[英]What the prediction says? CNN Keras
I've created a CNN model to try to predict if the image is either a dog or a cat, but on the output I don't know what it predicted. 我创建了一个CNN模型来尝试预测图像是狗还是猫,但是在输出中我不知道它的预测。 See below:
见下文:
import pandas as pd
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator
from keras.layers import Dense, Flatten, Conv2D, Dropout, MaxPooling2D
from scipy import misc
import numpy as np
def build_classifier():
#Model based on 'https://www.researchgate.net/profile/Le_Lu/publication/277335071/figure/fig8/AS:294249976352779@1447166069905/Figure-8-The-proposed-CNN-model-architecture-is-composed-of-five-convolutional-layers.png'
#It's smarter to add layer without creating variables because of the processing, but as a small dataset it doesn't matter a lot.
classifier = Sequential()
conv1 = Conv2D(filters=64, kernel_size=(2,2), activation='relu', input_shape=(64,64,3))
conv2 = Conv2D(filters=192, kernel_size=(2,2), activation='relu')
conv3 = Conv2D(filters=384, kernel_size=(2,2), activation='relu')
conv4 = Conv2D(filters=256, kernel_size=(2,2), activation='relu')
conv5 = Conv2D(filters=256, kernel_size=(2,2), activation='relu')
pooling1 = MaxPooling2D(pool_size=(2,2))
pooling2 = MaxPooling2D(pool_size=(2,2))
pooling3 = MaxPooling2D(pool_size=(2,2))
fcl1 = Dense(1024, activation='relu')
fcl2 = Dense(1024, activation='relu')
fcl3 = Dense(2, activation='softmax')
dropout1= Dropout(0.5)
dropout2 = Dropout(0.5)
flatten = Flatten()
layers = [conv1, pooling1, conv2, pooling2, conv3, conv4, conv5,
pooling3, flatten, fcl1, dropout1, fcl2, dropout2, fcl3]
for l in layers:
classifier.add(l)
return classifier
model = build_classifier()
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
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(
'dataset/training_set',
target_size=(64, 64),
batch_size=32,
class_mode='categorical')
validation_generator = test_datagen.flow_from_directory(
'dataset/test_set',
target_size=(64, 64),
batch_size=32,
class_mode='categorical')
model.fit_generator(
train_generator,
steps_per_epoch=200,
epochs=32,
validation_data=validation_generator,
validation_steps=100)
model.save('model.h5')
model.save_weights('model_weights.h5')
I opened my saved model in another file: 我在另一个文件中打开了保存的模型:
from keras.models import load_model
from scipy import misc
import numpy as np
def single_pred(filepath, model):
classifier = load_model(model)
img = misc.imread(filepath)
img = misc.imresize(img, (64,64,3))
img = np.expand_dims(img, 0)
print(classifier.predict(img))
if __name__ == '__main__':
single_pred('/home/leonardo/Desktop/Help/dataset/single_prediction/cat_or_dog_2.jpg', 'model.h5')
As output i get this: 作为输出我得到这个:
Using TensorFlow backend.
2017-10-09 14:06:25.520018: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-10-09 14:06:25.520054: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
[[ 0. 1.]]
But how to know if the prediction says that it is a dog or a cat. 但是如何知道预测是说它是狗还是猫。 Having this result in hands I still don't know if the image is a dog or a cat.
有了这种结果,我仍然不知道图像是狗还是猫。
Unless you specify the labels, your generator will automatically create the categorical labels for you. 除非您指定标签,否则生成器将自动为您创建分类标签。 You can inspect those using
train_generator.class_indices
The order of the class labels is alphanumeric, so cats=0 dogs=1 您可以使用
train_generator.class_indices
进行检查。类标签的顺序为字母数字,因此cats = 0 dogs = 1
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