import pathlib
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import numpy as np

# 100个用户，300个物品
data = pd.DataFrame(np.random.randint(5, size=(100, 300)))

def build_model():
    model = keras.Sequential([
        layers.Dense(100, activation = 'relu', input_shape=[100]),
        layers.Dense(100, activation = 'relu'),
    ])
    optimizer = tf.keras.optimizers.RMSprop(0.001)
    model.compile(loss = 'mse',
                 optimizer = optimizer,
                 metrics = ['mae', 'mse'])
    return model

model = build_model()
model.summary()

train_data = data.T
EPOCHS = 500
model.fit(train_data,
           train_data,
           epochs = EPOCHS,
           validation_split = 0.1,
           verbose = 0)

pd.DataFrame(model.predict(train_data).T)