Replacing placeholder for tensorflow v2

Question

For my project, I need to convert a directed graph into a tensorflow implementation of the graph as if it was a neural network. In tensorflow version 1, I could just define

Answer 1

Make your code work with TF 2.0

Below is a sample code which you can use with TF 2.0. It relies on the compatibility API that is accessible as tensorflow.compat.v1, and requires to disable v2 behaviors. I don't know if it behaves as you expected. If not, then provide us more explanation of what you try to achieve.

import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

@tf.function
def construct_graph(graph_dict, inputs, outputs):
    queue = inputs[:]
    make_dict = {}
    for key, val in graph_dict.items():
        if key in inputs:
            make_dict[key] = tf.placeholder(tf.float32, name=key)
        else:
            make_dict[key] = None
    # Breadth-First search of graph starting from inputs
    while len(queue) != 0:
        cur = graph_dict[queue[0]]
        for outg in cur["outgoing"]:
            if make_dict[outg[0]]: # If discovered node, do add/multiply operation
                make_dict[outg[0]] = tf.add(make_dict[outg[0]], tf.multiply(outg[1], make_dict[queue[0]]))
            else: # If undiscovered node, input is just coming in multiplied and add outgoing to queue
                make_dict[outg[0]] = tf.multiply(make_dict[queue[0]], outg[1])
                for outgo in graph_dict[outg[0]]["outgoing"]:
                    queue.append(outgo[0])
        queue.pop(0)
    # Returns one data graph for each output
    return [make_dict[x] for x in outputs]

def main():
    graph_def = {
        "B": {
            "incoming": [],
            "outgoing": [("A", 1.0)]
        },
        "C": {
            "incoming": [],
            "outgoing": [("A", 1.0)]
        },
        "A": {
            "incoming": [("B", 2.0), ("C", -1.0)],
            "outgoing": [("D", 3.0)]
        },
        "D": {
            "incoming": [("A", 2.0)],
            "outgoing": []
        }
    }
    outputs = construct_graph(graph_def, ["B", "C"], ["A"])
    print(outputs)

if __name__ == "__main__":
    main()

[<tf.Tensor 'PartitionedCall:0' shape=<unknown> dtype=float32>]

 Migrate your code to TF 2.0

While the above snippet is valid, it is still tied to TF 1.0. To migrate it to TF 2.0 you have to refactor a little bit your code.

Instead of returning a list of tensors, which were callables with TF 1.0, I advise you to return a list of keras.layers.Model.

Below is a working example:

import tensorflow as tf

def construct_graph(graph_dict, inputs, outputs):
    queue = inputs[:]
    make_dict = {}
    for key, val in graph_dict.items():
        if key in inputs:
            # Use keras.Input instead of placeholders
            make_dict[key] = tf.keras.Input(name=key, shape=(), dtype=tf.dtypes.float32)
        else:
            make_dict[key] = None
    # Breadth-First search of graph starting from inputs
    while len(queue) != 0:
        cur = graph_dict[queue[0]]
        for outg in cur["outgoing"]:
            if make_dict[outg[0]] is not None: # If discovered node, do add/multiply operation
                make_dict[outg[0]] = tf.keras.layers.add([
                    make_dict[outg[0]],
                    tf.keras.layers.multiply(
                        [[outg[1]], make_dict[queue[0]]],
                    )],
                )
            else: # If undiscovered node, input is just coming in multiplied and add outgoing to queue
                make_dict[outg[0]] = tf.keras.layers.multiply(
                    [make_dict[queue[0]], [outg[1]]]
                )
                for outgo in graph_dict[outg[0]]["outgoing"]:
                    queue.append(outgo[0])
        queue.pop(0)
    # Returns one data graph for each output
    model_inputs = [make_dict[key] for key in inputs]
    model_outputs = [make_dict[key] for key in outputs]
    return [tf.keras.Model(inputs=model_inputs, outputs=o) for o in model_outputs]

def main():
    graph_def = {
        "B": {
            "incoming": [],
            "outgoing": [("A", 1.0)]
        },
        "C": {
            "incoming": [],
            "outgoing": [("A", 1.0)]
        },
        "A": {
            "incoming": [("B", 2.0), ("C", -1.0)],
            "outgoing": [("D", 3.0)]
        },
        "D": {
            "incoming": [("A", 2.0)],
            "outgoing": []
        }
    }
    outputs = construct_graph(graph_def, ["B", "C"], ["A"])
    print("Builded models:", outputs)
    for o in outputs:
        o.summary(120)
        print("Output:", o((1.0, 1.0)))

if __name__ == "__main__":
    main()

What to notice here?

• Change from placeholder to keras.Input, requiring to set the shape of the input.
• Use keras.layers.[add|multiply] for computation. This is probably not required, but stick to one interface. However, it requires to wrap factors inside a list (to handle batching)
• Build keras.Model to return
• Call your model with a tuple of values (not a dictionary anymore)

Here is the output of the code.

Builded models: [<tensorflow.python.keras.engine.training.Model object at 0x7fa0b49f0f50>]
Model: "model"
________________________________________________________________________________________________________________________
Layer (type)                           Output Shape               Param #       Connected to                            
========================================================================================================================
B (InputLayer)                         [(None,)]                  0                                                     
________________________________________________________________________________________________________________________
C (InputLayer)                         [(None,)]                  0                                                     
________________________________________________________________________________________________________________________
tf_op_layer_mul (TensorFlowOpLayer)    [(None,)]                  0             B[0][0]                                 
________________________________________________________________________________________________________________________
tf_op_layer_mul_1 (TensorFlowOpLayer)  [(None,)]                  0             C[0][0]                                 
________________________________________________________________________________________________________________________
add (Add)                              (None,)                    0             tf_op_layer_mul[0][0]                   
                                                                                tf_op_layer_mul_1[0][0]                 
========================================================================================================================
Total params: 0
Trainable params: 0
Non-trainable params: 0
________________________________________________________________________________________________________________________
Output: tf.Tensor([2.], shape=(1,), dtype=float32)