validation_data=(X_test, y_test), verbose=2, shuffle=True)
时间: 2024-04-23 12:23:20 浏览: 9
这是 Keras 中 `model.fit()` 函数中的一些参数。其中 `validation_data` 参数表示用于验证模型的数据集,通常是从训练数据中预留出来的一部分数据。`X_test` 和 `y_test` 分别是测试数据的输入和标签。`verbose` 表示训练过程中输出日志的详细程度,其取值为 0、1、2 时分别代表不输出、简略输出和详细输出。`shuffle` 表示是否打乱数据集的顺序进行训练。
相关问题
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=42) for train, test in kfold.split(X, y): history = model.fit(X[train], y[train], validation_data=(X[test], y[test]), epochs=1, batch_size=10,callbacks=[checkpoint])训练之后想打印每一个epoch的损失与准确率该如何修改代码
你可以在每个 epoch 结束时,添加一个回调函数 `History()` 并将其传递给 `fit()` 函数。这个回调函数将记录每个 epoch 的训练损失、训练准确率、验证损失和验证准确率,并将它们存储在一个字典中。你可以在训练完成后打印这个字典,以获取所有 epoch 的损失和准确率。以下是修改后的代码:
```
from keras.callbacks import ModelCheckpoint, History
# 创建一个回调函数来记录损失和准确率
history = History()
# 设置模型检查点回调
checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
# 使用回调函数训练模型
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)
for train, test in kfold.split(X, y):
model.fit(X[train], y[train], validation_data=(X[test], y[test]), epochs=1, batch_size=10,callbacks=[checkpoint, history])
# 打印每个 epoch 的损失和准确率
print(history.history)
```
这将打印一个字典,其中包含每个 epoch 的训练损失、训练准确率、验证损失和验证准确率。
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))
```