mxnet

import mxnet as mx from mxnet import gluon, nd from mxnet.gluon import nn # Define a simple neural network class MLP(nn.Block): def __init__(self, **kwargs): super(MLP, self).__init__(**kwargs) self.dense0 = nn.Dense(128, activation='relu') self.dense1 = nn.Dense(64, activation='relu') self.dense2 = nn.Dense(10) def forward(self, x): x = self.dense0(x) x = self.dense1(x) x = self.dense2(x) return x # Create an instance of the network net = MLP() # Initialize parameters ctx = mx.cpu(0) # Or mx.gpu(0) if GPU is available and MXNet-GPU is installed net.initialize(mx.init.Xavier(), ctx=ctx) # Create a dummy input (e.g., for a batch of 1 with 784 features) dummy_input = nd.random.uniform(shape=(1, 784), ctx=ctx) # Perform a forward pass output = net(dummy_input) print(f"Network output shape: {output.shape}") # Simple tensor operation a = nd.ones((2, 3), ctx=ctx) b = a * 2 print(f"Simple NDArray operation result: {b.asnumpy()}")