当我运行以下脚本时,我收到错误消息“没有为任何变量提供渐变”。 “grads”变量是“None”值的列表。这么简单的脚本可能会出什么问题?
import tensorflow as tf
import numpy as np
tf.enable_eager_execution()
class Model(tf.keras.Model):
def __init__(self):
super(Model, self).__init__()
self.layer = tf.keras.layers.Dense(4, activation = "linear")
def call(self, x):
y = self.layer(x)
return y
model = Model()
model._set_inputs(tf.zeros((1, 5)))
optimizer = tf.train.GradientDescentOptimizer(0.5)
# gibberish data
x_train = np.array([[0, 0, 0, 0, 1]], dtype=np.float32)
y_train = np.array([[0.1, 0.1, 0.4, 0.4]])
y_pred = model.call(x_train)
with tf.GradientTape() as tape:
loss = tf.losses.mean_squared_error(y_train, y_pred)
grads = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
最佳答案
模型预测线
y_pred = model.call(x_train)
必须位于 with tf.GradientTape() as Tape:
范围内。
关于python - tensorflow 梯度为 None ('No gradients provided for any variable' ),我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/56307523/