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Till now in MLP

from keras.models import Sequential
from keras.layers import Flatten, Dense

model = Sequential()
# Imports a Flatten layer to convert the image matrix into a vector
# Defines the neural network architecture
model.add( Flatten(input_shape = (28,28) ))
model.add(Dense(512, activation = 'relu'))
model.add(Dense(512, activation = 'relu'))
model.add(Dense(10, activation = 'softmax'))
model.summary()
  • Params after the flatten layer = 0, because this layer only flattens the image to a vector for feeding into the input layer. The weights haven’t been added yet.
  • Params after layer 1 = (784 nodes in input layer) × (512 in hidden layer 1) +
    (512 connections to biases) = 401,920.
  • Params after layer 2 = (512 nodes in hidden layer 1) × (512 in hidden layer 2) + (512 connections to biases) = 262,656.
  • Params after layer 3= (512 nodes in hidden layer 2) × (10 in output layer) + (10 connections to biases) = 5,130.
  • Total params in the network = 401,920 + 262,656 + 5,130 = 669,706.

Why Convolutions?

SPATIAL INVARIANCE or LOSS IN FEATURES

The spatial features of a 2D image are lost when it is flattened to a 1D vector input. Before feeding an image to the hidden layers of an MLP, we must flatten the image matrix to a 1D vector, as we saw in the mini project. This implies that all of the image's 2D information is discarded.