I am trying to re-implement Multi-View CNN (MVCNN) in Tensorflow 2.0. However, from what I see, keras layers do not have the options reuse=True|False like in tf.layers. Is there any way that I can define my layers which share parameters using the new API? Or I need to build my model in a TFv1 fashion?
Thank you very much!
To share the parameters of a model you just have to use the same model. This is the new paradigm introduced in TensorFlow 2.0;
In TF 1.xt we were using a graph-oriented approach, where we need to re-use the same graph to share the variables, but now we can just re-use the same tf.keras.Model object with different inputs.
Is the object that carries its own variables.
Using a Keras model and tf.GradientTape you can train a model sharing the variables easily as shown in the example below.
# This is your model definition
model = tf.keras.Sequential(...)
#input_1,2 are model different inputs
with tf.GradientTape() as tape:
a = model(input_1)
b = model(input_2)
# you can just copute the loss
loss = a + b
# Use the tape to compute the gradients of the loss
# w.r.t. the model trainable variables
grads = tape.gradient(loss, model.trainable_varibles)
# Opt in an optimizer object, like tf.optimizers.Adam
# and you can use it to apply the update rule, following the gradient direction
opt.apply_gradients(zip(grads, model.trainable_variables))