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

Not sure if the answer given actually runs (I found you need to convert the dim object to an int for it to work). Here is is one that works and you can just copy paste the functions and call them (added a few comments too):

def count_number_trainable_params():
    '''
    Counts the number of trainable variables.
    '''
    tot_nb_params = 0
    for trainable_variable in tf.trainable_variables():
        shape = trainable_variable.get_shape() # e.g [D,F] or [W,H,C]
        current_nb_params = get_nb_params_shape(shape)
        tot_nb_params = tot_nb_params + current_nb_params
    return tot_nb_params

def get_nb_params_shape(shape):
    '''
    Computes the total number of params for a given shap.
    Works for any number of shapes etc [D,F] or [W,H,C] computes D*F and W*H*C.
    '''
    nb_params = 1
    for dim in shape:
        nb_params = nb_params*int(dim)
    return nb_params 

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/

I have an even shorter version, one line solution using using numpy:

np.sum([np.prod(v.get_shape().as_list()) for v in tf.trainable_variables()])