model.compile(optimizer='adam', loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
datasets = ['fer2013']
for dataset_name in datasets:
print('Training dataset:', dataset_name)
# callbacks
log_file_path = base_path + dataset_name + '_emotion_training.log'
csv_logger = CSVLogger(log_file_path, append=False)
early_stop = EarlyStopping('val_loss', patience=patience)
reduce_lr = ReduceLROnPlateau('val_loss', factor=0.1,
patience=int(patience/4), verbose=1)
trained_models_path = base_path + dataset_name + '_mini_XCEPTION'
model_names = trained_models_path + '.{epoch:02d}-{val_acc:.2f}.hdf5'
model_checkpoint = ModelCheckpoint(model_names, 'val_loss', verbose=1,
save_best_only=True)
callbacks = [model_checkpoint, csv_logger, early_stop, reduce_lr]
# loading dataset
data_loader = DataManager(dataset_name, image_size=input_shape[:2])
faces, emotions = data_loader.get_data()
faces = preprocess_input(faces)
num_samples, num_classes = emotions.shape
train_data, val_data = split_data(faces, emotions, validation_split)
val_face, val_emotion = val_dataloss, accuracy = model.evaluate(val_face, val_emotion)
print('\ntest loss: ', loss)
print('\ntest accuracy: ', accuracy)
def train_emotion_classifier():
# 参数设置
batch_size = 32 # 批量训练数据大小
epochs = 10000 # 训练轮数
input_shape = (64, 64, 1) # 图片矩阵
validation_split = .2 # 验证集大小
num_classes = 7 # 类数
patience = 50 # 信心值，用于后面的EarlyStopping等,在信心值个epochs过去后模型性能不再提升，就执行指定动作
base_path = '../datasets/trained_models/emotion_models/'
# 数据生成
data_generator = ImageDataGenerator(
featurewise_center=False, # 使输入数据集去中心化（均值为0）, 按feature执行
featurewise_std_normalization=False, # 将输入除以数据集的标准差以完成标准化, 按feature执行
rotation_range=10, # 数据提升时图片随机转动的角度
width_shift_range=0.1, # 图片宽度的某个比例，数据提升时图片水平偏移的幅度
height_shift_range=0.1, # 图片高度的某个比例，数据提升时图片竖直偏移的幅度
zoom_range=
.1, # 浮点数或形如[lower,upper]的列表，随机缩放的幅度，若为浮点数，则相当于[lower,upper] = [1 - zoom_range, 1+zoom_range]
horizontal_flip=True, # 进行随机水平翻转
)
# 模型参数/编译
model = mini_XCEPTION(input_shape, num_classes)
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
datasets = ['fer2013']
for dataset_name in datasets:
print('Training dataset:', dataset_name)
# 回调
log_file_path = base_path + dataset_name + '_emotion_training.log' # 构造log文件名
csv_logger = CSVLogger(log_file_path,
append=False) # 将epochs训练结果保存为csv
early_stop = EarlyStopping(
'val_loss', patience=patience) # 当patience个epoch过去而模型性能不提升时，停止训练
reduce_lr = ReduceLROnPlateau( # 当评价指标不在提升时，减少学习率.当学习停滞时，减少2倍或10倍的学习率常常能获得较好的效果。
monitor='val_loss',
factor=0.1, # 监视val_loss,每次减少学习率的因子为0.1
patience=int(patience / 4),
verbose=
1, # 该回调函数检测指标的情况，如果在patience个epoch中看不到模型性能提升，则减少学习率, verbose信息展示模式=1
)
trained_models_path = base_path + dataset_name + '_mini_XCEPTION'
model_names = trained_models_path + '.{epoch:02d}-{val_acc:.2f}.hdf5'
model_checkpoint = ModelCheckpoint(
model_names,
monitor='val_loss',
verbose=1,
save_best_only=True, # 只保存性能最好的模型
)
callbacks = [model_checkpoint, csv_logger, early_stop, reduce_lr]
# 加载数据集
data_loader = DataManager(dataset_name, image_size=input_shape[:2])
faces, emotions = data_loader.get_data()
faces = preprocess_input(faces)
num_samples, num_classes = emotions.shape
train_data, val_data = split_data(faces, emotions, validation_split)
train_faces, train_emotions = train_data
