我正在為分類任務訓練影像資料集上的張量流模型，我們通常為model.fit方法提供訓練集和驗證集，我們可以稍后輸出訓練和驗證的模型收斂圖。我想對測驗集做同樣的事情，換句話說，我想在每個 epoch 之后獲得我的模型在測驗集上的準確性和損失（不是驗證集 - 我不能用測驗替換驗證集設定，因為我需要它們的圖表）。
我設法通過在每個時期之后使用一些回呼保存我的模型的檢查點來做到這一點，然后將每個檢查點加載到我的模型并計算準確性和損失，但我想知道是否存在一些更簡單的方法來做到這一點，也許有一些其他回呼或一些解決model.fit方法。
uj5u.com熱心網友回復：
您可以使用自定義Callback并傳遞您的測驗資料并做任何您喜歡的事情：
import tensorflow as tf
import pathlib
import numpy as np
dataset_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)
batch_size = 5
train_ds = tf.keras.utils.image_dataset_from_directory(
data_dir,
seed=123,
image_size=(64, 64),
batch_size=batch_size)
test_ds = train_ds.take(30)
model = tf.keras.Sequential([
tf.keras.layers.Rescaling(1./255, input_shape=(64, 64, 3)),
tf.keras.layers.Conv2D(16, 3, padding='same', activation='relu'),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Conv2D(32, 3, padding='same', activation='relu'),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Conv2D(64, 3, padding='same', activation='relu'),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(5)
])
class TestCallback(tf.keras.callbacks.Callback):
def __init__(self, test_dataset):
super().__init__()
self.test_dataset = test_dataset
self.test_acc_metric = tf.keras.metrics.SparseCategoricalAccuracy()
self.loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
def on_epoch_end(self, epoch, logs=None):
losses = []
for x_batch_test, y_batch_test in self.test_dataset:
test_logits = self.model(x_batch_test, training=False)
losses.append(self.loss_fn(y_batch_test, test_logits))
self.test_acc_metric.update_state(y_batch_test, test_logits)
test_acc = self.test_acc_metric.result()
self.test_acc_metric.reset_states()
logs['test_loss'] = tf.reduce_mean(tf.stack(losses)) # not sure if the reduction is correct
logs['test_sparse_categorical_accuracy'] = test_acc
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer='adam',
loss=loss_fn,
metrics=tf.keras.metrics.SparseCategoricalAccuracy())
epochs = 5
history = model.fit(train_ds, epochs=epochs, callbacks= [TestCallback(test_ds)])
Found 3670 files belonging to 5 classes.
Epoch 1/5
734/734 [==============================] - 14s 17ms/step - loss: 1.2709 - sparse_categorical_accuracy: 0.4591 - test_loss: 1.0020 - test_sparse_categorical_accuracy: 0.5533
Epoch 2/5
734/734 [==============================] - 13s 18ms/step - loss: 0.9574 - sparse_categorical_accuracy: 0.6275 - test_loss: 0.8348 - test_sparse_categorical_accuracy: 0.6467
Epoch 3/5
734/734 [==============================] - 9s 12ms/step - loss: 0.8136 - sparse_categorical_accuracy: 0.6733 - test_loss: 0.8379 - test_sparse_categorical_accuracy: 0.6467
Epoch 4/5
734/734 [==============================] - 8s 11ms/step - loss: 0.6970 - sparse_categorical_accuracy: 0.7357 - test_loss: 0.5713 - test_sparse_categorical_accuracy: 0.7533
Epoch 5/5
734/734 [==============================] - 8s 11ms/step - loss: 0.5793 - sparse_categorical_accuracy: 0.7834 - test_loss: 0.5656 - test_sparse_categorical_accuracy: 0.7733
您也可以只model.evaluate在回呼中使用。另見這篇文章。
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