Предсказание мультиклассификации CNN () выводит только 1 вместо вероятностей всех классов - PullRequest
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Предсказание мультиклассификации CNN () выводит только 1 вместо вероятностей всех классов

0 голосов
/ 19 января 2019

Я играл с классификацией изображений.Я получил довольно точную классификацию, но не могу получить правильный вывод вероятностей, распределенных между всеми классами.

У меня есть softmax в качестве последнего выходного слоя с правильным количеством выходных классов, и я используюметод предиката ().Из моего ограниченного понимания это должно работать.Тем не менее, он не предсказывает правильные классификации, но я понятия не имею, насколько он уверен.

Пример вывода: [0,0,0,1,0]

Пример ожидаемого вывода: [.025, .01, .2, .75, .015] 

import os
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPool2D
from keras.layers import Flatten
from keras.layers import Dense, Dropout

"""
import tensorflow as tf
from keras import backend as K

num_cores = 6

config = tf.ConfigProto(intra_op_parallelism_threads=num_cores,
                        inter_op_parallelism_threads=num_cores)
session = tf.Session(config=config)
K.set_session(session)
"""

#from keras.models import load_model

#Got 25/26 w/ this one.
a_type = "relu"
d_type = "softmax"
i_shape = 512 #512 is the one to use
b_size = 32
e_poch = 15
output = 29

#====================================================================
#                               CNN
#====================================================================
classifier = Sequential()
#====================================================================
classifier.add(Conv2D(filters = 32, kernel_size = (3,3), 
                      input_shape = (i_shape,i_shape,1), 
                      activation = a_type))
classifier.add(MaxPool2D(pool_size = (2,2), strides = 2))
classifier.add(Dropout(.20))
#====================================================================
classifier.add(Conv2D(filters = 64, kernel_size = (3,3), 
                      input_shape = (i_shape,i_shape,1), 
                      activation = a_type))
classifier.add(MaxPool2D(pool_size = (2,2), strides = 2))
classifier.add(Dropout(.20))
#====================================================================
classifier.add(Conv2D(filters = 128, kernel_size = (3,3), 
                      input_shape = (i_shape,i_shape,1), 
                      activation = a_type))
classifier.add(MaxPool2D(pool_size = (2,2), strides = 2))
classifier.add(Dropout(.20))

classifier.add(Flatten())


#====================================================================


#====================================================================

#this was relu
classifier.add(Dense(units = 128, activation = a_type))
classifier.add(Dropout(.25))
classifier.add(Dense(units = 64, activation = a_type))
classifier.add(Dense(units = output, activation = d_type))
#print(classifier.summary())
classifier.compile(optimizer = 'adam',
                   loss = 'categorical_crossentropy', 
                   metrics= ['accuracy'])


#====================================================================


#====================================================================
#                       Image Processing
#====================================================================
from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(
        rescale=1./255)
        #shear_range=0.2,
        #zoom_range=0.2,
        #horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1./255)

training_set = train_datagen.flow_from_directory('dataset/training_set',
                                target_size=(i_shape, i_shape), #match to input_shape
                                color_mode='grayscale',
                                batch_size=b_size,
                                class_mode='categorical')

test_set = test_datagen.flow_from_directory('dataset/test_set',
                                            target_size=(i_shape, i_shape),
                                            color_mode='grayscale',
                                            batch_size=b_size,
                                            class_mode='categorical')

#====================================================================

test = classifier.fit_generator(training_set,
                        steps_per_epoch=(6230/b_size),
                        epochs=e_poch,
                        validation_data=test_set,
                        validation_steps=(699/b_size))

#====================================================================
#                     Save the model and weights
#====================================================================
classifier.save(a_type+'_'+d_type+'_'+str(output)+'_'+str(i_shape)+'_'+str(b_size)+'_'+str(e_poch)+'.h5')

#This will load the model
#class2 = load_model('path to .h5 file')

#classifier = load_model(a_type+'_'+d_type+str(i_shape)+'_'+str(b_size)+'_'+str(e_poch)+'.h5')
#classifier = load_model('256_32_50.h5')
#====================================================================
#                     Predict a new outcome
#====================================================================
import numpy as np
from keras.preprocessing import image
result_p = []
for root, dirs, files in os.walk(os.path.abspath("./dataset/single_prediction/")):
    for file in files:
        path = os.path.join(root, file)
        test_image = image.load_img(path,
                                    target_size = (i_shape, i_shape), 
                                    color_mode='grayscale')
        test_image = image.img_to_array(test_image)
        test_image = np.expand_dims(test_image, axis = 0)
        result_p.append(classifier.predict(test_image, batch_size=b_size))
        #result = classifier.predict_proba(test_image)
        result = classifier.predict_classes(test_image)
        print(path)
        print(result)
"""
test_image = image.load_img('dataset/single_prediction/VB.AT/1.png', 
                            target_size = (i_shape, i_shape), 
                            color_mode='grayscale')
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = classifier.predict(test_image)
"""
print(training_set.class_indices)
#print(result_p)
...