filepath = "model_checkpoint.h5"
时间: 2023-07-06 19:26:22 浏览: 47
这段代码是定义了一个字符串类型的变量`filepath`,用于存储模型的权重参数,即训练好的模型在每次epoch训练完成之后,将模型参数保存到这个文件中,以便之后可以方便地加载模型并继续训练或进行预测。
在Keras中,可以使用`ModelCheckpoint`回调函数来实现模型参数的自动保存。下面是一个示例代码:
```python
from keras.callbacks import ModelCheckpoint
checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
model.fit(x_train, y_train, epochs=10, validation_data=(x_val, y_val), callbacks=[checkpoint])
```
在上述代码中,`ModelCheckpoint`回调函数会在每次epoch训练完成之后,根据指定的监测指标`val_loss`(即验证集上的损失函数值)来判断当前模型是否是最优模型,并将其保存到`filepath`文件中。其中,`verbose=1`参数表示在保存模型时打印一条消息,`save_best_only=True`参数表示仅保存最优模型,`mode='min'`参数表示监测指标的最小化。
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`。
filepath = "model.h5" checkpoint = ModelCheckpoint(filepath, monitor='val_top_3_accuracy', verbose=1, save_best_only=True, mode='max') reduce_lr = ReduceLROnPlateau(monitor='val_top_3_accuracy', factor=0.5, patience=2, verbose=1, mode='max', min_lr=0.00001) callbacks_list = [checkpoint, reduce_lr] #datagen.flow(x_train,y_train, batch_size=batch_size) history = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size, target_size=(100, 125)), class_weight=class_weights, validation_data=(x_validate,y_validate),steps_per_epoch=x_train.shape[0] // batch_size, epochs=10, verbose=1, callbacks=callbacks_list)这一段代码报错TypeError: flow() got an unexpected keyword argument 'target_size'
很抱歉,我之前的回答并不正确,`target_size`参数适用于`ImageDataGenerator`,而不是`DataGenerator`。对于`DataGenerator`,你可以在实例化时指定输入数据的形状,例如:
```
train_generator = DataGenerator(x_train, y_train, batch_size=batch_size, input_shape=(100, 125, 1))
val_generator = DataGenerator(x_validate, y_validate, batch_size=batch_size, input_shape=(100, 125, 1))
```
这里我假设输入数据的形状是`(100, 125, 1)`,并使用`input_shape`参数在实例化`DataGenerator`时指定。请注意,这里的`batch_size`应该与你在`fit_generator()`中使用的`batch_size`一致。
然后,将`fit_generator()`中的生成器修改为`train_generator`和`val_generator`:
```
history = model.fit_generator(train_generator,
class_weight=class_weights,
validation_data=val_generator,
steps_per_epoch=x_train.shape[0] // batch_size,
epochs=10,
verbose=1,
callbacks=callbacks_list)
```