def train(network, x, y, epochs=3):
"""Train network for given number of epochs"""
for epoch in range(epochs):
print('epoch', epoch + 1)
losses = (
zip(x, y) >> nf.PrintProgress(x) >> vec2img >> augment >>
nf.Shuffle(1000) >> build_batch >> network.train() >> nf.Collect())
print('train loss:', np.mean(losses))
def train(network, epochs=3):
"""Train network for given number of epochs"""
print('loading data...')
filepath = download_mnist()
x_train, y_train, x_test, y_test = load_mnist(filepath)
plot = nm.PlotLines(None, every_sec=0.2)
build_batch = (nm.BuildBatch(128, verbose=False).input(
0, 'vector', 'float32').output(1, 'number', 'int64'))
for epoch in range(epochs):
print('epoch', epoch + 1)
losses = (zip(x_train,
y_train) >> nf.PrintProgress(x_train) >> nf.Shuffle(1000)
>> build_batch >> network.train() >> plot >> nf.Collect())
acc_test = evaluate(network, x_test, y_test)
acc_train = evaluate(network, x_train, y_train)
print('train loss : {:.6f}'.format(np.mean(losses)))
print('train acc : {:.1f}'.format(acc_train))
print('test acc : {:.1f}'.format(acc_test))
def predict(network, x, y):
"""Compute network outputs and print accuracy"""
preds = (zip(x, y) >> nf.PrintProgress(x) >> vec2img >> build_pred_batch >>
network.predict() >> nf.Collect())
acc = accuracy(y, preds)
print('test acc', acc)
def validate(network, x, y):
"""Compute validation/test loss (= mean over batch losses)"""
losses = (zip(x, y) >> nf.PrintProgress(x) >> vec2img >> build_batch >>
network.validate() >> nf.Collect())
print('val loss:', np.mean(losses))
def evaluate(network, x, y):
"""Evaluate network performance (here accuracy)"""
metrics = [accuracy]
result = (zip(x, y) >> nf.PrintProgress(x) >> vec2img >> build_batch >>
network.evaluate(metrics))
return result