检查目标时出错:预期density_1的形状为(1，)，但数组的形状为(256，)

问题描述

我正在尝试学习tensorflow，并且正在遵循一个演示教程( https://www.tensorflow.org/tutorials/keras/basic_text_classification )

I am trying to learn tensorflow, and I was following a demo tutorial (https://www.tensorflow.org/tutorials/keras/basic_text_classification)

错误报告告诉我

检查目标时出错:预期density_1的形状为(1，)，但数组的形状为(256，)"

"Error when checking target: expected dense_1 to have shape (1,) but got array with shape (256,)"

有人可以向我解释为什么这行不通吗?

Can someone explain to me why this won't work?

train_data = keras.preprocessing.sequence.pad_sequences(train_data,
                                                    value=word_index["<PAD>"],
                                                    padding='post',
                                                    maxlen=256) #max length
test_data = keras.preprocessing.sequence.pad_sequences(test_data,
                                                   value=word_index["<PAD>"],
                                                   padding='post',
                                                   maxlen=256)
vocal_size = 10000
model = keras.Sequential()
model.add(keras.layers.Embedding(vocal_size,16))
model.add(keras.layers.GlobalAveragePooling1D())
model.add(keras.layers.Dense(16,activation=tf.nn.relu))
model.add(keras.layers.Dense(1,activation=tf.nn.sigmoid))

model.compile(optimizer=tf.train.AdamOptimizer(),
          loss='binary_crossentropy',
          metrics=['accuracy'])


x_val = train_data[:10000]
partial_x_train = train_data[10000:]
y_val = train_data[:10000]
partial_y_train = train_data[10000:]


history = model.fit(partial_x_train, partial_y_train, epochs=40, batch_size=512, validation_data=(x_val, y_val), verbose=1)

推荐答案

这些行中有错误

y_val = train_data[:10000]
partial_y_train = train_data[10000:]

但是本教程说应该是

y_val = train_labels[:10000]
partial_y_train = train_labels[10000:]

train_data代表每个书面评论，而train_labels代表评论是正面还是负面.您希望模型在书面评论为正面或负面时学习.

train_data represents each written review, and train_labels represents whether the reviews are positive or negative. You want your model to learn when a written review is positive or negative.

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