# this trains a dense neural network on MNIST

program.options_from_args()
sfix.set_precision_from_args(program)

import torchvision

data = []
for train in True, False:
    ds = torchvision.datasets.MNIST(root='/tmp', train=train, download=True)
    # normalize to [0,1] before input
    samples = sfix.input_tensor_via(0, ds.data / 255)
    labels = sint.input_tensor_via(0, ds.targets, one_hot=True)
    data += [(labels, samples)]

(training_labels, training_samples), (test_labels, test_samples) = data

import torch
import torch.nn as nn

net = nn.Sequential(
    nn.Flatten(),
    nn.Linear(28 * 28, 128),
    nn.ReLU(),
    nn.Linear(128, 128),
    nn.ReLU(),
    nn.Linear(128, 10)
)

# test network
ds = torchvision.datasets.MNIST(
    root='/tmp', transform=torchvision.transforms.ToTensor())
inputs = next(iter(torch.utils.data.DataLoader(ds)))[0]
print(inputs.shape)
outputs = net(inputs)

from Compiler import ml

ml.set_n_threads(int(program.args[2]))

layers = ml.layers_from_torch(net, training_samples.shape, 128)

optimizer = ml.SGD(layers)
optimizer.fit(
    training_samples,
    training_labels,
    epochs=int(program.args[1]),
    batch_size=128,
    validation_data=(test_samples, test_labels),
    program=program
)

# store secret model for use in torch_mnist_dense_test
for var in optimizer.trainable_variables:
    var.write_to_file()

# output to be used in Scripts/torch_mnist_lenet_import.py
optimizer.reveal_model_to_binary()
