AttributeError：在拟合深度学习模型时，“str”对象没有属性“name”

Question

我开始研究神经网络。我正在复习《动手》一书第10章的练习10 使用 Scikit-Learn、Keras 和 TensorFlow 进行机器学习，第三版。我正在尝试运行 github 中显示的答案，但出现错误： istory = model.fit(X_train, y_train, epochs=1, 验证数据=（X_有效，y_有效）， 回调=[expon_lr])

K = keras.backend

class ExponentialLearningRate(keras.callbacks.Callback):
    def __init__(self, factor):
        self.factor = factor
        self.rates = []
        self.losses = []
    def on_batch_end(self, batch, logs):
        self.rates.append(K.get_value(self.model.optimizer.learning_rate))
        self.losses.append(logs["loss"])
        K.set_value(self.model.optimizer.learning_rate, self.model.optimizer.learning_rate * self.factor)

keras.backend.clear_session()
np.random.seed(42)
tf.random.set_seed(42)

model = keras.models.Sequential([
    keras.layers.Flatten(input_shape=[28, 28]),  # Capa de entrada
    keras.layers.Dense(300, activation="relu"),  # Primera capa oculta
    keras.layers.Dense(200, activation="relu"),  # Segunda capa oculta
    keras.layers.Dense(10, activation="softmax") # Capa de salida
])

model.compile(loss="sparse_categorical_crossentropy",
              optimizer=keras.optimizers.SGD(learning_rate=1e-3),
              metrics=["accuracy"])

expon_lr = ExponentialLearningRate(factor=1.005)

history = model.fit(X_train, y_train, epochs=1,
                    validation_data=(X_valid, y_valid),
                    callbacks=[expon_lr])

plt.plot(expon_lr.rates, expon_lr.losses)
plt.gca().set_xscale('log')
plt.hlines(min(expon_lr.losses), min(expon_lr.rates), max(expon_lr.rates))
plt.axis([min(expon_lr.rates), max(expon_lr.rates), 0, expon_lr.losses[0]])
plt.grid()
plt.xlabel("Learning rate")
plt.ylabel("Loss")

错误：

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-14-a0cc0ada5ef0> in <cell line: 1>()
----> 1 history = model.fit(X_train, y_train, epochs=1,
      2                     validation_data=(X_valid, y_valid),
      3                     callbacks=[expon_lr])

1 frames
<ipython-input-6-f87513637cd3> in on_batch_end(self, batch, logs)
      9         self.rates.append(K.get_value(self.model.optimizer.learning_rate))
     10         self.losses.append(logs["loss"])
---> 11         K.set_value(self.model.optimizer.learning_rate, self.model.optimizer.learning_rate * self.factor)

AttributeError: 'str' object has no attribute 'name'

我希望它能像我从 github 上复制并粘贴代码一样工作。 ¿也许是因为tensorflow版本的原因？

Answer 1

聊天gpt解决了它：

import tensorflow as tf
from tensorflow import keras

# Callback personalizado
class ExponentialLearningRate(keras.callbacks.Callback):
    def __init__(self, factor):
        super().__init__()
        self.factor = factor
        self.rates = []
        self.losses = []

    def on_batch_end(self, batch, logs=None):
        logs = logs or {}
        self.losses.append(logs.get("loss", None))

        # Obtener el valor de learning_rate y asegurarnos de que sea un número
        lr = self.model.optimizer.learning_rate
        if isinstance(lr, tf.Variable):
            lr = lr.numpy()  # Convertir a valor numérico si es un tf.Variable

        # Registrar la tasa de aprendizaje
        self.rates.append(lr)

        # Actualizar el learning rate
        if isinstance(lr, (float, int)):
            new_lr = lr * self.factor
            self.model.optimizer.learning_rate.assign(new_lr)  # Modificar learning_rate

# Modelo simple
model = keras.models.Sequential([
    keras.layers.Flatten(input_shape=[28, 28]),
    keras.layers.Dense(300, activation="relu"),
    keras.layers.Dense(100, activation="relu"),
    keras.layers.Dense(10, activation="softmax")
])

# Compilar modelo
model.compile(
    loss="sparse_categorical_crossentropy",
    optimizer=keras.optimizers.SGD(learning_rate=1e-3),
    metrics=["accuracy"]
)

# Crear el callback
expon_lr = ExponentialLearningRate(factor=1.005)

# Ajustar modelo
history = model.fit(
    X_train, y_train,
    epochs=1,
    validation_data=(X_valid, y_valid),
    callbacks=[expon_lr]
)