Yash-567

var request = require('request');
var cheerio = require('cheerio');

request('https://www.reddit.com/', function (error, response, body) {
	$ = cheerio.load(body);

	$('a.title').each(function (i, elem) {
		console.log('');
		console.log('** Link ** ')
		console.log($(this).text());

Yash-567

K = 10                             # Number of latent features 
N = len(set(df.userId.values))     # Number of users
M = len(set(df.movieId.values))    # Number of movies

from torch import nn
import torch
import torch.nn.functional as F

class Network(nn.Module):
  

Yash-567

model = Network()
model.double()
model

Yash-567

from torch import optim

criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr = 0.08, momentum=0.9)

Yash-567

import random
from tqdm import tqdm

train_losses, test_losses = [], []

for e in range(epochs):
   running_loss = 0
   for i in tqdm(range(0, len(df_train), 128)):
      train = df_train[i:i+128]
      optimizer.zero_grad()

Yash-567

# keras model
u = Input(shape=(1,))
m = Input(shape=(1,))
u_embedding = Embedding(N, K)(u) # (N, 1, K)
m_embedding = Embedding(M, K)(m) # (N, 1, K)
u_embedding = Flatten()(u_embedding) # (N, K)
m_embedding = Flatten()(m_embedding) # (N, K)
x = Concatenate()([u_embedding, m_embedding]) # (N, 2K)

x = Dense(400)(x)

Yash-567

from sklearn.utils import shuffle
from torch import nn
import torch
import torch.nn.functional as F

N = df.userId.max() + 1 # number of users
M = df.movie_idx.max() + 1 # number of movies

# split into train and test
df = shuffle(df, random_state = 12)

Yash-567

model.compile(
  loss='mse',
  optimizer=SGD(lr=0.08, momentum=0.9),
  metrics=['mse'],
)

Yash-567

mc = keras.callbacks.ModelCheckpoint('weights{epoch:01d}.h5', period=1)
r = model.fit(
  x=[df_train.userId.values, df_train.movie_idx.values],
  y=df_train.rating.values - mu,
  epochs=epochs,
  batch_size=128,
  validation_data=(
    [df_test.userId.values, df_test.movie_idx.values],
    df_test.rating.values - mu
  ),

Yash-567

model_2 = Model(input=model.get_layer('input_2').input, output = model.get_layer('embedding_2').output) 
user_movie_embeddings = model_2.predict(x = usrc) # usrc = array of movie id liked by user
user_movie_embeddings = user_movie_embeddings.reshape(len(usrc),10) 
user_movie_bias = np.array([5 for temp in range(len(usrc))])

user_embedding, residuals, rank, s = np.linalg.lstsq(user_movie_embeddings,user_movie_bias, rcond=-1) # Get embedding for new user
user_embedding = user_embedding.reshape(1, 10) # User embedding based on choices of user