model.stop_training = False
model.compile(loss="sparse_categorical_crossentropy",
optimizer=tf.keras.optimizers.Adam(),
metrics=["accuracy"])
earlystop = EarlyStopping(monitor="val_loss", patience=20, verbose=1)
earlystop.set_model(model)
earlystop.on_train_begin()
modelcheckpoint = ModelCheckpoint(filepath="weights/",
monitor="val_loss",
verbose=1,
save_best_only=True)
modelcheckpoint.set_model(model)
modelcheckpoint.on_train_begin()
reduce_lr = ReduceLROnPlateau(monitor="val_loss", patience=10, verbose=1)
reduce_lr.set_model(model)
reduce_lr.on_train_begin()
tensorboard = TensorBoard(log_dir="logs/")
tensorboard.set_model(model)
tensorboard.on_train_begin()
epochs = 3
train_logs_dict = {}
test_logs_dict = {}
for epoch in range(epochs):
training_acc, testing_acc, training_loss, testing_loss = [], [], [], []
print("\nStart of epoch %d" % (epoch + 1, ))
