I would like to extract weights of 1d CNN layer, and understand how exactly the prediction values are computed. I am not able to re-produce the prediction values using the weights from get_weights() function.
In order to explain my understanding, here is a small data set.
n_filter = 64
kernel_size = 10
len_timeseries = 123
n_feature = 3
X = np.random.random(sample_size*len_timeseries*n_feature).reshape(sample_size,len_timeseries,n_feature)
y = np.random.random(sample_size*(len_timeseries-kernel_size+1)*n_filter).reshape(sample_size,
(len_timeseries-kernel_size+1),
n_filter)
Now, create a simple 1d CNN model as:
model = Sequential()
model.add(Conv1D(n_filter,kernel_size,
input_shape=(len_timeseries,n_feature)))
model.compile(loss="mse",optimizer="adam")
Fit the model and predict the values of X as:
model.fit(X,y,nb_epoch=1)
y_pred = model.predict(X)
The dimension of y_pred is (1000, 114, 64) as it should.
Now, I want to reproduce the value of y_pred[irow,0,ilayer]] using weights stored in model.layer. As there is only single layer, len(model.layer)=1. So I extract the weights from the first and the only layer as:
weight = model.layers[0].get_weights()
print(len(weight))
> 2
weight0 = np.array(weight[0])
print(weight0.shape)
> (10, 1, 3, 64)
weight1 = np.array(weight[1])
print(weight1.shape)
> (64,)
The weight has length 2 and I assume that the 0th position contain the weights for features and the 1st position contain the bias.
As the weight0.shape=(kernel_size,1,n_feature,n_filter), I thought that I can obtain the values of y_pred[irow,0,ilayer] by:
ifilter = 0
irow = 0
y_pred_by_hand = weight1[ifilter] + np.sum( weight0[:,0,:,ifilter] * X[irow,:kernel_size,:])
y_pred_by_hand
> 0.5124888777
However, this value is quite different from y_pred[irow,0,ifilter] as:
y_pred[irow,0,ifilter]
>0.408206
Please let me know where I got wrong.