Prepending Downsample layer to Resnet50 Pretrained Model - keras

Question

I am using keras 1.1.1 in windows 7 with tensorflow backend.
I am trying to prepend the stock Resnet50 pretained model with an image downsampler. Below is my code.
from keras.applications.resnet50 import ResNet50
import keras.layers
# this could also be the output a different Keras model or layer
input = keras.layers.Input(shape=(400, 400, 1)) # this assumes K.image_dim_ordering() == 'tf'
x1 = keras.layers.AveragePooling2D(pool_size=(2,2))(input)
x2 = keras.layers.Flatten()(x1)
x3 = keras.layers.RepeatVector(3)(x2)
x4 = keras.layers.Reshape((200, 200, 3))(x3)
x5 = keras.layers.ZeroPadding2D(padding=(12,12))(x4)
m = keras.models.Model(input, x5)
model = ResNet50(input_tensor=m.output, weights='imagenet', include_top=False)
but I get an error which I am unsure how to fix.
builtins.Exception: Graph disconnected: cannot obtain value for tensor
Output("input_2:0", shape=(?, 400, 400, 1), dtype=float32) at layer
"input_2". The following previous layers were accessed without issue:
[]

Answer 1

You can use both the Functional API and Sequential approaches to solve this. See working example for both approaches below:
from keras.applications.ResNet50 import ResNet50
from keras.models import Sequential, Model
from keras.layers import AveragePooling2D, Flatten, RepeatVector, Reshape, ZeroPadding2D, Input, Dense
pretrained = ResNet50(input_shape=(224, 224, 3), weights='imagenet', include_top=False)
# Sequential method
model_1 = Sequential()
model_1.add(AveragePooling2D(pool_size=(2,2),input_shape=(400, 400, 1)))
model_1.add(Flatten())
model_1.add(RepeatVector(3))
model_1.add(Reshape((200, 200, 3)))
model_1.add(ZeroPadding2D(padding=(12,12)))
model_1.add(pretrained)
model_1.add(Dense(1))
# functional API method
input = Input(shape=(400, 400, 1))
x = AveragePooling2D(pool_size=(2,2),input_shape=(400, 400, 1))(input)
x = Flatten()(x)
x = RepeatVector(3)(x)
x = Reshape((200, 200, 3))(x)
x = ZeroPadding2D(padding=(12,12))(x)
x = pretrained(x)
preds = Dense(1)(x)
model_2 = Model(input,preds)
model_1.summary()
model_2.summary()
The summaries (replace resnet for xception):
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
average_pooling2d_1 (Average (None, 200, 200, 1) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 40000) 0
_________________________________________________________________
repeat_vector_1 (RepeatVecto (None, 3, 40000) 0
_________________________________________________________________
reshape_1 (Reshape) (None, 200, 200, 3) 0
_________________________________________________________________
zero_padding2d_1 (ZeroPaddin (None, 224, 224, 3) 0
_________________________________________________________________
xception (Model) (None, 7, 7, 2048) 20861480
_________________________________________________________________
dense_1 (Dense) (None, 7, 7, 1) 2049
=================================================================
Total params: 20,863,529
Trainable params: 20,809,001
Non-trainable params: 54,528
_________________________________________________________________
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_2 (InputLayer) (None, 400, 400, 1) 0
_________________________________________________________________
average_pooling2d_2 (Average (None, 200, 200, 1) 0
_________________________________________________________________
flatten_2 (Flatten) (None, 40000) 0
_________________________________________________________________
repeat_vector_2 (RepeatVecto (None, 3, 40000) 0
_________________________________________________________________
reshape_2 (Reshape) (None, 200, 200, 3) 0
_________________________________________________________________
zero_padding2d_2 (ZeroPaddin (None, 224, 224, 3) 0
_________________________________________________________________
xception (Model) (None, 7, 7, 2048) 20861480
_________________________________________________________________
dense_2 (Dense) (None, 7, 7, 1) 2049
=================================================================
Total params: 20,863,529
Trainable params: 20,809,001
Non-trainable params: 54,528
_________________________________________________________________
Both approaches work fine. If you plan on freezing the pretrained model and letting pre/post layers learn -- and afterward finetuning the model, the approach I found to work goes like so:
# given the same resnet model as before...
model = load_model('modelname.h5')
# pull out the nested model
nested_model = model.layers[5] # assuming the model is the 5th layer
# loop over the nested model to allow training
for l in nested_model.layers:
l.trainable=True
# insert the trainable pretrained model back into the original
model.layer[5] = nested_model