我正在处理一个二元分类问题。起初准确率为69%，但由于内存不足，我缩小了一些参数，现在准确率变为0。你们知道这是怎么回事吗？

model = Sequential()from keras.layers import Dropoutmodel.add(Conv2D(96, kernel_size=11, padding="same", input_shape=(300, 300, 1), activation = 'relu'))model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2)))model.add(Conv2D(128, kernel_size=3, padding="same", activation = 'relu'))model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))model.add(Conv2D(128, kernel_size=3, padding="same", activation = 'relu'))model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))from keras.layers.core import Activationmodel.add(Flatten())# model.add(Dense(units=1000, activation='relu'  ))model.add(Dense(units= 300, activation='relu'))model.add(Dropout(0.2))model.add(Dense(1))model.add(Activation("softmax"))model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])from keras.preprocessing.image import ImageDataGeneratordatagen = ImageDataGenerator(    featurewise_center=True,    rotation_range=90,    fill_mode='nearest',    validation_split = 0.2    )datagen.fit(train)train_generator = datagen.flow(train, train_labels, batch_size=8)# # fits the model on batches with real-time data augmentation:history = model.fit_generator(generator=train_generator,                    use_multiprocessing=True,                    steps_per_epoch = len(train_generator) / 8,                    epochs = 5,                    workers=20)

回答：

Softmax函数只应在多类分类问题中使用。你从Dense层只有一个输出，因此应该使用sigmoid函数。