checkpoint_save_path = "./cwru_checkpoint/cwru_cnn.ckpt" if os.path.exists(checkpoint_save_path + '.index'): print('-------------load the model-----------------') model.load_weights(checkpoint_save_path) cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path, save_weights_only=True, save_best_only=True) history = model.fit(x=x_train, y=y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_valid, y_valid), shuffle=True, callbacks=[cp_callback]) model.summary() 这段代码是做什么
时间: 2023-12-11 07:04:12 浏览: 43
这段代码是用来训练一个卷积神经网络模型,并且在训练过程中保存最好的模型权重到指定路径。具体来说:
- 第一行定义了模型权重的保存路径。
- 接下来的几行代码判断是否存在已经训练好的模型权重,如果存在则加载模型权重。
- `tf.keras.callbacks.ModelCheckpoint`是一个回调函数,它会在每个epoch结束时保存模型的权重。这里设定了`save_weights_only=True`,只保存权重而不保存模型结构;`save_best_only=True`表示只保存最好的模型,即验证集上准确率最高的模型。
- `model.fit`函数用于训练模型。其中`x_train`和`y_train`是训练数据的输入和输出;`batch_size`是每次训练时使用的样本数;`epochs`表示训练的轮数;`validation_data`是用于验证模型性能的数据集;`shuffle=True`表示每个epoch前随机打乱训练数据集。
- 最后一行打印出模型的结构信息。
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`。
解释一下:model = ResNet18([2, 2, 2, 2]) model.build(input_shape=(128, 32, 32, 1)) model.compile(optimizer = tf.keras.optimizers.Adam(lr = 1e-3), loss = tf.keras.losses.MSE, metrics = ['MSE']) checkpoint_save_path = "./checkpoint/InceptionNet_im_3/checkpoint.ckpt"
这段代码定义了一个名为`model`的ResNet18模型,并使用给定的超参数和输入形状来构建模型。其中`[2, 2, 2, 2]`表示ResNet18中每个残差块里有2个卷积层,一共有4个残差块,因此有16个卷积层。`input_shape=(128, 32, 32, 1)`表示输入数据的形状是`(batch_size, height, width, channels)`,其中`batch_size=128`,`height=32`,`width=32`,`channels=1`。模型使用Adam优化器和均方误差损失函数进行编译,同时计算均方误差指标。最后,将模型的checkpoint保存路径设置为`"./checkpoint/InceptionNet_im_3/checkpoint.ckpt"`,用于在训练过程中保存模型的权重。