TF 2.0 print tensor values

Question

I'm learning the newest release of Tensorflow (2.0) and I have tried to run a simple code to slice a matrix. Using the decorator @tf.function I made the following class:

class Data:
def __init__(self):
    pass

def back_to_zero(self, input):
    time = tf.slice(input, [0,0], [-1,1])
    new_time = time - time[0][0]
    return new_time

@tf.function
def load_data(self, inputs):
    new_x = self.back_to_zero(inputs)
    print(new_x)

So, when run the code using a numpy matrix, I can't retrieve the numbers.

time = np.linspace(0,10,20)
magntiudes = np.random.normal(0,1,size=20)
x = np.vstack([time, magntiudes]).T


d = Data()
d.load_data(x)

Output:

Tensor("sub:0", shape=(20, 1), dtype=float64)

I need to get this tensor in a numpy format, but TF 2.0 has not the class tf.Session to use run() or eval() methods.

Thanks for any help that you can offer me!

Answer 1

Inside the graph indicated by the decorator @tf.function, you can use tf.print to print the values of your tensor.

tf.print(new_x)

Here is how the code can be rewritten

class Data:
    def __init__(self):
        pass

    def back_to_zero(self, input):
        time = tf.slice(input, [0,0], [-1,1])
        new_time = time - time[0][0]
        return new_time

    @tf.function
    def load_data(self, inputs):
        new_x = self.back_to_zero(inputs)
        tf.print(new_x) # print inside the graph context
        return new_x

time = np.linspace(0,10,20)
magntiudes = np.random.normal(0,1,size=20)
x = np.vstack([time, magntiudes]).T

d = Data()
data = d.load_data(x)
print(data) # print outside the graph context

the tensor type outside the tf.decorator context is of type tensorflow.python.framework.ops.EagerTensor. To convert it to a numpy array, you can use data.numpy()