我用keras实现了一个多类分类器。 我现在的问题是做出预测，因为我得到了一个错误。我相信它与代码的预测部分有关。
代码如下：
import numpy as np
from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img
from keras.models import Sequential
from keras.layers import Dropout, Flatten, Dense
from keras import applications
from keras.utils.np_utils import to_categorical
from PIL import Image
import matplotlib.pyplot as plt
import math
%matplotlib inline
# dimensions of our images.
img_width, img_height = 150, 150
top_model_weights_path = 'bottleneck_fc_model.h5'
train_data_dir = 'data/train'
validation_data_dir = 'data/validation'
epochs = 30
batch_size = 16
def save_bottleneck_features():
model = applications.VGG16(include_top=False, weights='imagenet')
datagen = ImageDataGenerator(rescale=1. / 255)
generator = datagen.flow_from_directory(train_data_dir, target_size=(img_width, img_height), \
batch_size=batch_size, class_mode=None, shuffle=False)
n_train_samples = len(generator.filenames)
n_classes = len(generator.class_indices)
print("Number of train files = {}".format(n_train_samples))
print("Number of classes = {}".format(n_classes))
predict_size_train = int(math.ceil(n_train_samples / batch_size))
bottleneck_features_train = model.predict_generator(generator, predict_size_train)
np.save('bottleneck_features_train.npy', bottleneck_features_train)
generator = datagen.flow_from_directory(validation_data_dir, target_size=(img_width, img_height), \
batch_size=batch_size, class_mode=None, shuffle=False)
n_validation_samples = len(generator.filenames)
predict_size_validation = int(math.ceil(n_validation_samples / batch_size))
bottleneck_features_validation = model.predict_generator(generator, predict_size_validation)
np.save('bottleneck_features_validation.npy', bottleneck_features_validation)
def train_top_model():
datagen_top = ImageDataGenerator(rescale=1./255)
generator_top = datagen_top.flow_from_directory(train_data_dir, target_size=(img_width, img_height),\
batch_size=batch_size, class_mode='categorical', \
shuffle=False)
n_train_samples = len(generator_top.filenames)
n_classes = len(generator_top.class_indices)
# load the bottleneck features saved earlier
train_data = np.load('bottleneck_features_train.npy')
# get the class lebels for the training data, in the original order
train_labels = generator_top.classes
# convert the training labels to categorical vectors
train_labels = to_categorical(train_labels, num_classes=n_classes)
generator_top = datagen_top.flow_from_directory(validation_data_dir, target_size=(img_width, img_height),\
batch_size=batch_size, class_mode=None, shuffle=False)
n_validation_samples = len(generator_top.filenames)
validation_data = np.load('bottleneck_features_validation.npy')
validation_labels = generator_top.classes
validation_labels = to_categorical(validation_labels, num_classes=n_classes)
model = Sequential()
model.add(Flatten(input_shape=train_data.shape[1:]))
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(n_classes, activation='sigmoid'))
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy', metrics=['accuracy'])
history = 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)
(eval_loss, eval_accuracy) = model.evaluate(validation_data, validation_labels, \
batch_size=batch_size, verbose=1)
print("[INFO] accuracy: {:.2f}%".format(eval_accuracy * 100))
print("[INFO] Loss: {}".format(eval_loss))
return model
要执行我们的程序：
save_bottleneck_features()
model = train_top_model()
当我尝试使用以下代码进行预测时：
img_path = 'image_test/bird.jpg'
# predicting images
img = load_img(img_path, target_size=(img_width, img_height))
x = img_to_array(img)
x = np.expand_dims(x, axis=0)
images = np.vstack([x])
classes = model.predict_classes(images, batch_size=10)
print (classes)
它给了我以下错误：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-44-c3652addeabc> in <module>()
8
9 images = np.vstack([x])
---> 10 classes = model.predict_classes(images, batch_size=10)
11 print (classes)
~/anaconda/lib/python3.6/site-packages/keras/models.py in predict_classes(self, x, batch_size, verbose)
1016 A numpy array of class predictions.
1017 """
-> 1018 proba = self.predict(x, batch_size=batch_size, verbose=verbose)
1019 if proba.shape[-1] > 1:
1020 return proba.argmax(axis=-1)
~/anaconda/lib/python3.6/site-packages/keras/models.py in predict(self, x, batch_size, verbose)
911 if not self.built:
912 self.build()
--> 913 return self.model.predict(x, batch_size=batch_size, verbose=verbose)
914
915 def predict_on_batch(self, x):
~/anaconda/lib/python3.6/site-packages/keras/engine/training.py in predict(self, x, batch_size, verbose, steps)
1693 x = _standardize_input_data(x, self._feed_input_names,
1694 self._feed_input_shapes,
-> 1695 check_batch_axis=False)
1696 if self.stateful:
1697 if x[0].shape[0] > batch_size and x[0].shape[0] % batch_size != 0:
~/anaconda/lib/python3.6/site-packages/keras/engine/training.py in _standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
142 ' to have shape ' + str(shapes[i]) +
143 ' but got array with shape ' +
--> 144 str(array.shape))
145 return arrays
146
ValueError: Error when checking : expected flatten_8_input to have shape (None, 7, 7, 512) but got array with shape (1, 150, 150, 3)
答案 0 :(得分：0)
我终于找到了答案。 为了预测图像的类，我们需要像以前一样通过相同的管道运行它。
预测功能必须是：
image_path = 'image_test/bird.jpg'
orig = cv2.imread(image_path)
print("[INFO] loading and preprocessing image...")
image = load_img(image_path, target_size=(img_width, img_height))
image = img_to_array(image)
# important! otherwise the predictions will be '0'
image = image / 255
image = np.expand_dims(image, axis=0)
# build the VGG16 network
model = applications.VGG16(include_top=False, weights='imagenet')
# get the bottleneck prediction from the pre-trained VGG16 model
bottleneck_prediction = model.predict(image)
# build top model
model = Sequential()
model.add(Flatten(input_shape=bottleneck_prediction.shape[1:]))
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(n_classes, activation='softmax'))
model.load_weights(top_model_weights_path)
# use the bottleneck prediction on the top model to get the final classification
class_predicted = model.predict_classes(bottleneck_prediction)
inID = class_predicted[0]
class_dictionary = generator_top.class_indices
inv_map = {v: k for k, v in class_dictionary.items()}
label = inv_map[inID]
# get the prediction label
print("Image ID: {}, Label: {}".format(inID, label))