python - 使用tf.GradientTape()训练逻辑回归不能收敛

我使用tf.GradientTape训练了逻辑回归，但它无法收敛

import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

xs = np.array([[1, 1], [-1, -1], [-1, -1.1]])
ys = np.array([1, 0, 0])

def lr_model():
    inputs = keras.Input(shape=(2))
    outputs = layers.Dense(1, activation='sigmoid')(inputs)

    return keras.Model(inputs=inputs, outputs=outputs)

model = lr_model()

model.compile(loss=keras.losses.BinaryCrossentropy(),
          optimizer=keras.optimizers.SGD(0.1),
          metrics=['accuracy'])

history = model.fit(xs, ys, batch_size=3, epochs=10)

for i in range(10):
    print(i, history.history['loss'][i], history.history['accuracy'][i])

  收敛，结果：
      0 1.04525887966156 0.0
      1 0.9557339549064636 0.0
      2 0.8753216862678528 0.0
      3 0.8033372759819031 0.0
      4 0.7390384674072266 0.0
      5 0.6816689968109131 0.6666667
      6 0.6304909586906433 1.0
      7 0.5848075151443481 1.0
      8 0.5439766049385071 1.0
      9 0.5074175596237183 1.0


但我想写我的训练流程如下：

train_loss = keras.metrics.Mean(name='train_loss')
train_acc = keras.metrics.BinaryAccuracy()

model = lr_model()
optimizer = keras.optimizers.SGD(0.1)

def train_step(data, labels):
    with tf.GradientTape() as tape:
    data = tf.cast(data, tf.float32)
    pred = model(data)
    loss = keras.losses.binary_crossentropy(labels, pred)

    grads = tape.gradient(loss, model.trainable_variables)

    optimizer.apply_gradients(zip(grads, model.trainable_variables))

    train_loss(loss)
    train_acc(labels, pred)

for i in range(100):
    train_loss.reset_states()
    train_acc.reset_states()
    train_step(xs, ys)

    if i % 10 == 0:
    print(i, train_loss.result().numpy(),train_acc.result().numpy())

  它不能像前面的示例那样收敛，我也不知道为什么
      0 0.7586897 1.0
      10 0.6607897 1.0
      20 0.64341 0.6666667
      30 0.63867164 0.6666667
      40 0.63722247 0.6666667
      50 0.63676286 0.6666667
      60 0.63661444 0.6666667
      70 0.636566 0.6666667
      80 0.63654995 0.6666667
      90 0.6365447 0.6666667


我的代码有什么问题？我应该如何通过tf.GradientTape修改我的训练代码，使其在keras编译/拟合时收敛？谢谢

最佳答案

试试这个，对我有用：

import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import tensorflow as tf
import tensorflow.contrib.eager as tfe
tf.enable_eager_execution()

xs = np.array([[1, 1], [-1, -1], [-1, -1.1]])
ys = np.array([1., 0., 0.])

def get_model():
    inputs = keras.Input(shape=(2,))
    outputs = layers.Dense(1, activation='sigmoid')(inputs)
    return keras.Model(inputs=inputs, outputs=outputs)


# Without Gradient Tape
model = get_model()
model.compile(loss=keras.losses.BinaryCrossentropy(),
          optimizer=keras.optimizers.SGD(0.1),
          metrics=['accuracy'])
model.fit(xs, ys, batch_size=3, epochs=50)

# With Gradient Tape
optimizer = tf.train.GradientDescentOptimizer(0.1)
train_acc = keras.metrics.BinaryAccuracy()
model = get_model()

for i in range(50):
    with tf.GradientTape() as tape:
        xs_ = tf.cast(xs, tf.float32)
        ys_ = tf.cast(ys.reshape(-1,1), tf.float32)

        pred = model(xs_)

        loss = keras.losses.binary_crossentropy(ys_, pred)
        grads = tape.gradient(loss, model.trainable_variables)
        optimizer.apply_gradients(zip(grads, model.trainable_variables))

        print (train_acc(ys_, pred))
        #print (tf.math.reduce_sum(loss))

关于python - 使用tf.GradientTape()训练逻辑回归不能收敛，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/56868981/