Keras: Прогнозирование с использованием обученной модели

Question

Я абсолютный новичок в керасе. Я реализовал следующий код в керасе, нашел этот код в сети и успешно обучил его с точностью до 97%.У меня небольшая проблема во время Prediction.

Следующий код для обучения:

from __future__ import print_function
import keras
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.optimizers import SGD, Adam
from keras.utils import np_utils
import numpy as np

#seed = 7
#np.random.seed(seed)

batch_size = 50
nb_classes = 10
nb_epoch = 150
data_augmentation = False

# input image dimensions
img_rows, img_cols = 32, 32
# the CIFAR10 images are RGB
img_channels = 3

# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)

model = Sequential()

model.add(Convolution2D(32, 3, 3, border_mode='same',
                        input_shape=X_train.shape[1:]))
model.add(Activation('relu'))
model.add(Convolution2D(32, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))

# let's train the model using SGD + momentum (how original).

#sgd = SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True)
sgd= Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model.compile(loss='categorical_crossentropy',
              optimizer=sgd,
              metrics=['accuracy'])

X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255

if not data_augmentation:
    print('Not using data augmentation.')
    model.fit(X_train, Y_train,
              batch_size=batch_size,
              nb_epoch=nb_epoch,
              validation_data=(X_test, Y_test),
              shuffle=True)

else:
    print('Using real-time data augmentation.')

    # this will do preprocessing and realtime data augmentation
    datagen = ImageDataGenerator(
        featurewise_center=False,  # set input mean to 0 over the dataset
        samplewise_center=False,  # set each sample mean to 0
        featurewise_std_normalization=False,  # divide inputs by std of the dataset
        samplewise_std_normalization=False,  # divide each input by its std
        zca_whitening=False,  # apply ZCA whitening
        rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)
        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)
        horizontal_flip=True,  # randomly flip images
        vertical_flip=False)  # randomly flip images

    # compute quantities required for featurewise normalization
    # (std, mean, and principal components if ZCA whitening is applied)
    datagen.fit(X_train)

    # fit the model on the batches generated by datagen.flow()
    model.fit_generator(datagen.flow(X_train, Y_train,
                        batch_size=batch_size),
                        samples_per_epoch=X_train.shape[0],
                        nb_epoch=nb_epoch,
validation_data=(X_test, Y_test))

model.save('model3.h5')

Модель успешно сохранена, и я реализовал следующий код Prediction.

Codeдля прогноза:

import keras
import tensorflow as tf
import h5py
from keras.models import load_model
import cv2
import numpy as np

model = load_model('model3.h5')
print('Model Loaded')
dim = (32,32)
img = cv2.imread('download.jpg')
img = cv2.resize(img,dim)
Array = [np.array(img)]


Prediction = model.predict(Array)
print(Prediction)

Сгенерирована ошибка:

Using TensorFlow backend.
Model Loaded
Traceback (most recent call last):
  File "E:\Prediction\Prediction.py", line 16, in <module>
    Prediction = model.predict(Array)
  File "C:\Users\Dilip\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\engine\training.py", line 1149, in predict
    x, _, _ = self._standardize_user_data(x)
  File "C:\Users\Dilip\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\engine\training.py", line 751, in _standardize_user_data
    exception_prefix='input')
  File "C:\Users\Dilip\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\engine\training_utils.py", line 128, in standardize_input_data
    'with shape ' + str(data_shape))
ValueError: Error when checking input: expected conv2d_1_input to have 4 dimensions, but got array with shape (32, 32, 3)
>>> 

Здесь я знаю, что возникает некоторая проблема, связанная с неправильной формой входного изображения, в которое я пытался преобразовать его ((1,32,32,3) но я не смог !!

Помогите здесь, пожалуйста.

Answer 1

Похоже, вам не хватает классов в вашем коде для прогнозирования.Попробуйте вместо этого:

import cv2
import tensorflow as tf

#write the 10 classes here nb_classes
CATEGORIES = ['1','2','3','4','5','6','7','8','9','10']

def prepare(filepath):
    IMG_SIZE = 32
    img_array = cv2.imread(filepath, cv2.IMREAD_COLOR)
    new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
    return new_array.reshape(-1, IMG_SIZE, IMG_SIZE, 3) #img_channels = 3

model = tf.keras.models.load_model('model3.h5')

prediction = model.predict([prepare('download.jpg')])

print(CATEGORIES[int(prediction[0][0])])