class Model(object):
def __init__(self):
self.model = Sequential()
self.model.add(
Conv2D(32, (3, 3), input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3)))
self.model.add(Activation('relu'))
self.model.add(MaxPooling2D(pool_size=(2, 2)))
self.model.add(Conv2D(32, (3, 3)))
self.model.add(Activation('relu'))
self.model.add(MaxPooling2D(pool_size=(2, 2)))
self.model.add(Conv2D(64, (3, 3)))
self.model.add(Activation('relu'))
self.model.add(MaxPooling2D(pool_size=(2, 2)))
self.model.add(Flatten())
self.model.add(Dense(64))
self.model.add(Activation('relu'))
self.model.add(Dropout(0.5))
self.model.add(Dense(1))
self.model.add(Activation('sigmoid'))
def load(self, file_path=FILE_PATH):
print('Model Loaded.')
self.model.load_weights(file_path)
def predict(self, image):
# 预测样本分类
img = image.resize((1, IMAGE_SIZE, IMAGE_SIZE, 3))
img = image.astype('float32')
img /= 255
#归一化
result = self.model.predict(img)
print(result)
# 概率
result = self.model.predict_classes(img)
print(result)
# 0/1
return result[0]
class Model(object):
def __init__(self):
self.model = Sequential()
self.model.add(
Conv2D(32, (3, 3), input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3)))
self.model.add(Activation('relu'))
self.model.add(MaxPooling2D(pool_size=(2, 2)))
self.model.add(Conv2D(32, (3, 3)))
self.model.add(Activation('relu'))
self.model.add(MaxPooling2D(pool_size=(2, 2)))
self.model.add(Conv2D(64, (3, 3)))
self.model.add(Activation('relu'))
self.model.add(MaxPooling2D(pool_size=(2, 2)))
self.model.add(Flatten())
self.model.add(Dense(64))
self.model.add(Activation('relu'))
self.model.add(Dropout(0.5))
self.model.add(Dense(1))
self.model.add(Activation('sigmoid'))
def train(self, dataset, batch_size=batch_size, nb_epoch=epochs):
self.model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
self.model.fit_generator(
dataset.train,
steps_per_epoch=nb_train_samples // batch_size,
epochs=epochs,
validation_data=dataset.valid,
validation_steps=nb_validation_samples // batch_size)
def save(self, file_path=FILE_PATH):
print('Model Saved.')
self.model.save_weights(file_path)
def load(self, file_path=FILE_PATH):
print('Model Loaded.')
self.model.load_weights(file_path)
def predict(self, image):
# 预测样本分类
img = image.resize((1, IMAGE_SIZE, IMAGE_SIZE, 3))
img = image.astype('float32')
img /= 255
#归一化
result = self.model.predict(img)
print(result)
# 概率
result = self.model.predict_classes(img)
print(result)
# 0/1
return result[0]
def evaluate(self, dataset):
# 测试样本准确率
score = self.model.evaluate_generator(dataset.valid, steps=2)
print("样本准确率%s: %.2f%%" %
(self.model.metrics_names[1], score[1] * 100))
