python - 如何减少训练过程中模型损失的差异？(How to reduce variance of the model loss during training?)

Question

I know that stochastic gradient descent always gives different results.

(我知道随机梯度下降总是会产生不同的结果。)

What are the best practices to reduce this variance today?

(今天有什么最佳实践来减少这种差异？)

I tried to predict simple function with two different approaches and every time I train them I see very different results.

(我试图用两种不同的方法来预测简单函数，每次训练它们时，我都会看到截然不同的结果。)

Input data:

(输入数据：)

def plot(model_out):
  fig, ax = plt.subplots()
  ax.grid(True, which='both')
  ax.axhline(y=0, color='k', linewidth=1)
  ax.axvline(x=0, color='k', linewidth=1)

  ax.plot(x_line, y_line, c='g', linewidth=1)
  ax.scatter(inputs, targets, c='b', s=8)
  ax.scatter(inputs, model_out, c='r', s=8)

a = 5.0; b = 3.0; x_left, x_right = -16., 16.
NUM_EXAMPLES = 200
noise   = tf.random.normal((NUM_EXAMPLES,1))

inputs  = tf.random.uniform((NUM_EXAMPLES,1), x_left, x_right)
targets = a * tf.sin(inputs) + b + noise
x_line  = tf.linspace(x_left, x_right, 500)
y_line  = a * tf.sin(x_line) + b

Keras training:

(Keras培训：)

model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(50, activation='relu', input_shape=(1,)))
model.add(tf.keras.layers.Dense(50, activation='relu'))
model.add(tf.keras.layers.Dense(1))

model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(0.01))
model.fit(inputs, targets, batch_size=200, epochs=2000, verbose=0)

print(model.evaluate(inputs, targets, verbose=0))
plot(model.predict(inputs))

Manual training:

(手动培训：)

model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(50, activation='relu', input_shape=(1,)))
model.add(tf.keras.layers.Dense(50, activation='relu'))
model.add(tf.keras.layers.Dense(1))

optimizer = tf.keras.optimizers.Adam(0.01)

@tf.function
def train_step(inpt, targ):
  with tf.GradientTape() as g:
    model_out = model(inpt)
    model_loss = tf.reduce_mean(tf.square(tf.math.subtract(targ, model_out)))

  gradients = g.gradient(model_loss, model.trainable_variables)
  optimizer.apply_gradients(zip(gradients, model.trainable_variables))
  return model_loss

train_ds = tf.data.Dataset.from_tensor_slices((inputs, targets))
train_ds = train_ds.repeat(2000).batch(200)

def train(train_ds):
  for inpt, targ in train_ds:
    model_loss = train_step(inpt, targ)
  tf.print(model_loss)

train(train_ds)
plot(tf.squeeze(model(inputs)))

  ask by dereks translate from so

Answer 1

等待大神答复