for epoch in range(10): saving_dir = './' if epoch == 0: # save models every 5 epochs save_model(model_lstm, 'model_my_lstm', epoch, saving_dir)
时间: 2024-04-20 10:24:04 浏览: 15
这段代码是一个训练循环,它迭代了10个epoch(训练周期)。在每个epoch的开头,它检查当前的epoch是否为0。如果是第一个epoch,它会执行以下操作:
1. 定义保存模型的目录路径为'./'(当前目录)。
2. 使用自定义的`save_model`函数保存`model_lstm`模型。该函数可能会将模型保存为文件,以便在训练过程中或之后可以加载和使用。
3. 保存的模型文件名为'model_my_lstm',并且在文件名中包含了当前的epoch数和保存目录。
这个代码段的目的是在训练的开始阶段保存模型,以便在需要的时候可以恢复模型或进行模型评估等操作。根据具体需求,你可以根据自己的命名规则和保存路径进行修改。
相关问题
from keras import applications from keras.preprocessing.image import ImageDataGenerator from keras import optimizers from keras.models import Sequential, Model from keras.layers import Dropout, Flatten, Dense img_width, img_height = 256, 256 batch_size = 16 epochs = 50 train_data_dir = 'C:/Users/Z-/Desktop/kaggle/train' validation_data_dir = 'C:/Users/Z-/Desktop/kaggle/test1' OUT_CATAGORIES = 1 nb_train_samples = 2000 nb_validation_samples = 100 base_model = applications.VGG16(weights='imagenet', include_top=False, input_shape=(img_width, img_height, 3)) base_model.summary() for layer in base_model.layers[:15]: layer.trainable = False top_model = Sequential() top_model.add(Flatten(input_shape=base_model.output_shape[1:])) top_model.add(Dense(256, activation='relu')) top_model.add(Dropout(0.5)) top_model.add(Dense(OUT_CATAGORIES, activation='sigmoid')) model = Model(inputs=base_model.input, outputs=top_model(base_model.output)) model.compile(loss='binary_crossentropy', optimizer=optimizers.SGD(learning_rate=0.0001, momentum=0.9), metrics=['accuracy']) train_datagen = ImageDataGenerator(rescale=1. / 255, horizontal_flip=True) test_datagen = ImageDataGenerator(rescale=1. / 255) train_generator = train_datagen.flow_from_directory( train_data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='binary') validation_generator = test_datagen.flow_from_directory( validation_data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='binary', shuffle=False ) model.fit_generator( train_generator, steps_per_epoch=nb_train_samples / batch_size, epochs=epochs, validation_data=validation_generator, validation_steps=nb_validation_samples / batch_size, verbose=2, workers=12 ) score = model.evaluate_generator(validation_generator, nb_validation_samples / batch_size) scores = model.predict_generator(validation_generator, nb_validation_samples / batch_size)看看这段代码有什么错误
在段代码中,有一些错误和需要注意的地方:
1. `img_width` 和 `img_height` 变量没有被定义,需要先定义它们的值。
2. `OUT_CATAGORIES` 变量也没有被定义,需要给它赋值。
3. 在使用 `train_generator` 和 `validation_generator` 时,`steps_per_epoch` 和 `validation_steps` 应该为整数值,而不是浮点数。可以使用 `//` 运算符来进行整除操作。
4. 在评估模型的性能时,`evaluate_generator` 和 `predict_generator` 方法的参数应该是整数值,而不是浮点数。可以使用 `int()` 函数来将浮点数转换为整数。
下面是修改后的代码:
```python
from keras import applications
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from keras.models import Sequential, Model
from keras.layers import Dropout, Flatten, Dense
img_width, img_height = 256, 256
batch_size = 16
epochs = 50
train_data_dir = 'C:/Users/Z-/Desktop/kaggle/train'
validation_data_dir = 'C:/Users/Z-/Desktop/kaggle/test1'
OUT_CATAGORIES = 1
nb_train_samples = 2000
nb_validation_samples = 100
base_model = applications.VGG16(weights='imagenet',
include_top=False,
input_shape=(img_width, img_height, 3))
base_model.summary()
for layer in base_model.layers[:15]:
layer.trainable = False
top_model = Sequential()
top_model.add(Flatten(input_shape=base_model.output_shape[1:]))
