train_generator = train_datagen.flow_from_directory( train_dir, target_size=img_size, batch_size=batch_size, class_mode='categorical' )
时间: 2024-05-22 19:11:41 浏览: 13
这段代码是用来设置训练数据生成器的。训练数据生成器的作用是将训练数据集中的图像进行预处理,并将其转换为模型能够处理的格式,在模型训练过程中逐批次地输入到模型中进行训练。
`train_datagen` 是一个 ImageDataGenerator 对象,它可以在训练过程中对图像进行数据增强,从而增加模型的鲁棒性和泛化能力。
`train_dir` 是训练数据集所在的目录路径。
`target_size` 是将图像缩放到的大小。
`batch_size` 是每个批次输入的图像数量。
`class_mode` 是分类方式,`'categorical'` 表示分类标签是 one-hot 编码形式的分类结果。
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))
```
import tensorflow as tf from tensorflow.keras.preprocessing.image import ImageDataGenerator # 设置训练和验证数据集路径 train_dir = 'train/' validation_dir = 'validation/' # 设置图像的大小和通道数 img_width = 150 img_height = 150 img_channels = 3 # 设置训练和验证数据集的batch size batch_size = 32 # 使用ImageDataGenerator来进行数据增强 train_datagen = ImageDataGenerator( rescale=1./255, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest') validation_datagen = ImageDataGenerator(rescale=1./255) #使用flow_from_directory方法来读取数据集 train_generator = train_datagen.flow_from_directory( train_dir, target_size=(img_width, img_height), batch_size=batch_size, class_mode='binary') validation_generator = validation_datagen.flow_from_directory( validation_dir, target_size=(img_width, img_height), batch_size=batch_size, class_mode='binary') # 使用Sequential模型来搭建神经网络 model = tf.keras.models.Sequential([ tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(img_width, img_height, img_channels)), tf.keras.layers.MaxPooling2D((2, 2)), tf.keras.layers.Conv2D(64, (3, 3), activation='relu'), tf.keras.layers.MaxPooling2D((2, 2)), tf.keras.layers.Conv2D(128, (3, 3), activation='relu'), tf.keras.layers.MaxPooling2D((2, 2)), tf.keras.layers.Conv2D(128, (3, 3), activation='relu'), tf.keras.layers.MaxPooling2D((2, 2)), tf.keras.layers.Flatten(), tf.keras.layers.Dense(512, activation='relu'), tf.keras.layers.Dense(1, activation='sigmoid')]) # 编译模型 model.compile(optimizer=tf.keras.optimizers.RMSprop(lr=1e-4), loss='binary_crossentropy', metrics=['accuracy']) # 训练模型 history = model.fit(train_generator, steps_per_epoch=100, epochs=100, validation_data=validation_generator, validation_steps=50) # 保存模型 model.save('cat_dog_classifier.h5')解释每一行代码
1. `import tensorflow as tf`: 导入TensorFlow库。
2. `from tensorflow.keras.preprocessing.image import ImageDataGenerator`: 导入Keras中的ImageDataGenerator类,用于数据增强。
3. `train_dir = 'train/' validation_dir = 'validation/'`: 设置训练和验证数据集的路径。
4. `img_width = 150 img_height = 150 img_channels = 3`: 设置图像的大小和通道数。
5. `batch_size = 32`: 设置训练和验证数据集的batch size。
6. `train_datagen = ImageDataGenerator(rescale=1./255, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest')`: 创建一个ImageDataGenerator对象train_datagen,用于对训练数据进行数据增强。
7. `validation_datagen = ImageDataGenerator(rescale=1./255)`: 创建一个ImageDataGenerator对象validation_datagen,用于对验证数据进行数据增强。
8. `train_generator = train_datagen.flow_from_directory(train_dir, target_size=(img_width, img_height), batch_size=batch_size, class_mode='binary')`: 使用data_generator.flow_from_directory方法读取训练数据,并进行数据增强。
9. `validation_generator = validation_datagen.flow_from_directory(validation_dir, target_size=(img_width, img_height), batch_size=batch_size, class_mode='binary')`: 使用data_generator.flow_from_directory方法读取验证数据,并进行数据增强。
10. `model = tf.keras.models.Sequential([...])`: 使用Sequential模型搭建神经网络模型。
11. `model.compile(optimizer=tf.keras.optimizers.RMSprop(lr=1e-4), loss='binary_crossentropy', metrics=['accuracy'])`: 编译模型,指定优化器、损失函数和评估指标。
12. `history = model.fit(train_generator, steps_per_epoch=100, epochs=100, validation_data=validation_generator, validation_steps=50)`: 训练模型,并返回训练过程的历史记录。
13. `model.save('cat_dog_classifier.h5')`: 保存模型。
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