什么地方修改batch_size

你可以在 `model.fit()` 函数中通过设置 `batch_size` 参数来修改训练批次的大小。例如：

model.fit(x_train, y_train, epochs=epochs, batch_size=batch_size, callbacks=callbacks)

在这个例子中，`batch_size` 就是你想要设置的训练批次的大小。请注意，如果你的 GPU 内存不足以容纳整个批次的数据，那么 TensorFlow 会自动减小批次的大小以适应 GPU 内存。因此，如果你想要使用更大的批次大小，你需要确保你的 GPU 内存足够大。另外，较小的批次大小可能会导致收敛速度较慢，因此你需要根据具体情况进行调整。

相关问题

for index,img in enumerate(test_img): image = Image.open(img) image = image.resize([208, 208]) image_array = np.array(image) with tf.Graph().as_default(): BATCH_SIZE = 1 N_CLASSES = 4 image = tf.cast(image_array, tf.float32) image = tf.image.per_image_standardization(image) image = tf.reshape(image, [1, 208, 208, 3]) logit = model.cnn_inference(image, BATCH_SIZE, N_CLASSES) logit = tf.nn.softmax(logit) 这里将BATCH_SIZE = 1 改为BATCH_SIZE = 16还需要修改什么

如果您将 `BATCH_SIZE` 从 1 修改为 16，那么您还需要相应地修改以下几个地方：

1. 修改 `tf.reshape()` 的参数：将 `[1, 208, 208, 3]` 修改为 `[16, 208, 208, 3]`，以匹配新的 `BATCH_SIZE`。

2. 修改 `model.cnn_inference()` 的参数：将 `BATCH_SIZE` 修改为 16，以确保模型推理时处理正确的批量大小。

3. 修改 `test_img` 的列表长度：确保 `test_img` 列表中有足够的图像路径，使其长度为 16，以便与新的 `BATCH_SIZE` 匹配。

修改后的代码示例：

import tensorflow as tf
from PIL import Image
import numpy as np

# Assuming test_img is a list of image paths

BATCH_SIZE = 16
N_CLASSES = 4

for index, img in enumerate(test_img):
    image = Image.open(img)
    image = image.resize([208,＊＊＊

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：

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))