How to count total number of trainable parameters in a tensorflow model?

Question

Is there a function call or another way to count the total number of parameters in a tensorflow model?

By parameters I mean: an N dim vector of trainable variables has N parameters, a NxM matrix has N*M parameters, etc. So essentially I'd like to sum the product of the shape dimensions of all the trainable variables in a tensorflow session.

Answer 1

Loop over the shape of every variable in tf.trainable_variables().

total_parameters = 0
for variable in tf.trainable_variables():
    # shape is an array of tf.Dimension
    shape = variable.get_shape()
    print(shape)
    print(len(shape))
    variable_parameters = 1
    for dim in shape:
        print(dim)
        variable_parameters *= dim.value
    print(variable_parameters)
    total_parameters += variable_parameters
print(total_parameters)

Update: I wrote an article to clarify the dynamic/static shapes in Tensorflow because of this answer: https://pgaleone.eu/tensorflow/2018/07/28/understanding-tensorflow-tensors-shape-static-dynamic/