CODEWITHSUNDEEP
python-3.xkerasneural-networkdeep-learning
SteveS
SteveS

Reputation: 4040

How to calculate number of parameters in Keras models?

Here is my model:

from keras.layers import Input, Embedding, Flatten
from keras.models import Model


n_teams = 10888

team_lookup = Embedding(input_dim=n_teams,
                        output_dim=1,
                        input_length=1,
                        name='Team-Strength')

teamid_in = Input(shape=(1,))

strength_lookup = team_lookup(teamid_in)

strength_lookup_flat = Flatten()(strength_lookup)

team_strength_model = Model(teamid_in, strength_lookup_flat, name='Team-Strength-Model')

team_in_1 = Input(shape=(1,), name='Team-1-In')

team_in_2 = Input(shape=(1,), name='Team-2-In')

home_in = Input(shape=(1,), name='Home-In')

team_1_strength = team_strength_model(team_in_1)

team_2_strength = team_strength_model(team_in_2)

out = Concatenate()([team_1_strength, team_2_strength, home_in])

out = Dense(1)(out)

When I am fitting the model with 10888 inputs and running summary I am getting total of 10892 parameters, please explain me:

1) Where do the 4 come from? and

2) If each of my outputs is 10888 why it counts only once?

Here is the summary of the model:

__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
Team-1-In (InputLayer)          (None, 1)            0                                            
__________________________________________________________________________________________________
Team-2-In (InputLayer)          (None, 1)            0                                            
__________________________________________________________________________________________________
Team-Strength (Model)           (None, 1)            10888       Team-1-In[0][0]                  
                                                                 Team-2-In[0][0]                  
__________________________________________________________________________________________________
Home-In (InputLayer)            (None, 1)            0                                            
__________________________________________________________________________________________________
concatenate_1 (Concatenate)     (None, 3)            0           Team-Strength[1][0]              
                                                                 Team-Strength[2][0]              
                                                                 Home-In[0][0]                    
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 1)            4           concatenate_1[0][0]              
==================================================================================================
Total params: 10,892
Trainable params: 10,892
Non-trainable params: 0
__________________________________________________________________________________________________

Upvotes: 1

Views: 1233

Answers (1)

Lukasz Tracewski
Lukasz Tracewski

Reputation: 11367

To answer your questions:

  1. 4 stems from the output_size * (input_size + 1) = number_parameters. From concatenate_1[0][0] you have 3 connections and 1 bias, hence 4.

  2. 10880 is the size of your embedding layer to which Team-1 and Team-2 are connected. It's the total "vocabulary" that is going to be used and has nothing to do with the output (which is the second parameter to the Embedding).

I hope it makes sense.

Upvotes: 1

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