TensorFlow: import tensorflow as tf
from d2l import tensorflow as d2l
tf.random.set_seed(seed=1322)
n_train = 50
x_train = tf.sort(tf.random.uniform(shape=(n_train,), maxval=5))
def f(x):
    return 2 * tf.sin(x) + x**0.8
y_train = f(x_train) + tf.random.normal((n_train,), 0.0, 0.5)
x_test = tf.range(0, 5, 0.1)
y_truth = f(x_test)
n_test = len(x_test)
n_test
y_hat = tf.repeat(tf.reduce_mean(y_train), repeats=n_test)
plot_kernel_reg(y_hat)
X_repeat = tf.repeat(tf.expand_dims(x_train, axis=0), repeats=n_train, axis=0)
attention_weights = tf.nn.softmax(-(X_repeat - tf.expand_dims(x_train, axis=1))**2/2, axis=1)
y_hat = tf.matmul(attention_weights, tf.expand_dims(y_train, axis=1))
plot_kernel_reg(y_hat)
d2l.show_heatmaps(tf.expand_dims(tf.expand_dims(attention_weights, axis=0), axis=0), xlabel='Sorted training inputs', ylabel='Sorted testing inputs')
X = tf.ones((2, 1, 4))
Y = tf.ones((2, 4, 6))
tf.matmul(X, Y).shape
weights = tf.ones((2, 10)) * 0.1
values = tf.reshape(tf.range(20.0), shape = (2, 10))
tf.matmul(tf.expand_dims(weights, axis=1), tf.expand_dims(values, axis=-1)).numpy()
class NWKernelRegression(tf.keras.layers.Layer):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.w = tf.Variable(initial_value=tf.random.uniform(shape=(1,)))
    def call(self, queries, keys, values, **kwargs):
        queries = tf.repeat(tf.expand_dims(queries, axis=1), repeats=keys.shape[1], axis=1)
        self.attention_weights = tf.nn.softmax(-((queries - keys) * self.w)**2 /2, axis =1)
        return tf.squeeze(tf.matmul(tf.expand_dims(self.attention_weights, axis=1), tf.expand_dims(values, axis=-1)))
X_tile = tf.repeat(tf.expand_dims(x_train, axis=0), repeats=n_train, axis=0)
Y_tile = tf.repeat(tf.expand_dims(y_train, axis=0), repeats=n_train, axis=0)
keys = tf.reshape(X_tile[tf.cast(1 - tf.eye(n_train), dtype=tf.bool)], shape=(n_train, -1))
values = tf.reshape(Y_tile[tf.cast(1 - tf.eye(n_train), dtype=tf.bool)], shape=(n_train, -1))
net = NWKernelRegression()
loss_object = tf.keras.losses.MeanSquaredError()
optimizer = tf.keras.optimizers.SGD(learning_rate=0.5)
animator = d2l.Animator(xlabel='epoch', ylabel='loss', xlim=[1, 5])
for epoch in range(5):
    with tf.GradientTape() as t:
        loss = loss_object(y_train, net(x_train, keys, values)) * len(y_train)
    grads = t.gradient(loss, net.trainable_variables)
    optimizer.apply_gradients(zip(grads, net.trainable_variables))
    animator.add(epoch + 1, float(loss))
keys = tf.repeat(tf.expand_dims(x_train, axis=0), repeats=n_test, axis=0)
values = tf.repeat(tf.expand_dims(y_train, axis=0), repeats=n_test, axis=0)
y_hat = net(x_test, keys, values)
plot_kernel_reg(y_hat)
d2l.show_heatmaps(tf.expand_dims(tf.expand_dims(net.attention_weights, axis=0), axis=0), xlabel='Sorted training inputs', ylabel='Sorted testing inputs')

PyTorch: import torch
from torch import nn
from d2l import torch as d2l
n_train = 50
x_train, _ = torch.sort(torch.rand(n_train) * 5)
def f(x):
    return 2 * torch.sin(x) + x**0.8
y_train = f(x_train) + torch.normal(0.0, 0.5, (n_train,))
x_test = torch.arange(0, 5, 0.1)
y_truth = f(x_test)
n_test = len(x_test)
n_test
y_hat = torch.repeat_interleave(y_train.mean(), n_test)
plot_kernel_reg(y_hat)
X_repeat = x_test.repeat_interleave(n_train).reshape((-1, n_train))
attention_weights = nn.functional.softmax(-(X_repeat - x_train)**2 / 2, dim=1)
y_hat = torch.matmul(attention_weights, y_train)
plot_kernel_reg(y_hat)
d2l.show_heatmaps(attention_weights.unsqueeze(0).unsqueeze(0), xlabel='Sorted training inputs', ylabel='Sorted testing inputs')
X = torch.ones((2, 1, 4))
Y = torch.ones((2, 4, 6))
torch.bmm(X, Y).shape
weights = torch.ones((2, 10)) * 0.1
values = torch.arange(20.0).reshape((2, 10))
torch.bmm(weights.unsqueeze(1), values.unsqueeze(-1))
class NWKernelRegression(nn.Module):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.w = nn.Parameter(torch.rand((1,), requires_grad=True))
    def forward(self, queries, keys, values):
        queries = queries.repeat_interleave(keys.shape[1]).reshape((-1, keys.shape[1]))
        self.attention_weights = nn.functional.softmax(-((queries - keys) * self.w)**2 / 2, dim=1)
        return torch.bmm(self.attention_weights.unsqueeze(1), values.unsqueeze(-1)).reshape(-1)
X_tile = x_train.repeat((n_train, 1))
Y_tile = y_train.repeat((n_train, 1))
keys = X_tile[(1 - torch.eye(n_train)).type(torch.bool)].reshape((n_train, -1))
values = Y_tile[(1 - torch.eye(n_train)).type(torch.bool)].reshape((n_train, -1))
net = NWKernelRegression()
loss = nn.MSELoss(reduction='none')
trainer = torch.optim.SGD(net.parameters(), lr=0.5)
animator = d2l.Animator(xlabel='epoch', ylabel='loss', xlim=[1, 5])
for epoch in range(5):
    trainer.zero_grad()
    l = loss(net(x_train, keys, values), y_train)
    l.sum().backward()
    trainer.step()
    animator.add(epoch + 1, float(l.sum()))
keys = x_train.repeat((n_test, 1))
values = y_train.repeat((n_test, 1))
y_hat = net(x_test, keys, values).unsqueeze(1).detach()
plot_kernel_reg(y_hat)
d2l.show_heatmaps(net.attention_weights.unsqueeze(0).unsqueeze(0), xlabel='Sorted training inputs', ylabel='Sorted testing inputs')