In case that you want to connect a placeholder to a new layer you can do as below

x = tf.placeholder(shape=[None, 784], dtype=tf.float32) # mnist for example

x = tf.reshape(x, (-1, 784, 1)) # change to new shape (784 ,1)

x = tf.unstack(self._input,axis=1) # getting a list with 784 elements of 1

con = tf.concat(x[1,2,3,4,5]) # for instance you want only from 1 to 5 inputs/neurons

x = tf.placeholder(shape=[None, 784], dtype=tf.float32) # mnist for example

x = tf.reshape(x, (-1, 784, 1)) # change to new shape (784 ,1)

x = tf.unstack(self._input,axis=1) # getting a list with 784 elements of 1

con = tf.concat(x[1,2,3,4,5]) # for instance you want only from 1 to 5 inputs/neurons

you can feed your new layer with above con which is only corresponds from the input 1 to 5. you can apply same technique to a layer instead of tf.placeholder

In case that you want to connect a placeholder to a new layer you can do as below

x = tf.placeholder(shape=[None, 784], dtype=tf.float32) # mnist for example

x = tf.reshape(x, (-1, 784, 1)) # change to new shape (784 ,1)

x = tf.unstack(self._input,axis=1) # getting a list with 784 elements of 1

con = tf.concat(x[1,2,3,4,5]) # for instance you want only from 1 to 5 inputs/neurons

you can feed your new layer with above con which is only corresponds from the input 1 to 5. you can apply same technique to a layer instead of tf.placeholder

In case that you want to connect a placeholder to a new layer you can do as below

x = tf.placeholder(shape=[None, 784], dtype=tf.float32) # mnist for example

x = tf.reshape(x, (-1, 784, 1)) # change to new shape (784 ,1)

x = tf.unstack(self._input,axis=1) # getting a list with 784 elements of 1

con = tf.concat(x[1,2,3,4,5]) # for instance you want only from 1 to 5 inputs/neurons

you can feed your new layer with above con which is only corresponds from the input 1 to 5. you can apply same technique to a layer instead of tf.placeholder

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In case that you want to connect a placeholder to a new layer you can do as below

x = tf.placeholder(shape=[None, 784], dtype=tf.float32) # mnist for example

x = tf.reshape(x, (-1, 784, 1)) # change to new shape (784 ,1)

x = tf.unstack(self._input,axis=1) # getting a list with 784 elements of 1

con = tf.concat(x[1,2,3,4,5]) # for instance you want only from 1 to 5 inputs/neurons

you can feed your new layer with above con which is only corresponds from the input 1 to 5. you can apply same technique to a layer instead of tf.placeholder