Adding outputs of two layers in keras - python-2.7

Question

I have an issue that seems to have no straight forward solution in Keras.
My server runs on ubuntu 14.04, keras with backend tensorflow.
Here's the issue:
I have two input tenors of the shape: Input(shape=(30,125,1)), each of them is fed to a cascade of three layers below:
CNN1 = Conv2D(filters = 8, kernel_size = (1,64) , padding = "same" , activation = "relu" )
CNN2 = Conv2D(filters = 8, kernel_size = (8,1) , padding = "same" , activation = "relu" )
pool = MaxPooling2D((2, 2))
Each of the obtained output tensors for respective inputs is of shape (None, 15, 62, 8). Now, I wish to add each of the (15,62) matrix for both inputs for each filter and get an output of dimension again (None, 15, 62, 8).
I tried with the following lines of code using Lambda layer but it throws an error.
from keras import backend as K
from keras.layers import Lambda
def myadd(x):
increment = x[1]
result = K.update_add(x[0], increment)
return result
in_1 = Input(shape=(30,125,1))
in_1CNN1 = CNN1(in_1)
in_1CNN2 = CNN2(in_1CNN1)
in_1pool = pool(in_1CNN2)
in_2 = Input(shape=(30,125,1))
in_2CNN1 = CNN1(in_2)
in_2CNN2 = CNN2(in_2CNN1)
in_2pool = pool(in_2CNN2)
y1 =y1.astype(np.float32) # an input regression label array of shape (numsamples,1) loaded from a mat file
out1 = Lambda(myadd, output_shape=(None, 15, 62, 8))([in_1pool,in_2pool])
a= keras.layers.Flatten()(out1)
pre1 = Dense(1000, activation='sigmoid')(a)
pre2 =Dropout(0.2)(pre1)
predictions = Dense(1, activation='sigmoid')(pre2)
model = Model(inputs=[in_1,in_2], outputs=predictions)
model.compile(optimizer='sgd',loss='mean_squared_error')
model.fit([inputdata1,inputdata2], y1, epochs=20, validation_split=0.5)
#inputdata1, inputdata2 are arrays loaded from a mat file and are each of shape (5169, 30, 125, 1)
The error is highlighted below:
Traceback (most recent call last):
File "keras_workshop/keras_multipleinputs_multiple CNN.py", line 225, in <module>
out1 = Lambda(myadd, output_shape=(None, 15, 62, 8))([in_1pool,in_2pool])
File "/home/tharun/anaconda2/lib/python2.7/site-packages/keras/engine/topology.py", line 603, in __call__
output = self.call(inputs, **kwargs)
File "/home/tharun/anaconda2/lib/python2.7/site-packages/keras/layers/core.py", line 651, in call
return self.function(inputs, **arguments)
File "keras_workshop/keras_multipleinputs_multiple CNN.py", line 75, in myadd
result = K.update_add(x[0], increment)
File "/home/tharun/anaconda2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py", line 958, in update_add
return tf.assign_add(x, increment)
File "/home/tharun/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/state_ops.py", line 245, in assign_add
return ref.assign_add(value)
AttributeError: 'Tensor' object has no attribute 'assign_add'

Answer 1

Try the Add() layer or the add() function that Keras provides.
Add
keras.layers.Add()
Layer that adds a list of inputs.
It takes as input a list of tensors, all of the same shape, and returns a single tensor (also of the same shape).
add
keras.layers.add(inputs)
Functional interface to the Add layer.
Arguments
inputs: A list of input tensors (at least 2).
**kwargs: Standard layer keyword arguments.
Returns
A tensor, the sum of the inputs.