我正在尝试将损失函数的梯度转换为另一个张量。但是梯度正乘以我输入模型的输入批量大小。
import tensorflow as tf
from tensorflow.keras import Sequential, layers
#Sample States and Returns
states = tf.random.uniform(shape = (100,4))
returns = tf.constant([float(i) for i in range(100)])
#Creating dataset to feed data to model
states = tf.data.Dataset.from_tensor_slices(states)
returns = tf.data.Dataset.from_tensor_slices(returns)
#zipping datasets into one
batch_size = 4
dataset = tf.data.Dataset.zip((states, returns)).batch(batch_size)
model = Sequential([layers.Dense(128, input_shape =(4,), activation = tf.nn.relu),
layers.Dense(1, activation = tf.nn.tanh)])
for state_batch, returns_batch in dataset:
with tf.GradientTape(persistent=True) as tape:
values = model(state_batch)
loss = returns_batch - values
# d_loss/d_values should be -1.0, but i'm getting -1.0 * batch_size
print(tape.gradient(loss,values))
break
Output:
tf.Tensor(
[[-4.]
[-4.]
[-4.]
[-4.]], shape=(4, 1), dtype=float32)
Expected Output:
tf.Tensor(
[[-1.]
[-1.]
[-1.]
[-1.]], shape=(4, 1), dtype=float32)
从代码中可以看出loss = returns - values
。应该是这样d_loss/d_values = -1.0
,但我得到的结果是d_loss/d_values = -1.0 * batch_size
。有人请指出为什么会这样?我怎样才能得到真正的结果?
colab 链接:https ://colab.research.google.com/drive/1x4pyGJ5ccRVSMzDAeLzcPXRtO7cNFnJf?usp=sharing