ظهور الخطأ ValueError: Negative dimension size في كيراس Keras

قمت ببناء النموذج التالي لتصنيف حالة السائق (مستيقظ/ نائم)، حيث أريد تدريبه على صور أبعادها 100*100، لكن عندما أحاول تدريبه يظهر لي الخطأ التالي:

img = cv2.imread(r"C:\Users\Windows.10\Desktop\Safedrive\safe\train\ClosedFace\closed_eye_0509.jpg_face_1.jpg")
print(img.shape)
# (100, 100, 3)
# dimensions of our images.
img_width, img_height = 100, 100
train_data_dir = r'C:\Users\Windows.10\Desktop\Safedrive\safe\train'
validation_data_dir = r'C:\Users\Windows.10\Desktop\Safedrive\safe\val'
batch_size=16
train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary')
# validation data
val_datagen = ImageDataGenerator(rescale=1. / 255)
validation_generator = val_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary')
print(train_generator.class_indices)
from keras.layers import BatchNormalization
def Model():
    #Instantiation
    model = Sequential()
    #1st Convolutional Layer
    model.add(Conv2D(64, (3, 3), input_shape=(3,100,100)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    #2nd Convolutional Layer
    model.add(Conv2D(64, (3, 3)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    #3rd Convolutional Layer
    model.add(Conv2D(128, (3, 3)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    #Passing it to a Fully Connected layer
    model.add(Flatten())
    # 1st Fully Connected Layer
    model.add(Dense(124))
    model.add(Activation('relu'))
    # Add Dropout to prevent overfitting
    model.add(Dropout(0.5))
    #Output Layer
    model.add(Dense(1))
    model.add(Activation('sigmoid'))
    #Compiling
    model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])
    # View model layers
   #model.summary()
    return model
# Fitting
model=Model()
epochs=2
model.fit_generator(
    train_generator,
    steps_per_epoch=nb_train_samples // batch_size,  
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples // batch_size)
-------------------------------------------------------------------------------------
ValueError: Negative dimension size caused by subtracting 2 from 1 for '{{node max_pooling2d_9/MaxPool}} = MaxPool[T=DT_FLOAT, data_format="NHWC", explicit_paddings=[], ksize=[1, 2, 2, 1], padding="VALID", strides=[1, 2, 2, 1]](Placeholder)' with input shapes: [?,1,98,64].

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