Type error when trying to fit a VGG like model

When I try to fit the following model:

model = Sequential([
    Lambda(vgg_preprocess, input_shape=(3,244,244)),
    Conv2D(64,3,3, activation='relu'),
    BatchNormalization(axis=1),
    Conv2D(64,3,3, activation='relu'),
    MaxPooling2D(),
    BatchNormalization(axis=1),
    Conv2D(128,3,3, activation='relu'),
    BatchNormalization(axis=1),
    Conv2D(128,3,3, activation='relu'),
    MaxPooling2D(),
    BatchNormalization(axis=1),
    Conv2D(256,3,3, activation='relu'),
    BatchNormalization(axis=1),
    Conv2D(256,3,3, activation='relu'),
    MaxPooling2D(),
    Flatten(),
    BatchNormalization(),
    Dense(1024, activation='relu'),
    BatchNormalization(),
    Dropout(0.5),
    Dense(1024, activation='relu'),
    BatchNormalization(),
    Dense(10, activation='softmax')
])
model.compile(Adam(), loss='categorical_crossentropy', metrics=['accuracy'])

I get this error:

TypeError: Cannot convert Type TensorType(float32, 4D) (of Variable AbstractConv2d_gradInputs{convdim=2, border_mode='valid', subsample=(1, 1), filter_flip=True, imshp=(None, 256, 56, 56), kshp=(256, 256, 3, 3), filter_dilation=(1, 1)}.0) into Type TensorType(float64, 4D). You can try to manually convert AbstractConv2d_gradInputs{convdim=2, border_mode='valid', subsample=(1, 1), filter_flip=True, imshp=(None, 256, 56, 56), kshp=(256, 256, 3, 3), filter_dilation=(1, 1)}.0 into a TensorType(float64, 4D).

This is how I do the fitting:

 model.fit_generator(train_batches, train_batches.n, nb_epoch=1, validation_data=test_batches, nb_val_samples=test_batches.n)

And here is vgg_preprocess function:

vgg_mean = np.array([123.68, 116.779, 103.939]).reshape((3,1,1))

def vgg_preprocess(x):
    x = x - vgg_mean  #Subtract the mean of each channel
    return x[:, ::-1] #Inverse the channel order to suit that of VGG RGB->BGR

What does it mean, how to fix it?