相关疑难解决方法(0)

我知道如何在使用张量板训练后可视化张量流图.现在,是否可以只显示图形的前向部分,即没有定义训练操作符？

我问这个的原因是我收到了这个错误:

No gradients provided for any variable, check your graph for ops that do not support gradients, between variables [ ... list of model variables here ... ] and loss Tensor("Mean:0", dtype=float32).

我想检查图表以找出梯度张量流(双关语意图)被打破的位置.

我想在 tensorflow 2 中创建一个自定义训练循环并使用 tensorboard 进行可视化。这是我基于 tensorflow 文档创建的示例：

import tensorflow as tf
import datetime

os.environ["CUDA_VISIBLE_DEVICES"] = "0"    # which gpu to use

mnist = tf.keras.datasets.mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
test_dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test))

train_dataset = train_dataset.shuffle(60000).batch(64)
test_dataset = test_dataset.batch(64)


def create_model():
    return tf.keras.models.Sequential([
        tf.keras.layers.Flatten(input_shape=(28, 28), name='Flatten_1'),
        tf.keras.layers.Dense(512, activation='relu', name='Dense_1'),
        tf.keras.layers.Dropout(0.2, name='Dropout_1'),
        tf.keras.layers.Dense(10, activation='softmax', name='Dense_2')
    ], name='Network')


# Loss and optimizer
loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
optimizer = …