creacion_modelo.py

import os
from tensorflow.keras.preprocessing import image
import numpy as np
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from tensorflow.keras.optimizers import Adam

dataset_path = 'train'

def load_images(dataset_path):
    images = []
    labels = []
    
    for filename in os.listdir(dataset_path):
        img_path = os.path.join(dataset_path, filename)
        
        if filename.lower().endswith(('jpg', 'jpeg', 'png')):  
            img = image.load_img(img_path, target_size=(150, 150))
            img_array = image.img_to_array(img) / 255.0
            images.append(img_array)
            
            if 'cat' in filename.lower():
                labels.append(0)
            elif 'dog' in filename.lower():
                labels.append(1)
    
    images = np.array(images)
    labels = np.array(labels)
    return images, labels

images, labels = load_images(dataset_path)

X_train, X_val, y_train, y_val = train_test_split(images, labels, test_size=0.2, random_state=42)

print(f"Tamaño del conjunto de entrenamiento: {X_train.shape}")
print(f"Tamaño del conjunto de validación: {X_val.shape}")

model = Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),
    MaxPooling2D(2, 2),

    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D(2, 2),

    Conv2D(128, (3, 3), activation='relu'),
    MaxPooling2D(2, 2),

    Flatten(),
    
    Dense(512, activation='relu'),
    Dropout(0.5),
    Dense(1, activation='sigmoid')
])

model.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])

model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_val, y_val))

model.save('dogs_vs_cats_cnn.h5')