具有包含多个输入的自定义损失的 Tensorflow - 图形断开连接错误

Question

我有一个 CNN 输出标量，该输出与 MLP 的输出连接，然后馈送到另一个密集层。我收到图形断开连接错误

请告知如何解决此问题。提前致谢。

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)

图表断开连接：无法获取层“input_8”处的张量 Tensor(“input_8:0”, shape=(None, 2), dtype=float32) 的值.可以毫无问题地访问以下先前的层：['input_7'、'conv2d_12'、'conv2d_13'、'conv2d_14'、'conv2d_15'、'flatten_3'、'mlp_input', 'dense_24', 'dense_27', 'dense_25', 'dense_28', 'dense_29', 'dense_26', 'concatenate_3', 'dense_30', 'dense_31']

Answer 1

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

自定义 Model.fit 中发生的事情

我们创建一个新的 class 子class 是 keras.Model。
我们只是覆盖方法 train_step(self, data).
我们return一个字典映射度量名称（包括损失）到它们的当前值。

例如您的模型：

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>

具有包含多个输入的自定义损失的 Tensorflow - 图形断开连接错误

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