使用二元交叉熵将电影评论分类为正面或负面

原文标题 ：Classifying movie reviews as positive or negative using binary crossentropy [duplicate]

这个问题在这里已经有了答案：Tensorflow estimator ValueError: logits and labels must have the same shape ((?, 1) vs (?,)) (5个答案) Closed 19 hours ago 。

ValueError: `logits` and `labels` must have the same shape, received ((None, 2) vs (None, 1)).

我使用二元交叉熵将电影评论分类为正面或负面。所以，当我试图用 tensorflow 估计器包装我的 keras 模型时，我得到了错误：

import tensorflow as tf
import tensorflow as tf
genrator=tf.keras.preprocessing.image.ImageDataGenerator()
train=genrator.flow_from_directory('data/',class_mode='binary',
                                  batch_size=30,target_size=(128,128))
test=genrator.flow_from_directory('test_data/',class_mode='binary',
                                  batch_size=30,target_size=(128,128))
import os
from PIL import Image
folder_path = 'data/'
extensions = []
for fldr in os.listdir(folder_path):
    sub_folder_path = os.path.join(folder_path, fldr)
    for filee in os.listdir(sub_folder_path):
        file_path = os.path.join(sub_folder_path, filee)
        print('** Path: {}  **'.format(file_path), end="\r", flush=True)
        im = Image.open(file_path)
        rgb_im = im.convert('RGB')
        if filee.split('.')[1] not in extensions:
            extensions.append(filee.split('.')[1])
model=tf.keras.models.Sequential([
#     tf.keras.layers.ZeroPadding2D((1,1),input_shape=(128,128,1)),
    tf.keras.layers.Conv2D(32,(3,3),activation='ELU',input_shape=(128,128,1)),
    tf.keras.layers.MaxPool2D((2,2)),
    tf.keras.layers.Conv2D(64,(3,3),activation='relu'),
    tf.keras.layers.MaxPool2D((2,2)),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128,activation='relu'),
    tf.keras.layers.Dense(256,activation='relu'),
    tf.keras.layers.Dense(2,activation='sigmoid'),
])
model.compile(optimizer=tf.keras.optimizers.Adam(),
             loss='binary_crossentropy',metrics=['accuracy'])
model.fit_generator(train, steps_per_epoch=len(train), validation_data=test, validation_steps=len(test), epochs=10)