How does model.weights in tensorflow/keras work? - tensorflow

Question

I have a model trained.
summary is as follows
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 256) 2560
dense_1 (Dense) (None, 128) 32896
dropout (Dropout) (None, 128) 0
dense_2 (Dense) (None, 1) 129
=================================================================
Total params: 35,585
Trainable params: 35,585
Non-trainable params: 0
_________________________________________________________________
And have weights
for i,weight in enumerate(Model.weights):
exec('w{}=np.array(weight)'.format(i))
have test data for predict
x=test_data.iloc[0]
then I predict with model
Model.predict(np.array(x).reshape(1,9))
get array([[226241.66]], dtype=float32)
then I predict with weights
((x#w0+w1)#w2+w3)#w4+w5
get array([98039.99664026])
Can someone explain how the weights in model works?
And how to get the model-predict result with weights?

Answer 1

Try Model.layers which will return a list of all layers in your model, each layer has a function get_weights() which will return the weights as numpy arrays. I was able to reproduce the output of a simple 3 layer feed-forward model with this approach.
for i,layer in enumerate(model.layers):
exec('w{}=np.array(layer.get_weights()[0])'.format(i)) # weight
exec('b{}=np.array(layer.get_weights()[1])'.format(i)) # bias
X = np.random.randn(1,9)
np.allclose(((X#w1[0] + b1[1])#w2[0] + b2[1])#w4[0] + b4[1], model.predict(X)) # True
Note: In my examle layer 0 was a input layer (no weights) and layer 3 a dropout layer (no weights). When calling model.predict(), dropout is not applied, therefore you can ignore it in this case.