python 一个简单的例子:带有Keras的混淆矩阵flow_from_directory.py
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import numpy as np
from keras import backend as K
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import classification_report, confusion_matrix
#Start
train_data_path = 'F://data//Train'
test_data_path = 'F://data//Validation'
img_rows = 150
img_cols = 150
epochs = 30
batch_size = 32
num_of_train_samples = 3000
num_of_test_samples = 600
#Image Generator
train_datagen = ImageDataGenerator(rescale=1. / 255,
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest')
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(train_data_path,
target_size=(img_rows, img_cols),
batch_size=batch_size,
class_mode='categorical')
validation_generator = test_datagen.flow_from_directory(test_data_path,
target_size=(img_rows, img_cols),
batch_size=batch_size,
class_mode='categorical')
# Build model
model = Sequential()
model.add(Convolution2D(32, (3, 3), input_shape=(img_rows, img_cols, 3), padding='valid'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(32, (3, 3), padding='valid'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(64, (3, 3), padding='valid'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(5))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
#Train
model.fit_generator(train_generator,
steps_per_epoch=num_of_train_samples // batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=num_of_test_samples // batch_size)
#Confution Matrix and Classification Report
Y_pred = model.predict_generator(validation_generator, num_of_test_samples // batch_size+1)
y_pred = np.argmax(Y_pred, axis=1)
print('Confusion Matrix')
print(confusion_matrix(validation_generator.classes, y_pred))
print('Classification Report')
target_names = ['Cats', 'Dogs', 'Horse']
print(classification_report(validation_generator.classes, y_pred, target_names=target_names))
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