Tensorflow自定义丢失包含多个输入-图形断开连接错误

Question

我有一个CNN输出——一个标量，这个输出与MLP的输出级联，然后馈送到另一个密集层。我得到一个图形断开错误

请告知如何解决这个问题。提前谢谢。

from tensorflow.keras.models import  Model 
from tensorflow.keras.layers import Conv2D, Dense, Flatten, concatenate, Input
import tensorflow as tf
tf.keras.backend.clear_session()

#----custom function
def custom_loss(ytrue, ypred):
loss = tf.math.log(1. + ytrue) - tf.math.log(1. + ypred)
loss = tf.math.square(loss)
loss = tf.math.reduce_mean(loss)
return loss   

#------------------
cnnin = Input(shape=(10, 10, 1))
x = Conv2D(8, 4)(cnnin)
x = Conv2D(16, 4)(x)
x = Conv2D(32, 2)(x)
x = Conv2D(64, 2)(x)
x = Flatten()(x)
x = Dense(4)(x)
x = Dense(4, activation="relu")(x)
cnnout = Dense(1, activation="linear")(x)
cnnmodel= Model(cnnin, cnnout, name="cnn_model")
yt = Input(shape=(2, ))   #---dummy input 
#---mlp start 
mlpin    = Input(shape=(2, ), name="mlp_input")
z        = Dense(4, activation="sigmoid")(mlpin)
z        = Dense(4, activation = "softmax")(z)
mlpout   = Dense(1, activation="linear")(z)
mlpmodel = Model(mlpin, mlpout, name="mlp_model")

#----concatenate 
combinedout = concatenate([mlpmodel.output, cnnmodel.output ])
x = Dense(4, activation="sigmoid")(combinedout)
finalout = Dense(2, activation="linear")(x)

model = Model( [mlpin, cnnin], finalout)
model.add_loss(custom_loss(yt, finalout))
model.compile(optimizer='adam', learning_rate=1e-3, initialization="glorotnorm",
loss=None)

图断开：无法获得层"处张量张量("input_8:0"，shape=(None，2(，dtype=float32(的值；输入_8"；。访问以下先前的层时没有出现问题：["put_7"、"conv2d_12'、"conf2d_13'、"conv2d_14'、"conv2d_15'、"flatten_3'、"mlp_input"、"dense_24'、"dense_27'、"dence_25'、"dence_28'、"dense_29'、"dene_26'、"concatenate_3'、"dene_30'、"dense_31']

Answer 1

您可以根据https://www.tensorflow.org/guide/keras/customizing_what_happens_in_fit

• 我们创建了一个新的类，将keras子类化。模型
• 我们只是覆盖方法train_step(self，data(
• 我们返回一个字典，将度量名称(包括丢失(映射到它们的当前值

例如您的型号：

loss_tracker = tf.keras.metrics.Mean(name = "custom_loss")
class TestModel(tf.keras.Model):
def __init__(self, model1):
super(TestModel, self).__init__()
self.model1 = model1
def compile(self, optimizer):
super(TestModel, self).compile()
self.optimizer = optimizer
def train_step(self, data):
x, y = data
with tf.GradientTape() as tape:
ypred = self.model1([x], training = True)
loss_value = custom_loss(y, ypred)
# Compute gradients
trainable_vars = self.trainable_variables
gradients = tape.gradient(loss_value, trainable_vars)
# Update weights
self.optimizer.apply_gradients(zip(gradients, trainable_vars))
loss_tracker.update_state(loss_value)
return {"loss": loss_tracker.result()}
import numpy as np
x = np.random.rand(6, 10,10,1)
x2 = np.random.rand(6,2)
y = tf.ones((6,2))
model = Model( [mlpin, cnnin], finalout)
trainable_model = TestModel(model)
trainable_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate = 0.0001))
trainable_model.fit(x=(x2, x), y = y, epochs=5)

给出以下输出：

Epoch 1/5
1/1 [==============================] - 0s 382ms/step - loss: 0.2641
Epoch 2/5
1/1 [==============================] - 0s 4ms/step - loss: 0.2640
Epoch 3/5
1/1 [==============================] - 0s 6ms/step - loss: 0.2638
Epoch 4/5
1/1 [==============================] - 0s 7ms/step - loss: 0.2635
Epoch 5/5
1/1 [==============================] - 0s 6ms/step - loss: 0.2632
<tensorflow.python.keras.callbacks.History at 0x14c69572688>