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I have upgraded with tf_upgrade_v2 TF1 code to TF2. I'm a noob with both. I got the next error:
RuntimeError: tf.placeholder() is not compatible with eager execution. I have some tf.compat.v1.placeholder().
self.temperature = tf.compat.v1.placeholder_with_default(1., shape=()) self.edges_labels = tf.compat.v1.placeholder(dtype=tf.int64, shape=(None, vertexes, vertexes)) self.nodes_labels = tf.compat.v1.placeholder(dtype=tf.int64, shape=(None, vertexes)) self.embeddings = tf.compat.v1.placeholder(dtype=tf.float32, shape=(None, embedding_dim)) Could you give me any advice about how to proceed? Any "fast" solutions? or should I to recode this?
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A placeholder is simply a variable that we will assign data to at a later date. It allows us to create our operations and build our computation graph, without needing the data. In TensorFlow terminology, we then feed data into the graph through these placeholders.
With eager execution enabled, TensorFlow functions execute operations immediately (as opposed to adding to a graph to be executed later in a tf. compat. v1. Session ) and return concrete values (as opposed to symbolic references to a node in a computational graph).
Eager execution is a powerful execution environment that evaluates operations immediately. It does not build graphs, and the operations return actual values instead of computational graphs to run later. With Eager execution, TensorFlow calculates the values of tensors as they occur in your code.
compat. v1. disable_eager_execution() This function can only be called before any Graphs, Ops, or Tensors have been created. It can be used at the beginning of the program for complex migration projects from TensorFlow 1.
I found an easy solution here: disable Tensorflow eager execution
Basicaly it is:
tf.compat.v1.disable_eager_execution()
With this, you disable the default activate eager execution and you don't need to touch the code much more.
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