract-layer-in-keras
keras_tensor_neg_1 = keras.layers.Lambda(lambda x: -keras.backend.ones_like(x) )(keras_tensor_input)
print("keras_tensor_neg_1 = ", keras_tensor_neg_1)
# keras_tensor_neg_1 =  Tensor("lambda_1/Neg:0", shape=(?, 3), dtype=float32)
# note batch size, just like keras_tensor_input

# https://stackoverflow.com/questions/53250533/how-to-use-tile-function-in-keras
keras_tensor_neg_1_tiled = keras.layers.Lambda(lambda x: keras.backend.tile(x, (1, n-1)))(keras_tensor_neg_1)
print("keras_tensor_neg_1_tiled = ", keras_tensor_neg_1_tiled)
# keras_tensor_neg_1_tiled =  Tensor("Tile_2:0", shape=(12,), dtype=float32)
# note batch size, just like keras_tensor_input

# concatenate the input from the generator and the padding
keras_tensor_concat = keras.layers.Concatenate()( inputs = [keras_tensor_input, keras_tensor_neg_1_tiled] )
print("keras_tensor_concat = ", keras_tensor_concat)
# keras_tensor_concat =  Tensor("concatenate_1/concat:0", shape=(?, 15), dtype=float32)

my_model = keras.models.Model( inputs=keras_tensor_input, output=keras_tensor_concat)

# dummy optimizer, loss, and metric so I can compile and test the model
my_model.compile(optimizer='rmsprop',loss='categorical_crossentropy', metrics=['accuracy'])

# a batch of size 1, for a tensor of shape =[3]
d1 = numpy.array([[1, 2, 3]])

out = my_model.predict( [ d1 ] )
print(out)
# [[ 1.  2.  3. -1. -1. -1. -1. -1. -1. -1. -1. -1. -1. -1. -1.]]