代做program、c/c++，Java程序设计代写

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Your job is to use backpropagation with adam to train a network to
recognize the handwritten digits in the MNIST data set. The network
should be a 4-layer mapnet where the two middle layers are 28-by-28
maps with field-fractions of 0.25. Please construct your network so
that each layer projects to the next one (as in create_net.m), but layer 2
also projects directly to layer 4 through weight matrix W{4} — so
W{4} is a 10-by- 1568 matrix with input [y{2}; y{3}]. And please
make the connections from both middle layers to the output layer
dense (that is, fully connected, not maplike).

In the two middle layers, use the following activation function, which
we’ll call relog:

y{l}i = max( 0, sgn(v{l}i) log( 1 + |v{l}i | ) ).

Make the final layer of your network affine, but then apply a softmax
function to the network’s output signal:

yi = exp(y{4}i) / Σj exp(y{4}j).

From the resulting y vector and the one-hot label vector y*, compute
the error and loss as e = y – y* and L = eTe/2.

To make your mapnet,write an m-file called init_surname_create_
mapnet, analogous to the posted m-file create_net. All the vectors and
matrices associated with your mapnet should be initialized inside init_
surname_create_mapnet. In particular, that m-file should set all biases
b to 0, and should initialize each neuron’s weight by drawing it from a
normal distribution with a mean of 0 and standard deviation of
sqrt(2/N), where Nis the number of the neuron’s inputs, i.e. the num-
ber of upstream neurons projecting to it directly.

For learning, write m-files init_surname_forward_relog and init_sur-
name_backprop_relog, analogous to the posted m-files forward_relu
and backprop_relu but modified to use therelog function instead of
relu and to take into account the direct projection from layer 2 to layer
4. Please compute the softmax outside your forward_relog function, so
your code calls your forward_relog and then applies softmax to its
output. And compute the derivative ∂L/∂v{4} outside your backprop_
relog function and use that derivative as the input to backprop_relog.
Implement adam using the posted m-file adam.m, and set the adam η =
0.001 and the other adam hyperparameters to their usual values.

You’ll find the MNIST data set in the file MNIST.mat, along with
MNIST_key.m, which explains the format of those data. Load the data
set as in MNIST_key.m. Feed the images to the network in minibatch-
es of 100 each. If you can avoid it, don’t write any code, anywhere in
your files, that uses a loop or list to go through the minibatch example
by example; instead, for concision and speed, write your code so that
all operations are done on the whole minibatch array at once, as in the
posted sample code.

Run 10 epochs — that is, 10 passes through all 60,000 training exam-
ples — with 600 minibatches in each epoch. At the start of each epoch,
shuffle the data, as in MNIST_key.m (this shuffling can aid learning,
as it supplies a different series of 600 minibatches to the network in
different epochs).

After each epoch, run your network on the entire test set (in one big
“minibatch” of all 10,000 test examples), count the number of incor-
rect guesses, and display that number in the Command Window. If all
goes well, then on most runs, the number of incorrect guesses should
be 400 or better after one epoch, and should fall below 160 at least
once in the first 10 epochs.

Submit an m-file called INIT_SURNAME_A2.m, along with init_
surname_create_mapnet.m, init_surname_forward_relog.m, and init_
surname_backprop_relog.m.