首页AttributeError: 'Adam' object has no attribute 'zero_grad'

AttributeError: 'Adam' object has no attribute 'zero_grad'

时间: 2023-09-07 12:12:29 浏览: 306

对不起，我犯了一个错误。在Keras中，优化器没有 `zero_grad` 方法。相反，您可以使用 `optimizer.apply_gradients([])` 来清除梯度。以下是修改后的代码： ```python import tensorflow as tf from tensorflow import keras # 创建生成器模型 generator = keras.Sequential([...]) # 填入生成器的层 # 创建辨别器模型 discriminator = keras.Sequential([...]) # 填入辨别器的层 # 定义损失函数 criterion = keras.losses.BinaryCrossentropy(from_logits=True) # 定义优化器 optimizer_d = keras.optimizers.Adam(learning_rate=0.0001) optimizer_g = keras.optimizers.Adam(learning_rate=0.0001) for i in range(MAX_EPOCH): # 为真实数据加上噪声 real_data = np.vstack([np.sin(POINT) + np.random.normal(0, 0.01, SAMPLE_NUM) for _ in range(BATCH_SIZE)]) real_data = tf.convert_to_tensor(real_data, dtype=tf.float32) real_data = tf.Variable(real_data).to(device) # 用随机噪声作为生成器的输入 g_noises = np.random.randn(BATCH_SIZE, N_GNET) g_noises = tf.convert_to_tensor(g_noises, dtype=tf.float32) g_noises = tf.Variable(g_noises).to(device) with tf.GradientTape() as tape_d: d_real = discriminator(real_data) loss_d_real = criterion(d_real, true_lable) grads_d_real = tape_d.gradient(loss_d_real, discriminator.trainable_weights) optimizer_d.apply_gradients(zip(grads_d_real, discriminator.trainable_weights)) with tf.GradientTape() as tape_d: fake_data = generator(g_noises) d_fake = discriminator(fake_data) loss_d_fake = criterion(d_fake, fake_lable) grads_d_fake = tape_d.gradient(loss_d_fake, discriminator.trainable_weights) optimizer_d.apply_gradients(zip(grads_d_fake, discriminator.trainable_weights)) ```

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