- Keras在会为Model的每一个输出构建一个loss，这些loss之间无法交互。同时，Model中每一个output，都必须在fit()方法中有对应的y_true。因此，数据输入的label数==model.outputs数==loss数。
- 而Model的每一个输入，都必须在fit()方法中有对应的x。即len(x in model.fit())==len(model.inputs)
https://blog.csdn.net/AZRRR/article/details/90380372
# 终于搞懂了loss之间的对应关系
model = Model(inputs=[img, tgt], outputs=[out1, out2])
#定义网络的时候会给出输入和输出
model.compile(optimizer=Adam(lr=lr), loss=[
losses.cc3D(), losses.gradientLoss('l2')], loss_weights=[1.0, reg_param])
#训练网络的时候指定loss，如果是多loss，loss weights分别对应前面的每个loss的权重，最后输出loss的和
train_loss = model.train_on_batch(
[x1,x2], [y_true_1, y_true_2])
开始训练，loss中的对应关系是：
推理输出out1与y_true_1算cc3D_loss，推理输出out2与y_true_2算gradientloss。
而模型的两个输入img、tgt对应的分别是数据x1，x2。
数据生成器写法：
Keras的数据生成器每次生成并返回的必须是一个tuple，而python函数返回的 x,y会被默认包装为tuple。
The output of the generator must be either
- a tuple `(inputs, targets)`
- a tuple `(inputs, targets, sample_weights)`.
因此单输入单输出的模型，数据生成器每次可以
def .....
while True:
yield x,y_true
或者，当有多输入多输出时：
def .....
While True:
yield [x1,x2,...], [label1,label2,...]
小结：
每次返回的x1,....,xn都会被自动喂入model.input中，故长度必须一致。之后模型进行推理，根据model.output获取m个output推理值，每一个output都会去调用相应的loss函数，并去获取得到对应的真实的label值，进行loss的计算。因此有m个label，对应了m个model的output数，对应了loss的数目。
也可以使用dict包裹：
def generate_arrays_from_file(path):
while True:
with open(path) as f:
for line in f:
# create numpy arrays of input data
# and labels, from each line in the file
x1, x2, y = process_line(line)
yield ({'input_1': x1, 'input_2': x2}, {'output': y})
model.fit_generator(generate_arrays_from_file('/my_file.txt'),
steps_per_epoch=10000, epochs=10)
实战案例：
- 多输入，单输出，配合Dataset API：
if __name__ == '__main__':
a = Input(shape=(368, 368, 3))
a2 = Input(shape=(368, 368, 4))
conv1 = layers.Conv2D(64, 3)(a)
conv2 = layers.Conv2D(64, 3)(conv1)
maxpool = layers.MaxPooling2D(pool_size=8, strides=8, padding='same')(conv2)
conv3 = layers.Conv2D(5, 1)(maxpool)
model = keras.Model(inputs=[a,a2], outputs=[conv3])
model.compile(optimizer=keras.optimizers.SGD(lr=0.05),
loss=keras.losses.mean_squared_error)
import numpy as np
data = np.random.rand(10, 368, 368, 3)
data2 = np.random.rand(10, 368, 368, 4)
label = np.random.rand(10, 46, 46, 5)
dataset = tf.data.Dataset.from_tensor_slices((data,data2, label)).batch(5).repeat()
iterator = dataset.make_one_shot_iterator()
# print(next(iterator))
# print(K.get_session().run(iterator.get_next())[1][0])
def mannual_iter(iter_):
next_batch = iter_.get_next()
while True:
img, img2, label = K.get_session().run(next_batch)
yield [img, img2], label
# yield [data,data2],label
with K.get_session() as sess:
model.fit_generator(mannual_iter(iterator), epochs=3, steps_per_epoch=5,
workers=1, # This is important
verbose=1
)
- 单输入，多输出：
if __name__ == '__main__':
a = Input(shape=(368, 368, 3))
a2 = Input(shape=(368, 368, 4))
conv1 = layers.Conv2D(64, 3)(a)
conv2 = layers.Conv2D(64, 3)(conv1)
maxpool = layers.MaxPooling2D(pool_size=8, strides=8, padding='same')(conv2)
conv3 = layers.Conv2D(5, 1)(maxpool)
model = keras.Model(inputs=[a], outputs=[maxpool, conv3])
model.summary()
model.compile(optimizer=keras.optimizers.SGD(lr=0.05),
loss=[keras.losses.mean_squared_error,
keras.losses.mean_squared_error,
],
loss_weights=[0.1,1])
import numpy as np
data = np.random.rand(10, 368, 368, 3)
data2 = np.random.rand(10, 368, 368, 4)
label_maxpool = np.random.rand(10, 46, 46, 64)
label = np.random.rand(10, 46, 46, 5)
dataset = tf.data.Dataset.from_tensor_slices((data, label_maxpool, label)).batch(5).repeat()
iterator = dataset.make_one_shot_iterator()
# print(next(iterator))
# print(K.get_session().run(iterator.get_next())[1][0])
def mannual_iter(iter_):
next_batch = iter_.get_next()
while True:
img, label_maxpool, label = K.get_session().run(next_batch)
yield [img], [label_maxpool, label]
# yield [data,data2],label
with K.get_session() as sess:
model.fit_generator(mannual_iter(iterator), epochs=3, steps_per_epoch=5,
workers=1, # This is important
verbose=1
)