Merge pull request #287 from ahmedfgad/master

Keep up to date

examples/KerasGA/image_classification_CNN.py

@@ -2,6 +2,8 @@
 import pygad.kerasga
 import numpy
 import pygad
+import gc
+
 
 def fitness_func(ga_instanse, solution, sol_idx):
     global data_inputs, data_outputs, keras_ga, model
@@ -11,27 +13,33 @@ def fitness_func(ga_instanse, solution, sol_idx):
                                         data=data_inputs)
 
     cce = tensorflow.keras.losses.CategoricalCrossentropy()
-    solution_fitness = 1.0 / (cce(data_outputs, predictions).numpy() + 0.00000001)
+    solution_fitness = 1.0 / \
+        (cce(data_outputs, predictions).numpy() + 0.00000001)
 
     return solution_fitness
 
+
 def on_generation(ga_instance):
     print(f"Generation = {ga_instance.generations_completed}")
     print(f"Fitness    = {ga_instance.best_solution()[1]}")
+    gc.collect()  # can useful for not exploding the memory usage on notebooks (ipynb) freeing memory
+
 
 # Build the keras model using the functional API.
 input_layer = tensorflow.keras.layers.Input(shape=(100, 100, 3))
 conv_layer1 = tensorflow.keras.layers.Conv2D(filters=5,
                                              kernel_size=7,
                                              activation="relu")(input_layer)
-max_pool1 = tensorflow.keras.layers.MaxPooling2D(pool_size=(5,5),
+max_pool1 = tensorflow.keras.layers.MaxPooling2D(pool_size=(5, 5),
                                                  strides=5)(conv_layer1)
 conv_layer2 = tensorflow.keras.layers.Conv2D(filters=3,
                                              kernel_size=3,
                                              activation="relu")(max_pool1)
-flatten_layer  = tensorflow.keras.layers.Flatten()(conv_layer2)
-dense_layer = tensorflow.keras.layers.Dense(15, activation="relu")(flatten_layer)
-output_layer = tensorflow.keras.layers.Dense(4, activation="softmax")(dense_layer)
+flatten_layer = tensorflow.keras.layers.Flatten()(conv_layer2)
+dense_layer = tensorflow.keras.layers.Dense(
+    15, activation="relu")(flatten_layer)
+output_layer = tensorflow.keras.layers.Dense(
+    4, activation="softmax")(dense_layer)
 
 model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer)
 
@@ -47,13 +55,15 @@ def on_generation(ga_instance):
 data_outputs = tensorflow.keras.utils.to_categorical(data_outputs)
 
 # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class
-num_generations = 200 # Number of generations.
-num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool.
-initial_population = keras_ga.population_weights # Initial population of network weights.
+num_generations = 200  # Number of generations.
+# Number of solutions to be selected as parents in the mating pool.
+num_parents_mating = 5
+# Initial population of network weights.
+initial_population = keras_ga.population_weights
 
 # Create an instance of the pygad.GA class
-ga_instance = pygad.GA(num_generations=num_generations, 
-                       num_parents_mating=num_parents_mating, 
+ga_instance = pygad.GA(num_generations=num_generations,
+                       num_parents_mating=num_parents_mating,
                        initial_population=initial_population,
                        fitness_func=fitness_func,
                        on_generation=on_generation)
@@ -62,7 +72,8 @@ def on_generation(ga_instance):
 ga_instance.run()
 
 # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations.
-ga_instance.plot_fitness(title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4)
+ga_instance.plot_fitness(
+    title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4)
 
 # Returning the details of the best solution.
 solution, solution_fitness, solution_idx = ga_instance.best_solution()

examples/example_custom_operators.py

@@ -41,7 +41,7 @@ def crossover_func(parents, offspring_size, ga_instance):
         parent1 = parents[idx % parents.shape[0], :].copy()
         parent2 = parents[(idx + 1) % parents.shape[0], :].copy()
 
-        random_split_point = numpy.random.choice(range(offspring_size[0]))
+        random_split_point = numpy.random.choice(range(offspring_size[1]))
 
         parent1[random_split_point:] = parent2[random_split_point:]