I feed a 514 dimensional real-valued input to a Sequential
model in Keras.
My model is constructed in following way :
predictivemodel = Sequential()
predictivemodel.add(Dense(514, input_dim=514, W_regularizer=WeightRegularizer(l1=0.000001,l2=0.000001), init='normal'))
predictivemodel.add(Dense(257, W_regularizer=WeightRegularizer(l1=0.000001,l2=0.000001), init='normal'))
predictivemodel.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
When I print model.summary()
I get following result:
Layer (type) Output Shape Param # Connected to
================================================================
dense_1 (Dense) (None, 514) 264710 dense_input_1[0][0]
________________________________________________________________
activation_1 (None, 514) 0 dense_1[0][0]
________________________________________________________________
dense_2 (Dense) (None, 257) 132355 activation_1[0][0]
================================================================
Total params: 397065
________________________________________________________________
For the dense_1 layer , number of params is 264710. This is obtained as : 514 (input values) * 514 (neurons in the first layer) + 514 (bias values)
For dense_2 layer, number of params is 132355. This is obtained as : 514 (input values) * 257 (neurons in the second layer) + 257 (bias values for neurons in the second layer)