Python Keras中的二维卷积误差

我知道这类问题在这里被问过很多次，但我无法从这些问题中找出答案。我有一张100x100的灰度图像。我在第一层尝试执行2D卷积时遇到以下错误

    import theano
    from keras.layers import Activation, Flatten, Dense
    from keras.layers import Convolution2D,MaxPooling2D
    from keras.models import Sequential

    nb_epoch = 40
    batch_size = 32
    nb_classes = 2
    model = Sequential()
    model.add(Convolution2D(32,3,3,border_mode = 'valid',subsample = (1,1),init = 'glorot_uniform',input_shape = (1,100,100)))
    model.add(Activation('relu'))

    train_datagen = ImageDataGenerator(
    rescale=1./255,
    rotation_range = 300,
    horizontal_flip=True,
    vertical_flip = True)

    test_datagen = ImageDataGenerator(rescale=1./255)

    train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=16,
    class_mode='binary')

    validation_generator = test_datagen.flow_from_directory(
    test_data_dir,
    target_size=(img_width, img_height),
    batch_size=16,
    class_mode='binary')

    model.fit_generator(
    train_generator,
    samples_per_epoch=nb_train_samples,
    nb_epoch=nb_epoch,
    validation_data=validation_generator,
    nb_val_samples=nb_validation_samples) 

我得到一个类似这样的错误：检查模型输入时出错：预期卷积2D_输入_1具有形状（None，1100，100），但得到具有形状（32，31000，100）的数组。我不确定我会错在哪里

试试看：

 train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=16,
    color_mode='grayscale',
    class_mode='binary')

    validation_generator = test_datagen.flow_from_directory(
    test_data_dir,
    target_size=(img_width, img_height),
    batch_size=16,
    color_mode='grayscale
    class_mode='binary')