AttributeError: 'Modell' - Objekt hat kein Attribut 'predict_classes'

Ich versuche, vorherzusagen, auf die Validierung der Daten mit bereits ausgebildeten und fein abgestimmt, und DL-Modelle. Der code folgt dem Beispiel in der Keras blog "building-Bild-Klassifizierung Modelle mit sehr wenig Daten". Hier ist der code:

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)

Das problem ist, dass die vor-geschult sind, Modell ist immer geschult auf die Daten und sagt Voraus, die Klassen perfekt und gibt die Verwirrung matrix als gut. Wie beginne ich mit der fine-tuning des Modells, die ich finden konnte, dass Modell.predict_classes nicht funktioniert. Hier ist der Fehler:

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'

Ich bin verwirrt, weil model.predict_classes geklappt mit der pre-geschult Modell, aber nicht in der fine-tuning-Phase. Die Größe der Validierung der Daten (20,1) und float32 geben. Jede Hilfe würde geschätzt werden.

InformationsquelleAutor shiva | 2017-06-28