Gluon is a standard implemented by MXNet and which would unify the API of diverse frameworks. It has been superseded by ONNX.
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from mxnet import gluon import mxnet as mx import numpy as np x_input = mx.nd.empty((1, 5), mx.cpu()) x_input[:] = np.array([[1,2,3,4,5]], np.float32) y_input = mx.nd.empty((1, 5), mx.cpu()) y_input[:] = np.array([[10, 15, 20, 22.5, 25]], np.float32) net = gluon.nn.Sequential() with net.name_scope(): net.add(gluon.nn.Dense(16, activation="relu")) net.add(gluon.nn.Dense(len(y_input))) net.collect_params().initialize(mx.init.Normal(), ctx=mx.cpu()) softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss() trainer = gluon.Trainer(net.collect_params(), 'adam', {'learning_rate': .1}) n_epochs = 10 for e in range(n_epochs): for i in range(len(x_input)): input = x_input[i] target = y_input[i] with mx.autograd.record(): output = net(input) loss = softmax_cross_entropy(output, target) loss.backward() trainer.step(input.shape[0]) |
