我正在尝试开发一个多输入的卷积神经网络，参考了用于花卉分级的多输入卷积神经网络这篇文章中的架构。
我有一个csv文件，其中存储了每个数据项的值，并且对于每个项目，我从不同角度捕获了4张图片。当我运行以下代码时，网络被正确打印出来，但它似乎从未开始训练，因为没有任何反应，并且使用nvidia-smi查看的GPU使用率低于5%。
kilograms_trees = tf.data.experimental.CsvDataset( filenames='dataset/agrumeto.csv', record_defaults=[tf.float32], field_delim=",", header=True)kilo_train = kilograms_trees.take(35)kilo_test = kilograms_trees.skip(35)def create_conv_layer(input): x = tf.keras.layers.Conv2D(32, (7, 7), activation='relu')(input) x = tf.keras.layers.MaxPooling2D((2, 2), (2,2))(x) x = tf.keras.Model(inputs=input, outputs=x) return xinputA = tf.keras.Input(shape=(size,size,3))inputB = tf.keras.Input(shape=(size,size,3))inputC = tf.keras.Input(shape=(size,size,3))inputD = tf.keras.Input(shape=(size,size,3))x = create_conv_layer(inputA)y = create_conv_layer(inputB)w = create_conv_layer(inputC)z = create_conv_layer(inputD)# combine the output of the two branchescombined = tf.keras.layers.concatenate([x.output, y.output, w.output, z.output])layer_1 = tf.keras.layers.Conv2D(16, (3,3), activation="relu")(combined)layer_1 = tf.keras.layers.MaxPooling2D((2, 2))(layer_1)layer_2 = tf.keras.layers.Conv2D(16, (3,3), activation="relu")(layer_1)layer_2 = tf.keras.layers.MaxPooling2D((2, 2), (2,2))(layer_2)layer_3 = tf.keras.layers.Conv2D(32, (3,3), activation="relu")(layer_2)layer_3 = tf.keras.layers.MaxPooling2D((2, 2), (2,2))(layer_3)layer_4 = tf.keras.layers.Conv2D(32, (3,3), activation="relu")(layer_3)layer_4 = tf.keras.layers.MaxPooling2D((2, 2), (2,2))(layer_4)flatten = tf.keras.layers.Flatten()(layer_4)hidden1 = tf.keras.layers.Dense(10, activation='relu')(flatten)output = tf.keras.layers.Dense(1, activation='relu')(hidden1)model = tf.keras.Model(inputs=[x.input, y.input, w.input, z.input], outputs=output)print(model.summary())model.compile(optimizer='adam', loss="mean_absolute_percentage_error")print("[INFO] training model...")model.fit([trainA, trainB, trainC, trainD], kilo_train, epochs=5, batch_size=4)test_loss, test_acc = model.evaluate([testA, testB, testC, testD], kilo_test)print(test_acc)以下是nvidia-smi的输出结果：
+-----------------------------------------------------------------------------+| NVIDIA-SMI 418.40.04 Driver Version: 418.40.04 CUDA Version: 10.1 ||-------------------------------+----------------------+----------------------+| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC || Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. ||===============================+======================+======================|| 0 GeForce GTX 1050 On | 00000000:01:00.0 Off | N/A || N/A 54C P0 N/A / N/A | 3830MiB / 4042MiB | 8% Default |+-------------------------------+----------------------+----------------------++-----------------------------------------------------------------------------+| Processes: GPU Memory || GPU PID Type Process name Usage ||=============================================================================|| 0 909 C ...ycharmProjects/agrumeto/venv/bin/python 3159MiB || 0 1729 G /usr/lib/xorg/Xorg 27MiB || 0 1870 G /usr/bin/gnome-shell 69MiB || 0 6290 G /usr/lib/xorg/Xorg 273MiB || 0 6420 G /usr/bin/gnome-shell 127MiB || 0 6834 G ...quest-channel-token=6261236721362009153 85MiB || 0 8806 G ...pycharm-professional/132/jre64/bin/java 2MiB || 0 12830 G ...-token=60E939FEF0A8E3D5C46B3D6911048536 31MiB || 0 27478 G ...-token=ECA4D3D9ADD8448674D34492E89E40E3 51MiB |+-----------------------------------------------------------------------------+以下是输出控制台的最后几行内容：
conv2d_7 (Conv2D) (None, 14, 14, 32) 9248 max_pooling2d_6[0][0] __________________________________________________________________________________________________max_pooling2d_7 (MaxPooling2D) (None, 7, 7, 32) 0 conv2d_7[0][0] __________________________________________________________________________________________________flatten (Flatten) (None, 1568) 0 max_pooling2d_7[0][0] __________________________________________________________________________________________________dense (Dense) (None, 10) 15690 flatten[0][0] __________________________________________________________________________________________________dense_1 (Dense) (None, 1) 11 dense[0][0] ==================================================================================================Total params: 69,301Trainable params: 69,301Non-trainable params: 0__________________________________________________________________________________________________None[INFO] training model...回答：
我忘记禁用TensorFlow 2.0中默认启用的Eager Execution。这就是问题所在。
