问Keras与Convolution2D不兼容

EN

import keras
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import Flatten, Dense, Dropout
img_width, img_height = 28, 28
mnist = keras.datasets.mnist
(X_train, Y_train), (X_test, Y_test) = mnist.load_data()
(X_train, y_train), (X_test, y_test) = mnist.load_data()

X_train = keras.utils.normalize(X_train, axis=1) #Normalizes from 0-1 (originally each pixel is valued 0-255)
X_test = keras.utils.normalize(X_test, axis=1) #Normalizes from 0-1 (originally each pixel is valued 0-255)
Y_train = keras.utils.to_categorical(Y_train) #Reshapes to allow ytrain to work with x train
Y_test = keras.utils.to_categorical(Y_test)

from sklearn import preprocessing
lb = preprocessing.LabelBinarizer()
Y_train = lb.fit_transform(Y_train)
Y_test = lb.fit_transform(Y_test)

#Model
model = Sequential()
model.add(Flatten())
model.add(Convolution2D(16, 5, 5, activation='relu', input_shape=(1,img_width, img_height, 1)))
model.add(Dense(128, activation='relu'))
model.add(Dense(128, activation='relu'))
model.add(Dropout(.2))
model.add(Dense(64, activation='relu'))
model.add(Dense(10, activation='softmax'))

model.compile(optimizer = 'adam',
            loss='categorical_crossentropy',
            metrics=['accuracy'])
model.fit(X_train, Y_train, epochs=3, verbose=2)

val_loss, val_acc = model.evaluate(X_test, Y_test) #Check to see if model fits test
print(val_loss, val_acc)