AttributeError: 'Model' object has no attribute 'predict_classes'

Question

I'm trying to predict on the validation data with pre-trained and fine-tuned DL models. The code follows the example available in the Keras blog on "building image classification models using very little data". Here is the 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) 

The problem is that the pre-trained model is getting trained on the data and predicts the classes perfectly and gives the confusion matrix as well. As I proceed to fine-tuning the model, I could find that model.predict_classes is not working. Here is the error:

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' 

I am confused because, model.predict_classes worked well with the pre-trained model, but not in the fine-tuning stage. The size of validation data is (20,1) and float32 type. Any help would be appreciated.

Answer 1

The predict_classes method is only available for the Sequential class (which is the class of your first model) but not for the Model class (the class of your second model).

With the Model class, you can use the predict method which will give you a vector of probabilities and then get the argmax of this vector (with np.argmax(y_pred1,axis=1)).

Answer 2

For a multiclass classification use: np.arg(model.predict,axis=1)

For a binary class classification use : model.predict>0.5.astype('int32')