update

mxnet/ex06/mxnet101.py

@@ -0,0 +1,73 @@
+import mxnet as mx
+from mxnet import nd, autograd
+import matplotlib.pyplot as plt
+
+mx.random.seed(1)
+
+num_inputs = 2
+num_outputs = 1
+num_examples = 10000
+
+X = nd.random_normal(shape=(num_examples, num_inputs))
+y = 2 * X[:, 0] - 3.4 * X[:, 1] + 4.2 + 0.1 * \
+    nd.random_normal(shape=(num_examples,))
+
+print(X[0])
+
+batch_size = 4
+train_data = mx.gluon.data.DataLoader(mx.gluon.data.ArrayDataset(X, y),
+                                      batch_size=batch_size, shuffle=True)
+
+w = nd.random_normal(shape=(num_inputs, num_outputs))
+b = nd.random_normal(shape=num_outputs)
+params = [w, b]
+
+for param in params:
+    param.attach_grad()
+
+
+def net(X):
+    """Net"""
+    return mx.nd.dot(X, w) + b
+
+
+def square_loss(yhat, y):
+    """Loss function"""
+    return nd.mean((yhat - y) ** 2)
+
+
+def SGD(params, lr):
+    """SGD"""
+    for param in params:
+        param[:] = param - lr * param.grad
+
+
+epochs = 2
+ctx = mx.cpu()
+learning_rate = 0.001
+smoothing_constant = 0.01
+
+for e in range(epochs):
+    for i, (data, label) in enumerate(train_data):
+        data = data.as_in_context(ctx)
+        label = label.as_in_context(ctx).reshape((-1, 1))
+        with autograd.record():
+            output = net(data)
+            loss = square_loss(output, label)
+        loss.backward()
+        SGD(params, learning_rate)
+        curr_loss = nd.mean(loss).asscalar()
+        moving_loss = (curr_loss if ((i == 0) and (e == 0))
+                       else(1 - smoothing_constant) * moving_loss +
+                       (smoothing_constant) * curr_loss)
+        if (i + 1) % 500 == 0:
+            print("Epoch %s, batch %s. Moving avg of loos: %s" %
+                  (e, i, moving_loss))
+
+# %%
+pred = mx.nd.dot(X, params[0]) + params[1]
+# %%
+plt.scatter(pred.asnumpy(), y.asnumpy())
+plt.xlabel('predict')
+plt.ylabel('real')
+plt.show()