Tensorflow 训练与可变批量大小

Question

是否可以使用 Tensorflow/Keras 并训练具有可变批量大小的 model？ 所以我在每个时代都有不同大小的批次。 我认为使用高级 tf.keras API 这是不可能的吗？

Answer 1

您可以使用model.train_on_batch ：

import tensorflow as tf
import random
import numpy as np
from sklearn.datasets import load_iris

data, target = load_iris(return_X_y=True)

both = list(zip(data.tolist(), target.tolist()))

random.shuffle(both)

data, target = map(list, zip(*both))

ds = [
    [np.array(data[:13]), np.array(target[:13])],
    [np.array(data[13:44]), np.array(target[13:44])],
    [np.array(data[44:77]), np.array(target[44:77])],
    [np.array(data[77:121]), np.array(target[77:121])],
    [np.array(data[121:]), np.array(target[121:])]
]

model = tf.keras.Sequential([
    tf.keras.layers.Dense(8, input_shape=(4,), activation="relu"),
    tf.keras.layers.Dense(16, activation="relu"),
    tf.keras.layers.Dense(3, activation="softmax")
])

model.compile(loss='sparse_categorical_crossentropy', 
              optimizer='adam',
              metrics=['accuracy'])

for i in range(100):
    for x, y in ds:
        loss, acc = model.train_on_batch(x, y)
    if not i % 10:
        print(f'epoch {i:2d} loss {loss:=5.3f}, acc {acc:=6.2%}')

epoch  0 loss 1.144, acc 37.93%
epoch 10 loss 0.960, acc 37.93%
epoch 20 loss 0.831, acc 65.52%
epoch 30 loss 0.712, acc 65.52%
epoch 40 loss 0.606, acc 65.52%
epoch 50 loss 0.522, acc 68.97%
epoch 60 loss 0.440, acc 86.21%
epoch 70 loss 0.371, acc 93.10%
epoch 80 loss 0.317, acc 96.55%
epoch 90 loss 0.273, acc 96.55%

Answer 2

您可以通过从头开始编写自定义训练循环。

这是一个简单的例子：

import tensorflow as tf

model = tf.keras.Sequential([
    tf.keras.layers.Dense(1, input_shape=(1,), activation="sigmoid")
])

# creating some random data with different batch sizes, 32, 7, 1
data = [
    (tf.random.normal((32,1)), tf.random.normal((32,))),
    (tf.random.normal((7,1)), tf.random.normal((7,))),
    (tf.random.normal((1,1)), tf.random.normal((1,))),
]

loss_func = tf.losses.MSE
opt = tf.optimizers.SGD()

for x,y in data:
    print(f"Batch size: {x.shape[0]}")
    with tf.GradientTape() as tape:
        pred = model(x)
        loss = loss_func(y, pred)
    grad = tape.gradient(loss, model.trainable_variables)
    opt.apply_gradients(zip(grad, model.trainable_variables))