Question

我正在尝试使用vgg16预训练模型修改三类分类的keras示例。这是我的代码：

import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dropout, Flatten, Dense
from keras import applications
from keras.optimizers import SGD
from keras import backend as K
K.set_image_dim_ordering('tf')
img_width, img_height = 224, 224
top_model_weights_path = 'bottleneck_fc_model.h5'
train_data_dir = 'data6/train'
validation_data_dir = 'data6/validation'
nb_train_samples = 300
nb_validation_samples = 60
epochs = 50
batch_size = 10
def save_bottlebeck_features():
   datagen = ImageDataGenerator(rescale=1. / 255)
   model = applications.VGG16(include_top=False, weights='imagenet', input_shape=(224, 224, 3))
   generator = datagen.flow_from_directory(
               train_data_dir,
               target_size=(img_width, img_height),
               batch_size=batch_size,
               class_mode='categorical',
               shuffle=False)
   bottleneck_features_train = model.predict_generator(
               generator, nb_train_samples // batch_size)
   np.save(open('bottleneck_features_train', 'wb'),bottleneck_features_train)

   generator = datagen.flow_from_directory(
               validation_data_dir,
               target_size=(img_width, img_height),
               batch_size=batch_size,
               class_mode='categorical',
               shuffle=False)
   bottleneck_features_validation = model.predict_generator(
               generator, nb_validation_samples // batch_size)
   np.save(open('bottleneck_features_validation', 'wb'),bottleneck_features_validation)

def train_top_model():
   train_data = np.load(open('bottleneck_features_train.npy', 'rb'))
   train_labels = np.array([0] * (nb_train_samples // 3) + [1] *       (nb_train_samples // 3 + [2] * (nb_train_samples // 3)))
   validation_data = np.load(open('bottleneck_features_validation.npy', 'rb'))
   validation_labels = np.array([0] * (nb_validation_samples // 3) + [1] * (nb_validation_samples // 3 + [2] * (nb_validation_samples // 3))
   model = Sequential()
   model.add(Flatten(input_shape=train_data.shape[1:]))
   model.add(Dense(128, activation='relu'))
   model.add(Dropout(0.5))
   model.add(Dense(3, activation='sigmoid'))
   sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=False)
   model.compile(optimizer=sgd,
         loss='categorical_crossentropy', metrics=['accuracy'])
   model.fit(train_data, train_labels,
          epochs=epochs,
          batch_size=batch_size,
   validation_data=(validation_data, validation_labels))
          model.save_weights(top_model_weights_path)

save_bottlebeck_features()
train_top_model()

我在这行收到了无效的语法：model = Sequential（）请帮我改正。期待。

Answer 1

调整代码以使其与tensorflow 2.0和tf.keras兼容。

from tf.keras.preprocessing.image import ImageDataGenerator
from tf.keras.models import Sequential
from tf.keras.layers import Dropout, Flatten, Dense
from tf.keras import applications
from tf.keras.optimizers import SGD
from tf.keras import backend as K
K.set_image_dim_ordering('tf')
img_width, img_height = 224, 224
top_model_weights_path = 'bottleneck_fc_model.h5'
train_data_dir = 'data6/train'
validation_data_dir = 'data6/validation'
nb_train_samples = 300
nb_validation_samples = 60
epochs = 50
batch_size = 10
def save_bottlebeck_features():
   datagen = ImageDataGenerator(rescale=1. / 255)
   model = applications.VGG16(include_top=False, weights='imagenet', input_shape=(224, 224, 3))
   generator = datagen.flow_from_directory(
               train_data_dir,
               target_size=(img_width, img_height),
               batch_size=batch_size,
               class_mode='categorical',
               shuffle=False)
   bottleneck_features_train = model.predict_generator(
               generator, nb_train_samples // batch_size)
   np.save(open('bottleneck_features_train', 'wb'),bottleneck_features_train)

   generator = datagen.flow_from_directory(
               validation_data_dir,
               target_size=(img_width, img_height),
               batch_size=batch_size,
               class_mode='categorical',
               shuffle=False)
   bottleneck_features_validation = model.predict_generator(
               generator, nb_validation_samples // batch_size)
   np.save(open('bottleneck_features_validation', 'wb'),bottleneck_features_validation)

def train_top_model():
   train_data = np.load(open('bottleneck_features_train.npy', 'rb'))
   train_labels = np.array([0] * (nb_train_samples // 3) + [1] *       (nb_train_samples // 3 + [2] * (nb_train_samples // 3)))
   validation_data = np.load(open('bottleneck_features_validation.npy', 'rb'))
   validation_labels = np.array([0] * (nb_validation_samples // 3) + [1] * (nb_validation_samples // 3 + [2] * (nb_validation_samples // 3)))
   model = Sequential()
   model.add(Flatten(input_shape=train_data.shape[1:]))
   model.add(Dense(128, activation='relu'))
   model.add(Dropout(0.5))
   model.add(Dense(3, activation='sigmoid'))
   sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=False)
   model.compile(optimizer=sgd,
         loss='categorical_crossentropy', metrics=['accuracy'])
   model.fit(train_data, train_labels,
          epochs=epochs,
          batch_size=batch_size,
   validation_data=(validation_data, validation_labels))
          model.save_weights(top_model_weights_path)

save_bottlebeck_features()
train_top_model()

使用Keras进行多类分类

1 个答案: