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import numpy as np
import tensorflow as tf
from tensorflow import keras
from pathlib import Path
from jsonargparse import ArgumentParser
from utils_c import normalize, cost_function
def parse_args():
"""Parse command-line arguments."""
parser = ArgumentParser()
parser.add_argument("--data_dir", type=str, required=True, default="./processed")
parser.add_argument("--out_dir", type=str, required=True, default="./weight")
parser.add_argument("--num_features", type=int, required=True, default=10)
parser.add_argument("--num_iterators", type=int, required=True, default=200)
parser.add_argument("--learning_rate", type=float, required=True, default=1e-1)
parser.add_argument("--lambda_", type=float, required=True, default=2.0)
parser.add_argument("--seed", type=int, required=True, default=1234)
parser.add_argument("--freq", type=int, required=True, default=20)
return vars(parser.parse_args())
def main(
data_dir,
out_dir,
num_features,
num_iterators,
learning_rate,
lambda_,
seed,
freq
):
# Load R matrix from file
R = np.load(f'{data_dir}/R.npy', allow_pickle=True)
# Load Y matrix from file
Y = np.load(f'{data_dir}/Y.npy', allow_pickle=True)
# Normalize the Dataset
Y_norm, Y_mean = normalize(Y, R)
num_books, num_users = Y.shape
# Set Initial Parameters (W, X), use tf.Variable to track these variables
tf.random.set_seed(seed) # for consistent results
W = tf.Variable(tf.random.normal((num_users, num_features), dtype=tf.float64), name='W')
X = tf.Variable(tf.random.normal((num_books, num_features), dtype=tf.float64), name='X')
b = tf.Variable(tf.random.normal((1, num_users), dtype=tf.float64), name='b')
# Instantiate an optimizer.
optimizer = keras.optimizers.Adam(learning_rate=learning_rate)
for iter in range(num_iterators):
# Use TensorFlow’s GradientTape
# to record the operations used to compute the cost
with tf.GradientTape() as tape:
# Compute the cost (forward pass included in cost)
cost_value = cost_function(X, W, b, Y_norm, R, lambda_)
# Use the gradient tape to automatically retrieve
# the gradients of the trainable variables with respect to the loss
grads = tape.gradient(cost_value, [X, W, b])
# Run one step of gradient descent by updating
# the value of the variables to minimize the loss.
optimizer.apply_gradients(zip(grads, [X, W, b]))
# Log periodically.
if iter % freq == 0:
print(f"Training loss at iteration {iter}: {cost_value:0.1f}")
predict = np.matmul(X.numpy(), np.transpose(W.numpy())) + b.numpy()
predict = predict + Y_mean
# Save weight
out_dir = Path(out_dir)
if out_dir.exists():
assert out_dir.is_dir()
else:
out_dir.mkdir(parents=True)
np.save(f'{out_dir}/W.npy', W)
np.save(f'{out_dir}/X.npy', X)
np.save(f'{out_dir}/b.npy', b)
np.save(f'{out_dir}/predicted.npy', predict)
if __name__ == "__main__":
main(**parse_args())
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