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import tensorflow as tf | |
import matplotlib.pyplot as plt | |
from tensorflow.keras.callbacks import ModelCheckpoint | |
import tensorflow as tf | |
import datetime | |
#Preprocesamiento de imagenes del conjunto de entrenamiento | |
training_set = tf.keras.utils.image_dataset_from_directory( | |
'TrainingFruitDataset/train', | |
labels="inferred", | |
label_mode="categorical", | |
class_names=None, | |
color_mode="rgb", | |
batch_size=32, | |
image_size=(64, 64), | |
shuffle=True, | |
seed=None, | |
validation_split=None, | |
subset=None, | |
interpolation="bilinear", | |
follow_links=False, | |
crop_to_aspect_ratio=False | |
) | |
#Preprocesamiento de imagenes del conjunto de validacion | |
validation_set = tf.keras.utils.image_dataset_from_directory( | |
'TrainingFruitDataset/validation', | |
labels="inferred", | |
label_mode="categorical", | |
class_names=None, | |
color_mode="rgb", | |
batch_size=32, | |
image_size=(64, 64), | |
shuffle=True, | |
seed=None, | |
validation_split=None, | |
subset=None, | |
interpolation="bilinear", | |
follow_links=False, | |
crop_to_aspect_ratio=False | |
) | |
model = tf.keras.models.Sequential() | |
model.add(tf.keras.layers.Conv2D(filters=32,kernel_size=3,padding='same',activation='relu',input_shape=[64,64,3])) | |
model.add(tf.keras.layers.Conv2D(filters=32,kernel_size=3,activation='relu')) | |
model.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2)) | |
model.add(tf.keras.layers.Dropout(0.25)) | |
model.add(tf.keras.layers.Conv2D(filters=64,kernel_size=3,padding='same',activation='relu')) | |
model.add(tf.keras.layers.Conv2D(filters=64,kernel_size=3,activation='relu')) | |
model.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2)) | |
model.add(tf.keras.layers.Dropout(0.25)) | |
model.add(tf.keras.layers.Flatten()) | |
model.add(tf.keras.layers.Dense(units=512,activation='relu')) | |
model.add(tf.keras.layers.Dense(units=256,activation='relu')) | |
model.add(tf.keras.layers.Dropout(0.5)) #To avoid overfitting | |
#Output Layer | |
model.add(tf.keras.layers.Dense(units=36,activation='softmax')) | |
model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy',"mean_absolute_error","Precision","Recall",tf.keras.metrics.AUC()]) | |
#Entrenar el modelo desde la ultima epoca almacenada usando el parametro initial_epoch | |
history = model.fit(x=training_set,validation_data=validation_set, epochs=5, initial_epoch=10) | |
#Precisi贸n del conjunto de entrenamiento | |
train_loss, train_acc = model.evaluate(training_set) | |
print('Training accuracy:', train_acc) | |
#Precisi贸n del conjunto de validaci贸n | |
val_loss, val_acc = model.evaluate(validation_set) | |
print('Validation accuracy:', val_acc) |