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我正在尝试使用 Keras 对 IMDB 电影评论进行二进制分类。以下是我使用的代码。

from keras import models
from keras import layers

model = models.Sequential()
model.add(layers.Dense(16,activation="relu",input_shape=(10000,)))
model.add(layers.Dense(16,activation="relu"))
model.add(layers.Dense(1,activation="sigmoid"))

model.compile(optimizer="rmsprop",loss="binary_crossentropy", metrics=["acc"])

history = model.fit(partial_x_train,partial_y_train, epochs=20, batch_size=512, validation_data = (x_val, y_val))

每个输入张量的形状如下。

print(partial_x_train.shape) --> (15000, 10000)
print(partial_y_train.shape)--> (15000, 10000)
print(x_val.shape) --> (10000, 10000)
print(y_val.shape) --> (10000, 10000)

但是在执行上述程序时，我收到以下错误。

ValueError: in user code:
ValueError: logits and labels must have the same shape ((None, 1) vs (None, 10000))

我搜索了很多 SO 问题，但无法理解我做错了什么。有人可以帮我避免这个错误并编译模型吗？

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1 回答 1

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如前所述ValueError，您正在尝试计算 shape((None, 1)与(None, 10000)). 如果您发布或参考 IMDB 的训练集，那将很清楚。尝试使用来自keras.

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

max_features = 20000  # Only consider the top 20k words
maxlen = 200  # Only consider the first 200 words of each movie review

(x_train, y_train), (x_val, y_val) = keras.datasets.imdb.load_data(
    num_words=max_features
)

print(len(x_train), "Training sequences")
print(len(x_val), "Validation sequences")

x_train = keras.preprocessing.sequence.pad_sequences(x_train, maxlen=maxlen)
x_val = keras.preprocessing.sequence.pad_sequences(x_val, maxlen=maxlen)

x_train.shape, y_train.shape
# ((25000, 200), (25000,))

根据你的型号

model = models.Sequential()
model.add(layers.Dense(16,activation="relu",input_shape=(maxlen,)))
model.add(layers.Dense(16,activation="relu"))
model.add(layers.Dense(1,activation="sigmoid"))

model.compile(optimizer="rmsprop",loss="binary_crossentropy", metrics=["acc"])
model.fit(x_train, y_train, batch_size=32, epochs=2, validation_data=(x_val, y_val))

Epoch 1/2
782/782 [==============================] - 5s 4ms/step - loss: 164.2350 - acc: 0.5018 - val_loss: 1.0527 - val_acc: 0.5000
Epoch 2/2
782/782 [==============================] - 3s 4ms/step - loss: 1.0677 - acc: 0.4978 - val_loss: 0.7446 - val_acc: 0.5000
于 2021-02-18T16:06:07.423 回答