The path "/var/www/html/generated/code/Magento/Backend/Model/UrlFactory.php.56" cannot be renamed

Error:

The path "/var/www/html/generated/code/Magento/Backend/Model/UrlFactory.php.56" cannot be renamed into "/var/www/html/generated/code/Magento/Backend/Model/UrlFactory.php" Warn
  ing!rename(/var/www/html/generated/code/Magento/Backend/Model/UrlFactory.php.56,/var/www/html/generated/code/Magento/Backend/Model/UrlFactory.php): No such file or directory
  Class Magento\Backend\Model\UrlFactory generation error: The requested class did not generate properly, because the 'generated' directory permission is read-only. If --- after
   running the 'bin/magento setup:di:compile' CLI command when the 'generated' directory permission is set to write --- the requested class did not generate properly, then you m
  ust add the generated class object to the signature of the related construct method, only.

Solution:

Step1: Remove all files under  path "\magento\generated\code".

Step2: Execute the following CLI

php bin/magento setup:upgrade
php bin/magento setup:di:compile
php bin/magento setup:static-content:deploy -f

 

 

好的,你可以将最佳权重保存路径添加到代码中,如下所示: ```python import numpy as np import tensorflow as tf import os # 加载数据集 with open('poems.txt', 'r', encoding='utf-8') as f: data = f.read() # 构建词典 vocab = sorted(set(data)) char2idx = {char: idx for idx, char in enumerate(vocab)} idx2char = np.array(vocab) # 将文本数据转换为数字 text_as_int = np.array([char2idx[c] for c in data]) # 定义训练数据和标签 seq_length = 100 examples_per_epoch = len(data) // (seq_length + 1) char_dataset = tf.data.Dataset.from_tensor_slices(text_as_int) sequences = char_dataset.batch(seq_length + 1, drop_remainder=True) def split_input_target(chunk): input_text = chunk[:-1] target_text = chunk[1:] return input_text, target_text dataset = sequences.map(split_input_target) BATCH_SIZE = 128 BUFFER_SIZE = 10000 dataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE, drop_remainder=True) # 构建模型 vocab_size = len(vocab) embedding_dim = 256 rnn_units = 1024 def build_model(vocab_size, embedding_dim, rnn_units, batch_size): model = tf.keras.Sequential([ tf.keras.layers.Embedding(vocab_size, embedding_dim, batch_input_shape=[batch_size, None]), tf.keras.layers.GRU(rnn_units, return_sequences=True, stateful=True, recurrent_initializer='glorot_uniform'), tf.keras.layers.Dense(vocab_size) ]) return model model = build_model( vocab_size=len(vocab), embedding_dim=embedding_dim, rnn_units=rnn_units, batch_size=BATCH_SIZE) # 定义损失函数 def loss(labels, logits): return tf.keras.losses.sparse_categorical_crossentropy(labels, logits, from_logits=True) # 编译模型 model.compile(optimizer='adam', loss=loss) # 定义检查点 checkpoint_dir = './training_checkpoints' checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt_{epoch}") checkpoint_callback=tf.keras.callbacks.ModelCheckpoint( filepath=checkpoint_prefix, save_weights_only=True) # 定义最佳权重检查点 BEST_MODEL_PATH = './best_model.h5' best_checkpoint = tf.keras.callbacks.ModelCheckpoint(BEST_MODEL_PATH, monitor='val_loss', save_best_only=True, mode='min', save_weights_only=True) # 训练模型 EPOCHS = 50 history = model.fit(dataset, epochs=EPOCHS, callbacks=[checkpoint_callback, best_checkpoint]) # 生成诗歌 def generate_text(model, start_string): num_generate = 100 input_eval = [char2idx[s] for s in start_string] input_eval = tf.expand_dims(input_eval, 0) text_generated = [] temperature = 1.0 model.reset_states() for i in range(num_generate): predictions = model(input_eval) predictions = tf.squeeze(predictions, 0) predictions = predictions / temperature predicted_id = tf.random.categorical(predictions, num_samples=1)[-1,0].numpy() input_eval = tf.expand_dims([predicted_id], 0) text_generated.append(idx2char[predicted_id]) return (start_string + ''.join(text_generated)) # 加载检查点 model = build_model(vocab_size, embedding_dim, rnn_units, batch_size=1) model.load_weights(BEST_MODEL_PATH) model.build(tf.TensorShape([1, None])) # 生成一首诗 print(generate_text(model, start_string=u"山中")) ``` 现在,模型将保存最佳的权重到文件 `best_model.h5`。
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