top_model.add(Dense(256, activation='relu'))
top_model.add(Dropout(0.5))
top_model.add(Dense(OUT_CATAGORIES, activation='sigmoid'))
model = Model(inputs=base_model.input,
outputs=top_model(base_model.output))
model.compile(loss='binary_crossentropy',
optimizer=optimizers.SGD(learning_rate=0.0001, momentum=0.9),
metrics=['accuracy'])
train_datagen = ImageDataGenerator(rescale=1. / 255,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_height, img_width),
batch_size=batch_size,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_height, img_width),
batch_size=batch_size,
class_mode='binary',
shuffle=False)
model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples // batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size,
verbose=2,
workers=12)
score = model.evaluate_generator(validation_generator, int(nb_validation_samples / batch_size))
scores = model.predict_generator(validation_generator, int(nb_validation_samples / batch_size))
```
下面代码转化为paddle2.2.2代码 :log_dir = './logs/pretrain' if not os.path.isdir(log_dir): os.makedirs(log_dir) writer = SummaryWriter(log_dir) learning_rate = 1e-4 isp = torch.load('isp/ISP_CNN.pth').cuda() for k,v in isp.named_parameters(): v.requires_grad=False predenoiser = torch.load('./predenoising/PreDenoising.pth') for k,v in predenoiser.named_parameters(): v.requires_grad=False denoiser = RViDeNet(predenoiser=predenoiser).cuda() initial_epoch = findLastCheckpoint(save_dir=save_dir) if initial_epoch > 0: print('resuming by loading epoch %03d' % initial_epoch) denoiser = torch.load(os.path.join(save_dir, 'model_epoch%d.pth' % initial_epoch)) initial_epoch += 1 opt = optim.Adam(denoiser.parameters(), lr = learning_rate) # Raw data takes long time to load. Keep them in memory after loaded. gt_raws = [None] * len(gt_paths) iso_list = [1600,3200,6400,12800,25600] a_list = [3.513262,6.955588,13.486051,26.585953,52.032536] g_noise_var_list = [11.917691,38.117816,130.818508,484.539790,1819.818657] if initial_epoch==0: step=0 else: step = (initial_epoch-1)*int(len(gt_paths)/batch_size) temporal_frames_num = 3
```
import os
import paddle
from paddle import nn
from paddle.nn import functional as F
from paddle.io import DataLoader
from paddle.vision.datasets import ImageFolder
from paddle.optimizer import Adam
from paddle.utils.tensorboard import SummaryWriter
log_dir = './logs/pretrain'
if not os.path.isdir(log_dir):
os.makedirs(log_dir)
writer = SummaryWriter(log_dir)
learning_rate = 1e-4
isp = paddle.load('isp/ISP_CNN.pdparams')
for k, v in isp.named_parameters():
v.stop_gradient = True
predenoiser = paddle.load('./predenoising/PreDenoising.pdparams')
for k, v in predenoiser.named_parameters():
v.stop_gradient = True
denoiser = RViDeNet(predenoiser=predenoiser)
initial_epoch = findLastCheckpoint(save_dir=save_dir)
if initial_epoch > 0:
print('resuming by loading epoch %03d' % initial_epoch)
denoiser.set_state_dict(paddle.load(os.path.join(save_dir, 'model_epoch%d.pdparams' % initial_epoch)))
initial_epoch += 1
opt = Adam(denoiser.parameters(), lr=learning_rate)
# Raw data takes long time to load. Keep them in memory after loaded.
gt_raws = [None] * len(gt_paths)
iso_list = [1600, 3200, 6400, 12800, 25600]
a_list = [3.513262, 6.955588, 13.486051, 26.585953, 52.032536]
g_noise_var_list = [11.917691, 38.117816, 130.818508, 484.539790, 1819.818657]
if initial_epoch == 0:
step = 0
else:
step = (initial_epoch - 1) * int(len(gt_paths) / batch_size)
temporal_frames_num = 3
```