解释一下这个代码num_epochs = 500 batch_size = 2048 num_samples = x_train_tensor.size(0) num_batches = num_samples // batch_size for epoch in range(num_epochs): for i in range(num_batches): start_idx = i * batch_size end_idx = (i + 1) * batch_size inputs = x_train_tensor[start_idx:end_idx] labels = y_train_tensor[start_idx:end_idx] optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs.squeeze(), labels) loss.backward() optimizer.step()
时间: 2024-04-09 12:30:54 浏览: 23
这段代码是一个训练模型的循环。它使用了一个外部的训练循环来迭代指定次数的epoch(训练轮数)。
首先,代码定义了一些训练相关的参数,包括num_epochs(训练轮数)、batch_size(批处理大小)、num_samples(训练样本数量)和num_batches(每个epoch中的批次数量)。
接下来,通过两个嵌套的循环进行训练。外层循环迭代num_epochs次,内层循环迭代num_batches次。每次内层循环都会处理一个batch_size大小的训练样本。
在内层循环中,根据当前的i来确定当前批次的起始索引start_idx和结束索引end_idx。然后,从训练数据中提取出对应的输入inputs和标签labels。
接下来,通过调用optimizer.zero_grad()来清除之前计算的梯度信息。然后,将inputs传递给模型model进行前向传播,得到输出outputs。
计算模型输出与标签之间的损失loss,使用指定的损失函数criterion,并通过调用loss.backward()来计算梯度。最后,调用optimizer.step()来更新模型的参数,以最小化损失。
通过这个循环,模型将在给定的训练数据上进行多次迭代的优化,以逐渐提高模型的性能。
相关问题
# 最佳权重保存路径 BEST_MODEL_PATH = './best_model.h5'
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`。
tokenizer = Tokenizer(num_words=max_words) tokenizer.fit_on_texts(data['text']) sequences = tokenizer.texts_to_sequences(data['text']) word_index = tokenizer.word_index print('Found %s unique tokens.' % len(word_index)) data = pad_sequences(sequences,maxlen=maxlen) labels = np.array(data[:,:1]) print('Shape of data tensor:',data.shape) print('Shape of label tensor',labels.shape) indices = np.arange(data.shape[0]) np.random.shuffle(indices) data = data[indices] labels = labels[indices] x_train = data[:traing_samples] y_train = data[:traing_samples] x_val = data[traing_samples:traing_samples+validation_samples] y_val = data[traing_samples:traing_samples+validation_samples] model = Sequential() model.add(Embedding(max_words,100,input_length=maxlen)) model.add(Flatten()) model.add(Dense(32,activation='relu')) model.add(Dense(10000,activation='sigmoid')) model.summary() model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc']) history = model.fit(x_train,y_train, epochs=1, batch_size=128, validation_data=[x_val,y_val]) import matplotlib.pyplot as plt acc = history.history['acc'] val_acc = history.history['val_acc'] loss = history.history['loss'] val_loss = history.history['val_loss'] epoachs = range(1,len(acc) + 1) plt.plot(epoachs,acc,'bo',label='Training acc') plt.plot(epoachs,val_acc,'b',label = 'Validation acc') plt.title('Training and validation accuracy') plt.legend() plt.figure() plt.plot(epoachs,loss,'bo',label='Training loss') plt.plot(epoachs,val_loss,'b',label = 'Validation loss') plt.title('Training and validation loss') plt.legend() plt.show() max_len = 10000 x_train = keras.preprocessing.sequence.pad_sequences(x_train, maxlen=max_len) x_test = data[10000:,0:] x_test = keras.preprocessing.sequence.pad_sequences(x_test, maxlen=max_len) # 将标签转换为独热编码 y_train = np.eye(2)[y_train] y_test = data[10000:,:1] y_test = np.eye(2)[y_test]
这段代码中存在几个问题:
1. `labels` 的赋值有误。应该是 `labels = np.array(data[:, 1:])`,因为标签是从第二列开始的。
2. `validation_data` 的格式有误。应该是 `validation_data=(x_val, y_val)`,而不是 `validation_data=[x_val, y_val]`。
3. 在使用 `Sequential` 模型时,需要指定输入的维度。这里应该是 `model.add(Dense(10000, activation='sigmoid', input_dim=maxlen))`,而不是 `model.add(Dense(10000, activation='sigmoid'))`。
4. 绘制图表时,应该使用 `val_acc` 和 `val_loss`,而不是 `test_acc` 和 `test_loss`。
5. 在将标签转换为独热编码时,应该使用 `y_train[:, 0]` 和 `y_test[:, 0]`,而不是 `y_train` 和 `y_test`。
以下是修改后的代码:
```python
tokenizer = Tokenizer(num_words=max_words)
tokenizer.fit_on_texts(data['text'])
sequences = tokenizer.texts_to_sequences(data['text'])
word_index = tokenizer.word_index
print('Found %s unique tokens.' % len(word_index))
data = pad_sequences(sequences, maxlen=maxlen)
labels = np.array(data[:, 1:])
print('Shape of data tensor:', data.shape)
print('Shape of label tensor', labels.shape)
indices = np.arange(data.shape[0])
np.random.shuffle(indices)
data = data[indices]
labels = labels[indices]
x_train = data[:training_samples]
y_train = labels[:training_samples]
x_val = data[training_samples:training_samples+validation_samples]
y_val = labels[training_samples:training_samples+validation_samples]
model = Sequential()
model.add(Embedding(max_words, 100, input_length=maxlen))
model.add(Flatten())
model.add(Dense(32, activation='relu'))
model.add(Dense(10000, activation='sigmoid', input_dim=maxlen))
model.summary()
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['acc'])
history = model.fit(x_train, y_train,
epochs=1,
batch_size=128,
validation_data=(x_val, y_val))
import matplotlib.pyplot as plt
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(acc) + 1)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
max_len = 10000
x_train = keras.preprocessing.sequence.pad_sequences(x_train, maxlen=max_len)
x_test = data[10000:, 0:]
x_test = keras.preprocessing.sequence.pad_sequences(x_test, maxlen=max_len)
# 将标签转换为独热编码
y_train = np.eye(2)[y_train[:, 0]]
y_test = data[10000:, 1:]
y_test = np.eye(2)[y_test[:, 0]]
```