Update app.py

app.py

@@ -1,5 +1,9 @@
 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(
@@ -68,4 +72,22 @@ model.add(tf.keras.layers.Dropout(0.5)) #To avoid overfitting
 
 
 #Output Layer
-model.add(tf.keras.layers.Dense(units=36,activation='softmax'))
+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()])
+
+%load_ext tensorboard
+#!rm -rf ./logs/
+log_dir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
+tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
+
+#Entrenar el modelo desde la ultima epoca almacenada usando el parametro initial_epoch
+history = model.fit(x=training_set,validation_data=validation_set, epochs=epochs, initial_epoch=max_num, callbacks=[tensorboard_callback, checkpoint_callback])
+
+#Precisión del conjunto de entrenamiento
+train_loss, train_acc = model.evaluate(training_set)
+print('Training accuracy:', train_acc)
+
+#Precisión del conjunto de validación
+val_loss, val_acc = model.evaluate(validation_set)
+print('Validation accuracy:', val_acc)