# crear conjunto de datos

!unzip /content/Ventiladores/ventiladores.zip > /content/dataset

replace facebook_sharing_5.png.txt? [y]es, [n]o, [A]ll, [N]one, [r]ename: y

! sudo mv *.png *.jpg *.jpeg /content/Ventiladores

! mv /content/Ventiladores & /content/Computadoras > /content/dataset

! sudo mv babuino/ dataset/babuino

! sudo mv jirafa/ dataset/jirafa

mv: cannot stat 'jirafa/': No such file or directory

from tensorflow.keras.preprocessing.image import ImageDataGenerator

import numpy as np

# crear dataset generador

datagen= ImageDataGenerator(
    # normalizacion de las imagenes
    rescale=1. / 255,
    #rotacion
    rotation_range = 10,
    # giro de la imagen
    width_shift_range=0.15,
    # movimiento hacia arriba
    height_shift_range = 0.1,
    # deformacion de las imagenes
    shear_range = 5,
    # acercamiento
    zoom_range = [0.7 , 1.3],
    # validacion -> porcentaje de las imagenes a ser utilziadas en las pruebas
    validation_split= 0.2,
)

# datos entrenamiento y datos de prueba

data_gen_entrenamiento = datagen.flow_from_directory("/content/dataset",
                                                    target_size=(224,224),
                                                    batch_size=32 , shuffle=True,
                                                     subset="training"
                                                     )

Found 101 images belonging to 2 classes.

data_gen_pruebas = datagen.flow_from_directory("/content/dataset",
                                                    target_size=(224,224),
                                                    batch_size=32 , shuffle=True,
                                                     subset="validation"
                                                     )

Found 24 images belonging to 2 classes.

import matplotlib.pyplot as plt

for imagenes , etiqueta in data_gen_entrenamiento:
  for i in range(10):
    plt.subplot(2,5,i+1)
    plt.imshow(imagenes[1])
  break
plt.show()

/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images
  warnings.warn(

import tensorflow as tf
import tensorflow_hub as hub

url = "https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4"
mobilenetv2 = hub.KerasLayer(url , input_shape=(224,224,3))

# importante -> congelar las capas
# evitar el sobre entrenamiento del modelo descargado de google
mobilenetv2.trainable = False

# creacion del modelo en base al de google
modelo = tf.keras.Sequential([
    mobilenetv2,
    tf.keras.layers.Dense(units=100),
    tf.keras.layers.Dense(units=100),
    tf.keras.layers.Dense(2 , activation="softmax")

])

modelo.compile(
    optimizer="adam",
    loss="categorical_crossentropy",
    metrics=["accuracy"],
)

EPOCAS = 20
entrenamiento = modelo.fit(
    data_gen_entrenamiento,
    epochs=EPOCAS,
    batch_size = 32,
    validation_data = data_gen_pruebas
)

Epoch 1/20

/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images
  warnings.warn(

4/4 [==============================] - ETA: 0s - loss: 1.4060 - accuracy: 0.6337

/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images
  warnings.warn(


4/4 [==============================] - 7s 682ms/step - loss: 1.4060 - accuracy: 0.6337 - val_loss: 1.1179 - val_accuracy: 0.5000
Epoch 2/20
1/4 [======>.......................] - ETA: 1s - loss: 1.0500 - accuracy: 0.5625

/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images
  warnings.warn(

4/4 [==============================] - 2s 595ms/step - loss: 1.0861 - accuracy: 0.5842 - val_loss: 0.9222 - val_accuracy: 0.5833
Epoch 3/20

/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images
  warnings.warn(

2/4 [==============>...............] - ETA: 0s - loss: 0.3390 - accuracy: 0.8919

/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images
  warnings.warn(

from PIL import Image
import cv2


def categorizar(ruta):
  img = Image.open(ruta)
  img = img.convert("RGB")
  img = np.array(img).astype(float)/255
  img = cv2.resize(img , (224,224))
  prediccion = modelo.predict(img.reshape(-1, 224,224,3))
  return np.argmax(prediccion[0] , axis=-1)

ruta = input("indique donde se encuentra el archivo de imagen : \n" )
prediccion = categorizar(ruta)
if prediccion == 1:
  print("La imagen analizada corresponde a una Jirafa")
elif prediccion == 0:
  print("La imagen analizada corresponde a un babuino")
else:
  print("el archivo no se encontró")

indique donde se encuentra el archivo de imagen : 
ventilador.jpg
1/1 [==============================] - 0s 454ms/step