Incompatible shape sizes using PyGAD

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I'm trying to follow the tutorial given here.

This tutorial trains a Keras model using a genetic algorithm, with the PyGAD package. I'm interested in the binary classification case. My input matrix is of dimension 10000x20. Hence, I've created the following model using Keras:

input_layer = tensorflow.keras.layers.Input(20)
dense_layer1 = tensorflow.keras.layers.Dense(500, activation="relu")(input_layer)
dense_layer2 = tensorflow.keras.layers.Dense(500, activation="relu")(dense_layer1)
output_layer = tensorflow.keras.layers.Dense(1, activation="softmax")(dense_layer2)

model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer)

keras_ga = pygad.kerasga.KerasGA(model=model,
                                 num_solutions=10)

However, when I go to run the algorithm, using ga_instance.run(), I get the error:

ValueError: Shapes (10000,) and (10000, 1) are incompatible

I can't figure out why I'm getting this error? I want my Keras model to have 2 hidden layers, each with 500 hidden nodes and 1 output node.

keras tensorflow deep-learning python
1 Answers

I think the problem is related to how each output is represented in the array. if you have a single output for 10000 instances, then this is an example of preparing the data that works with PyGAD. Its shape is (1000, 1).

numpy.random.uniform(0, 1, (1000, 1))

Here is a code that works but for a simple network architecture because, based on the fitness function you used, the fitness sometimes is NaN.

As I do not have the same data you used, I generated the input/output data randomly.

import tensorflow.keras
import pygad.kerasga
import numpy
import pygad

def fitness_func(solution, sol_idx):
    global data_inputs, data_outputs, keras_ga, model

    model_weights_matrix = pygad.kerasga.model_weights_as_matrix(model=model,
                                                                 weights_vector=solution)

    model.set_weights(weights=model_weights_matrix)

    predictions = model.predict(data_inputs)

    cce = tensorflow.keras.losses.CategoricalCrossentropy()
    solution_fitness = 1.0 / (cce(data_outputs, predictions).numpy() + 0.00000001)
    # print("solution_fitness", cce(data_outputs, predictions).numpy(), solution_fitness)

    return solution_fitness

def callback_generation(ga_instance):
    print("Generation = {generation}".format(generation=ga_instance.generations_completed))
    print("Fitness    = {fitness}".format(fitness=ga_instance.best_solution(ga_instance.last_generation_fitness)[1]))

data_inputs = numpy.random.uniform(0, 1, (1000, 20))
data_outputs = numpy.random.uniform(0, 1, (1000, 1))

# create model
from tensorflow.keras.layers import Dense, Dropout
l1_rate=1e-6
l2_rate = 1e-6
input_layer  = tensorflow.keras.layers.InputLayer(20)
dense_layer1 = tensorflow.keras.layers.Dense(10, activation="relu",kernel_regularizer=tensorflow.keras.regularizers.l1_l2(l1=l1_rate, l2=l2_rate))
output_layer = tensorflow.keras.layers.Dense(1, activation="sigmoid")

model = tensorflow.keras.Sequential()
model.add(input_layer)
model.add(dense_layer1)
model.add(Dropout(0.2))  
model.add(output_layer)

keras_ga = pygad.kerasga.KerasGA(model=model,
                                 num_solutions=10)

# Run pygad

num_generations = 30
num_parents_mating = 5
initial_population = keras_ga.population_weights

ga_instance = pygad.GA(num_generations=num_generations,
                       num_parents_mating=num_parents_mating,
                       initial_population=initial_population,
                       fitness_func=fitness_func,
                       on_generation=callback_generation)

ga_instance.run()

Thanks for using PyGAD!

user_54265390
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