model.fit_generator(data_generator.flow(train_faces, train_emotions,
batch_size),
steps_per_epoch=len(train_faces) / batch_size,
epochs=epochs,
verbose=1,
callbacks=callbacks,
validation_data=val_data)
patience = 50
base_path = '../trained_models/emotion_models/'
emotion_model_path = '../trained_models/emotion_models/fer2013_my_smallerCNN.91-0.66.hdf5'
input_shape = (64, 64, 1)
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.3
with tf.Session(config=config) as session:
#Load in data
dataset = 'fer2013'
data_loader = DataManager(dataset, image_size=input_shape[:2])
faces, emotions = data_loader.get_data()
faces = preprocess_input(faces)
num_samples, num_classes = emotions.shape
train_data, val_data = split_data(faces, emotions, 0.2)
train_faces, train_emotions = train_data
print("Train_faces shape:", train_faces.shape)
print("Train_emotions shape:", train_emotions.shape)
#Generate hard data
faces = []
emotions = []
angry = 0
disgust = 0
fear = 0
happy = 0
sad = 0
surprise = 0
neutral = 0
datasets = ['fer2013']
for dataset_name in datasets:
print('Training dataset:', dataset_name)
# callbacks
log_file_path = base_path + dataset_name + '_emotion_training.log'
csv_logger = CSVLogger(log_file_path, append=False)
early_stop = EarlyStopping('val_loss', patience=patience)
reduce_lr = ReduceLROnPlateau('val_loss', factor=0.1,
patience=int(patience/4), verbose=1)
trained_models_path = base_path + dataset_name + '_mini_XCEPTION'
model_names = trained_models_path + '.{epoch:02d}-{val_acc:.2f}.hdf5'
model_checkpoint = ModelCheckpoint(model_names, 'val_loss', verbose=1,
save_best_only=True)
callbacks = [model_checkpoint, csv_logger, early_stop, reduce_lr]
# loading dataset
data_loader = DataManager(dataset_name, image_size=input_shape[:2])
faces, emotions = data_loader.get_data()
faces = preprocess_input(faces)
num_samples, num_classes = emotions.shape
train_data, val_data = split_data(faces, emotions, validation_split)
train_faces, train_emotions = train_data
model.fit_generator(data_generator.flow(train_faces, train_emotions,
batch_size),
steps_per_epoch=len(train_faces) / batch_size,
epochs=num_epochs, verbose=1, callbacks=callbacks,
validation_data=val_data)
# for循环实现回调，加载数据集
for dataset_name in datasets:
print('Training dataset:',
dataset_name) # 遍历循环，在数据dataset中逐个取值存入变量dataset_name中，然后运行循环体
data_loader = DataManager(
dataset_name, image_size=input_shape[:2]
) # 自定义DataManager函数实现根据数据集name进行加载，dataloder返回一个迭代器 对输入张量进行切片操作
faces, emotions = data_loader.get_data(
) # 自定义get_data函数根据不同数据集name得到各自的ground truth data
#faces = cv2.resize(faces.astype('uint8'), input_shape, cv2.COLOR_GRAY2BGR) ###!!!!
faces = preprocess_input(
faces) # 自定义preprocess_input函数：处理输入的数据，先转为float32类型然后/ 255.0
num_samples, num_classes = emotions.shape # shape函数读取矩阵的长度
train_data, val_data = split_data(
faces, emotions,
validation_split) # 自定义split_data对数据整理各取所得train_data、 val_data
train_faces, train_emotions = train_data
val_x, val_y = val_data
score = model.evaluate(val_x, val_y, verbose=0)
print('Test Loss:', score[0])
print('Test accuracy:', score[1])
test_true = np.argmax(val_y, axis=1)
test_pred = np.argmax(model.predict(val_x), axis=1)
print(test_pred)
print(classification_report(test_true, test_pred))
def plot_confusion_matrix(y_true,
y_pred,
classes,
