I am trying to expand dimension:
import tensorflow as tf
inp = tf.keras.layers.Input(shape=(1,))
inp = inp[..., tf.newaxis]
decoder_input = inp
output = tf.concat([inp, decoder_input], 1)
model = tf.keras.models.Model(inp, output )
But I get an error in the last line:
Exception has occurred: ValueError Graph disconnected: cannot obtain value for tensor Tensor("input_1:0", shape=(None, 1), dtype=float32) at layer "tf_op_layer_strided_slice". The following previous layers were accessed without issue: []
Is this what you are trying to do? Seems you have a variable conflict. You are setting the decoder_input as a reshape layer instead of an input layer. Changing the name of the reshape layer fixes it.
import tensorflow as tf
inp = tf.keras.layers.Input(shape=(1,))
x = tf.keras.layers.Reshape((-1,1))(inp) #Use any of the 3
#x = tf.expand_dims(inp, axis=-1)
#x = inp[...,tf.newaxis]
decoder_input = inp
output = tf.concat([inp, decoder_input], 1)
model = tf.keras.models.Model(inp, output)
model.summary()
Model: "functional_8"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_7 (InputLayer) [(None, 1)] 0
__________________________________________________________________________________________________
tf_op_layer_concat_4 (TensorFlo [(None, 2)] 0 input_7[0][0]
input_7[0][0]
==================================================================================================
Total params: 0
Trainable params: 0
Non-trainable params: 0