我正在尝试使用预训练和微调的深度学习模型对验证数据进行预测。 该代码遵循Keras博客上“使用极少量数据构建图像分类模型”的示例。以下是代码：
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.models import Model
from keras.layers import Flatten, Dense
from sklearn.metrics import classification_report,confusion_matrix
from sklearn.metrics import roc_auc_score
import itertools
from keras.optimizers import SGD
from sklearn.metrics import roc_curve, auc
from keras import applications
from keras import backend as K
K.set_image_dim_ordering('tf')
# Plotting the confusion matrix
def plot_confusion_matrix(cm, classes,
normalize=False, #if true all values in confusion matrix is between 0 and 1
title='Confusion matrix',
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j],
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
#plot data
def generate_results(validation_labels, y_pred):
fpr, tpr, _ = roc_curve(validation_labels, y_pred) ##(this implementation is restricted to a binary classification task)
roc_auc = auc(fpr, tpr)
plt.figure()
plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], 'k--')
plt.xlim([0.0, 1.05])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate (FPR)')
plt.ylabel('True Positive Rate (TPR)')
plt.title('Receiver operating characteristic (ROC) curve')
plt.show()
print('Area Under the Curve (AUC): %f' % roc_auc)
img_width, img_height = 100,100
top_model_weights_path = 'modela.h5'
train_data_dir = 'data4/train'
validation_data_dir = 'data4/validation'
nb_train_samples = 20
nb_validation_samples = 20
epochs = 50
batch_size = 10
def save_bottleneck_features():
datagen = ImageDataGenerator(rescale=1. / 255)
model = applications.VGG16(include_top=False, weights='imagenet', input_shape=(100,100,3))
generator = datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary',
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='binary',
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', 'rb'))
train_labels = np.array([0] * (nb_train_samples // 2) + [1] * (nb_train_samples // 2))
validation_data = np.load(open('bottleneck_features_validation', 'rb'))
validation_labels = np.array([0] * (nb_validation_samples // 2) + [1] * (nb_validation_samples // 2))
model = Sequential()
model.add(Flatten(input_shape=train_data.shape[1:]))
model.add(Dense(512, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
sgd = SGD(lr=1e-3, decay=0.00, momentum=0.99, nesterov=False)
model.compile(optimizer=sgd,
loss='binary_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)
print('Predicting on test data')
y_pred = model.predict_classes(validation_data)
print(y_pred.shape)
print('Generating results')
generate_results(validation_labels[:,], y_pred[:,])
print('Generating the ROC_AUC_Scores') #Compute Area Under the Curve (AUC) from prediction scores
print(roc_auc_score(validation_labels,y_pred)) #this implementation is restricted to the binary classification task or multilabel classification task in label indicator format.
target_names = ['class 0(Normal)', 'class 1(Abnormal)']
print(classification_report(validation_labels,y_pred,target_names=target_names))
print(confusion_matrix(validation_labels,y_pred))
cnf_matrix = (confusion_matrix(validation_labels,y_pred))
np.set_printoptions(precision=2)
plt.figure()
# Plot non-normalized confusion matrix
plot_confusion_matrix(cnf_matrix, classes=target_names,
title='Confusion matrix')
plt.show()
save_bottleneck_features()
train_top_model()
# path to the model weights files.
weights_path = '../keras/examples/vgg16_weights.h5'
top_model_weights_path = 'modela.h5'
# dimensions of our images.
img_width, img_height = 100, 100
train_data_dir = 'data4/train'
validation_data_dir = 'data4/validation'
nb_train_samples = 20
nb_validation_samples = 20
epochs = 50
batch_size = 10
# build the VGG16 network
base_model = applications.VGG16(weights='imagenet', include_top=False, input_shape=(100,100,3))
print('Model loaded.')
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
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')
top_model = Sequential()
top_model.add(Flatten(input_shape=base_model.output_shape[1:]))
top_model.add(Dense(512, activation='relu'))
top_model.add(Dense(1, activation='softmax'))
top_model.load_weights(top_model_weights_path)
model = Model(inputs=base_model.input, outputs=top_model(base_model.output))
# set the first 15 layers (up to the last conv block)
# to non-trainable (weights will not be updated)
for layer in model.layers[:15]: #up to the layer before the last convolution block
layer.trainable = False
model.summary()
# fine-tune the model
model.compile(loss='binary_crossentropy', optimizer=SGD(lr=1e-4, momentum=0.99), metrics=['accuracy'])
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=1)
model.save_weights(top_model_weights_path)
bottleneck_features_validation = model.predict_generator(validation_generator, nb_validation_samples // batch_size)
np.save(open('bottleneck_features_validation','wb'), bottleneck_features_validation)
validation_data = np.load(open('bottleneck_features_validation', 'rb'))
y_pred1 = model.predict_classes(validation_data)
问题在于预训练模型已经被训练并完美地预测了分类，并给出了混淆矩阵。但是，当我进行微调模型时，发现model.predict_classes无法正常工作。以下是错误信息：
File "C:/Users/rajaramans2/codes/untitled12.py", line 220, in <module>
y_pred1 = model.predict_classes(validation_data)
AttributeError: 'Model' object has no attribute 'predict_classes'
model.predict_classes与预训练模型相比表现不佳。验证数据的大小为（20,1），类型为float32。感谢任何提供帮助的人。