编辑:感谢您的评论,我对您的问题有了更好的了解。以下代码远非理想,并且没有考虑批量训练等,但它可能会给您一个很好的起点。我编写了一个自定义训练步骤,它基本上替代了该model.fit
方法。可能有更好的方法可以做到这一点,但它应该可以让您快速比较渐变。
def custom_training(model, data):
x, y = data
# Training
with tf.GradientTape() as tape:
y_pred = model(x, training=True) # Forward pass
# Compute the loss value
# (the loss function is configured in `compile()`)
loss = tf.keras.losses.mse(y, y_pred)
trainable_vars = model.trainable_variables
gradients = tape.gradient(loss, trainable_vars)
tf.keras.optimizers.Adam().apply_gradients(zip(gradients, trainable_vars))
# computing the gradient without optimizing it!
with tf.GradientTape() as tape:
y_pred = model(x, training=False) # Forward pass
# Compute the loss value
# (the loss function is configured in `compile()`)
loss = tf.keras.losses.mse(y, y_pred)
trainable_vars = model.trainable_variables
gradients_plus = tape.gradient(loss, trainable_vars)
return gradients, gradients_plus
让我们假设一个非常简单的模型:
import tensorflow as tf
train_data = tf.random.normal((1000, 32))
train_features = tf.random.normal((1000,))
inputs = tf.keras.layers.Input(shape=(32))
hidden_1 = tf.keras.layers.Dense(32)(inputs)
hidden_2 = tf.keras.layers.Dense(32)(hidden_1)
outputs = tf.keras.layers.Dense(1)(hidden_2)
model = tf.keras.Model(inputs, outputs)
你想计算所有层相对于输入的梯度。您可以使用以下内容:
with tf.GradientTape(persistent=True) as tape:
tape.watch(inputs)
out_intermediate = []
inputs = train_data
cargo = model.layers[0](inputs)
for layer in model.layers[1:]:
cargo = layer(cargo)
out_intermediate.append(cargo)
for x in out_intermediate:
print(tape.gradient(x, inputs))
如果您想计算自定义损失,我建议自定义 Model.fit 中发生的情况