TensorFlow: %matplotlib inline
import numpy as np
import pandas as pd
import tensorflow as tf
from d2l import tensorflow as d2l
n_train = train_data.shape[0]
train_features = tf.constant(all_features[:n_train].values, dtype=tf.float32)
test_features = tf.constant(all_features[n_train:].values, dtype=tf.float32)
train_labels = tf.constant(train_data.SalePrice.values.reshape(-1, 1), dtype=tf.float32)
loss = tf.keras.losses.MeanSquaredError()
def get_net():
    net = tf.keras.models.Sequential()
    net.add(tf.keras.layers.Dense(1, kernel_regularizer=tf.keras.regularizers.l2(weight_decay)))
    return net
def log_rmse(y_true, y_pred):
    clipped_preds = tf.clip_by_value(y_pred, 1, float('inf'))
    return tf.sqrt(tf.reduce_mean(loss(tf.math.log(y_true), tf.math.log(clipped_preds))))
def train(net, train_features, train_labels, test_features, test_labels, num_epochs, learning_rate, weight_decay, batch_size):
    train_ls, test_ls = [], []
    train_iter = d2l.load_array((train_features, train_labels), batch_size)
    optimizer = tf.keras.optimizers.Adam(learning_rate)
    net.compile(loss=loss, optimizer=optimizer)
    for epoch in range(num_epochs):
        for X, y in train_iter:
            with tf.GradientTape() as tape:
                y_hat = net(X)
                l = loss(y, y_hat)
            params = net.trainable_variables
            grads = tape.gradient(l, params)
            optimizer.apply_gradients(zip(grads, params))
        train_ls.append(log_rmse(train_labels, net(train_features)))
        if test_labels is not None:
            test_ls.append(log_rmse(test_labels, net(test_features)))
    return train_ls, test_ls
def get_k_fold_data(k, i, X, y):
    assert k > 1
    fold_size = X.shape[0] // k
    X_train, y_train = None, None
    for j in range(k):
        idx = slice(j * fold_size, (j + 1) * fold_size)
        X_part, y_part = X[idx, :], y[idx]
        if j == i:
            X_valid, y_valid = X_part, y_part
        elif X_train is None:
            X_train, y_train = X_part, y_part
        else:
            X_train = tf.concat([X_train, X_part], 0)
            y_train = tf.concat([y_train, y_part], 0)
    return X_train, y_train, X_valid, y_valid
def train_and_pred(train_features, test_features, train_labels, test_data, num_epochs, lr, weight_decay, batch_size):
    net = get_net()
    train_ls, _ = train(net, train_features, train_labels, None, None, num_epochs, lr, weight_decay, batch_size)
    d2l.plot(np.arange(1, num_epochs + 1), [train_ls], xlabel='epoch', ylabel='log rmse', xlim=[1, num_epochs], yscale='log')
    preds = net(test_features).numpy()
    test_data['SalePrice'] = pd.Series(preds.reshape(1, -1)[0])
    submission = pd.concat([test_data['Id'], test_data['SalePrice']], axis=1)
    submission.to_csv('submission.csv', index=False)



Paddle: %matplotlib inline
import warnings
import numpy as np
import pandas as pd
warnings.filterwarnings(action='ignore')
import paddle
from paddle import nn
warnings.filterwarnings("ignore", category=DeprecationWarning)
from d2l import paddle as d2l
n_train = train_data.shape[0]
train_features = paddle.to_tensor(all_features[:n_train].values, dtype=paddle.float32)
test_features = paddle.to_tensor(all_features[n_train:].values, dtype=paddle.float32)
train_labels = paddle.to_tensor(
    train_data.SalePrice.values.reshape(-1, 1), dtype=paddle.float32)
loss = nn.MSELoss()
in_features = train_features.shape[1]
def get_net():
    net = nn.Sequential(nn.Linear(in_features,1))
    return net
def log_rmse(net, features, labels):
    clipped_preds = paddle.clip(net(features), 1, float('inf'))
    rmse = paddle.sqrt(loss(paddle.log(clipped_preds), paddle.log(labels)))
    return rmse.item()
def train(net, train_features, train_labels, test_features, test_labels, num_epochs, learning_rate, weight_decay, batch_size):
    train_ls, test_ls = [], []
    train_iter = d2l.load_array((train_features, train_labels), batch_size)
    optimizer = paddle.optimizer.Adam(learning_rate=learning_rate*1.0, parameters=net.parameters(), weight_decay=weight_decay*1.0)
    for epoch in range(num_epochs):
        for X, y in train_iter:
            l = loss(net(X), y)
            l.backward()
            optimizer.step()
            optimizer.clear_grad()
        train_ls.append(log_rmse(net, train_features, train_labels))
        if test_labels is not None:
            test_ls.append(log_rmse(net, test_features, test_labels))
    return train_ls, test_ls
def get_k_fold_data(k, i, X, y):
    assert k > 1
    fold_size = X.shape[0] // k
    X_train, y_train = None, None
    for j in range(k):
        idx = slice(j * fold_size, (j + 1) * fold_size)
        X_part, y_part = X[idx, :], y[idx]
        if j == i:
            X_valid, y_valid = X_part, y_part
        elif X_train is None:
            X_train, y_train = X_part, y_part
        else:
            X_train = paddle.concat([X_train, X_part], 0)
            y_train = paddle.concat([y_train, y_part], 0)
    return X_train, y_train, X_valid, y_valid
def train_and_pred(train_features, test_features, train_labels, test_data, num_epochs, lr, weight_decay, batch_size):
    net = get_net()
    train_ls, _ = train(net, train_features, train_labels, None, None, num_epochs, lr, weight_decay, batch_size)
    d2l.plot(np.arange(1, num_epochs + 1), [train_ls], xlabel='epoch', ylabel='log rmse', xlim=[1, num_epochs], yscale='log')
    preds = net(test_features).detach().numpy()
    test_data['SalePrice'] = pd.Series(preds.reshape(1, -1)[0])
    submission = pd.concat([test_data['Id'], test_data['SalePrice']], axis=1)
    submission.to_csv('submission.csv', index=False)