Tensorflow 自定义训练循环中的 If-Else 语句
If-Else Statement in Custom Training Loop in Tensorflow
我创建了一个模型 class,它是 keras.Model 的子class。在训练模型时,我想在一些时期后更改损失函数的权重。为了做到这一点,我为我的模型创建了布尔变量,指示模型应该开始使用额外的损失函数进行训练。我添加了一个伪代码,主要展示了我想要实现的目标。
class MyModel(keras.Model):
self.start_loss_2 = False
def train_step(self):
# Check if training with loss_2 started
weight_loss_2 = 0.0
if self.start_loss_2:
weight_loss_2 = 0.5
# Pass the data through model
# Calculate two loss values
total_loss = loss_1 + weight_loss_2 * loss_2
# Calculate gradients with tf.Tape
# Update variables
# This is called via Callback after each epoch
def epoch_finised(epoch_num):
if epoch_num > START_LOSS_2:
self.start_loss_2 = True
我的问题是:
- 使用一段时间后值发生变化的if-else语句是否有效?如果不是,如何实现?
是的。您可以创建一个 tf.Variable
,然后根据一些训练标准为其分配一个新值。
示例:
import numpy as np
import tensorflow as tf
# simple toy network
x_in = tf.keras.Input((10))
x = tf.keras.layers.Dense(25)(x_in)
x_out = tf.keras.layers.Dense(1)(x)
# model
m = tf.keras.Model(x_in, x_out)
# fake data
X = tf.random.normal((100, 10))
y0 = tf.random.normal((100, ))
y1 = tf.random.normal((100, ))
# optimizer
m_opt = tf.keras.optimizers.Adam(1e-2)
# prep data
ds = tf.data.Dataset.from_tensor_slices((X, y0, y1))
ds = ds.repeat().batch(5)
train_iter = iter(ds)
# toy loss function that uses a weight
def loss_fn(y_true0, y_true1, y_pred, weight):
mse = tf.keras.losses.MSE
mse_0 = tf.math.reduce_mean(mse(y_true0, y_pred))
mse_1 = tf.math.reduce_mean(mse(y_true1, y_pred))
return mse_0 + weight * mse_1
NUM_EPOCHS = 4
NUM_BATCHES_PER_EPOCH = 10
START_NEW_LOSS_AT_GLOBAL_STEP = 20
# the weight variable set to 0 initially and then
# will be changed after a certain number of steps
# (or some other training criteria)
w = tf.Variable(0.0, trainable=False)
for epoch in range(NUM_EPOCHS):
losses = []
for batch in range(NUM_BATCHES_PER_EPOCH):
X_train, y0_train, y1_train = next(train_iter)
with tf.GradientTape() as tape:
y_hat = m(X_train)
loss = loss_fn(y0_train, y1_train, y_hat, w)
losses.append(loss)
m_vars = m.trainable_variables
m_grads = tape.gradient(loss, m_vars)
m_opt.apply_gradients(zip(m_grads, m_vars))
print(f"epoch: {epoch}\tloss: {np.mean(losses):.4f}")
losses = []
# if the criteria is met assign a huge number to see if the
# loss spikes up
if (epoch + 1) * (batch + 1) >= START_NEW_LOSS_AT_GLOBAL_STEP:
w.assign(10000.0)
# epoch: 0 loss: 1.8226
# epoch: 1 loss: 1.1143
# epoch: 2 loss: 8788.2227 <= looks like assign worked
# epoch: 3 loss: 10999.5449
我创建了一个模型 class,它是 keras.Model 的子class。在训练模型时,我想在一些时期后更改损失函数的权重。为了做到这一点,我为我的模型创建了布尔变量,指示模型应该开始使用额外的损失函数进行训练。我添加了一个伪代码,主要展示了我想要实现的目标。
class MyModel(keras.Model):
self.start_loss_2 = False
def train_step(self):
# Check if training with loss_2 started
weight_loss_2 = 0.0
if self.start_loss_2:
weight_loss_2 = 0.5
# Pass the data through model
# Calculate two loss values
total_loss = loss_1 + weight_loss_2 * loss_2
# Calculate gradients with tf.Tape
# Update variables
# This is called via Callback after each epoch
def epoch_finised(epoch_num):
if epoch_num > START_LOSS_2:
self.start_loss_2 = True
我的问题是:
- 使用一段时间后值发生变化的if-else语句是否有效?如果不是,如何实现?
是的。您可以创建一个 tf.Variable
,然后根据一些训练标准为其分配一个新值。
示例:
import numpy as np
import tensorflow as tf
# simple toy network
x_in = tf.keras.Input((10))
x = tf.keras.layers.Dense(25)(x_in)
x_out = tf.keras.layers.Dense(1)(x)
# model
m = tf.keras.Model(x_in, x_out)
# fake data
X = tf.random.normal((100, 10))
y0 = tf.random.normal((100, ))
y1 = tf.random.normal((100, ))
# optimizer
m_opt = tf.keras.optimizers.Adam(1e-2)
# prep data
ds = tf.data.Dataset.from_tensor_slices((X, y0, y1))
ds = ds.repeat().batch(5)
train_iter = iter(ds)
# toy loss function that uses a weight
def loss_fn(y_true0, y_true1, y_pred, weight):
mse = tf.keras.losses.MSE
mse_0 = tf.math.reduce_mean(mse(y_true0, y_pred))
mse_1 = tf.math.reduce_mean(mse(y_true1, y_pred))
return mse_0 + weight * mse_1
NUM_EPOCHS = 4
NUM_BATCHES_PER_EPOCH = 10
START_NEW_LOSS_AT_GLOBAL_STEP = 20
# the weight variable set to 0 initially and then
# will be changed after a certain number of steps
# (or some other training criteria)
w = tf.Variable(0.0, trainable=False)
for epoch in range(NUM_EPOCHS):
losses = []
for batch in range(NUM_BATCHES_PER_EPOCH):
X_train, y0_train, y1_train = next(train_iter)
with tf.GradientTape() as tape:
y_hat = m(X_train)
loss = loss_fn(y0_train, y1_train, y_hat, w)
losses.append(loss)
m_vars = m.trainable_variables
m_grads = tape.gradient(loss, m_vars)
m_opt.apply_gradients(zip(m_grads, m_vars))
print(f"epoch: {epoch}\tloss: {np.mean(losses):.4f}")
losses = []
# if the criteria is met assign a huge number to see if the
# loss spikes up
if (epoch + 1) * (batch + 1) >= START_NEW_LOSS_AT_GLOBAL_STEP:
w.assign(10000.0)
# epoch: 0 loss: 1.8226
# epoch: 1 loss: 1.1143
# epoch: 2 loss: 8788.2227 <= looks like assign worked
# epoch: 3 loss: 10999.5449