I have a simple NN model for detecting hand-written digits from a 28x28px image written in python using Keras:
model0 = Sequential()
#number of epochs to train for
nb_epoch = 12
#amount of data each iteration in an epoch sees
batch_size = 128
model0.add(Flatten(input_shape=(1, img_rows, img_cols))) model0.add(Dense(nb_classes))
model0.add(Activation('softmax'))
model0.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=1, validation_data=(X_test, Y_test))
score = model0.evaluate(X_test, Y_test, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])
This runs well and I get ~90% accuracy. I then perform the following command to get a summary of my network's structure by doing print(model0.summary()). This outputs the following:
Layer (type) Output Shape Param # Connected to ===================================================================== flatten_1 (Flatten) (None, 784) 0 flatten_input_1[0][0]
dense_1 (Dense) (None, 10) 7850 flatten_1[0][0]
activation_1 (None, 10) 0 dense_1[0][0] ====================================================================== Total params: 7850
I don't understand how they get to 7850 total params and what that actually means?