79 lines
2.2 KiB
Python
79 lines
2.2 KiB
Python
|
from datetime import datetime
|
||
|
|
|
||
|
|
import pandas as pd
|
||
|
|
import numpy as np
|
||
|
|
from sacred.observers import FileStorageObserver, MongoObserver
|
||
|
|
from sacred import Experiment
|
||
|
|
|
||
|
|
from tensorflow import keras
|
||
|
|
import sys
|
||
|
|
|
||
|
|
from evaluate_network import evaluate_model
|
||
|
|
|
||
|
|
ex = Experiment("sacred_scopes", interactive=True)
|
||
|
|
# ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017',
|
||
|
|
# db_name='sacred'))
|
||
|
|
ex.observers.append(FileStorageObserver('my_runs'))
|
||
|
|
|
||
|
|
|
||
|
|
@ex.config
|
||
|
|
def my_config():
|
||
|
|
epochs_amount = 30
|
||
|
|
|
||
|
|
|
||
|
|
def normalize_data(data):
|
||
|
|
return (data - np.min(data)) / (np.max(data) - np.min(data))
|
||
|
|
|
||
|
|
|
||
|
|
@ex.capture
|
||
|
|
def prepare_model(epochs_amount, _run):
|
||
|
|
_run.info["prepare_message_ts"] = str(datetime.now())
|
||
|
|
|
||
|
|
data = pd.read_csv("data_train", sep=',', skip_blank_lines=True, nrows=1087, error_bad_lines=False,
|
||
|
|
names=["vipip install sacreddeo_id", "last_trending_date", "publish_date", "publish_hour",
|
||
|
|
"category_id",
|
||
|
|
"channel_title", "views", "likes", "dislikes", "comment_count"]).dropna()
|
||
|
|
|
||
|
|
X = data.loc[:, data.columns == "views"].astype(int)
|
||
|
|
y = data.loc[:, data.columns == "likes"].astype(int)
|
||
|
|
|
||
|
|
min_val_sub = np.min(X)
|
||
|
|
max_val_sub = np.max(X)
|
||
|
|
X = (X - min_val_sub) / (max_val_sub - min_val_sub)
|
||
|
|
print(min_val_sub)
|
||
|
|
print(max_val_sub)
|
||
|
|
|
||
|
|
min_val_like = np.min(y)
|
||
|
|
max_val_like = np.max(y)
|
||
|
|
y = (y - min_val_like) / (max_val_like - min_val_like)
|
||
|
|
|
||
|
|
print(min_val_like)
|
||
|
|
print(max_val_like)
|
||
|
|
|
||
|
|
model = keras.Sequential([
|
||
|
|
keras.layers.Dense(512, input_dim=X.shape[1], activation='relu'),
|
||
|
|
keras.layers.Dense(256, activation='relu'),
|
||
|
|
keras.layers.Dense(256, activation='relu'),
|
||
|
|
keras.layers.Dense(128, activation='relu'),
|
||
|
|
keras.layers.Dense(1, activation='linear'),
|
||
|
|
])
|
||
|
|
|
||
|
|
model.compile(loss='mean_absolute_error', optimizer="Adam", metrics=['mean_absolute_error'])
|
||
|
|
|
||
|
|
model.fit(X, y, epochs=int(epochs_amount), validation_split=0.3)
|
||
|
|
|
||
|
|
model.save('model')
|
||
|
|
|
||
|
|
metrics = evaluate_model()
|
||
|
|
print(metrics)
|
||
|
|
|
||
|
|
return metrics
|
||
|
|
|
||
|
|
|
||
|
|
@ex.main
|
||
|
|
def my_main(epochs_amount):
|
||
|
|
print(prepare_model())
|
||
|
|
|
||
|
|
|
||
|
|
ex.run()
|
||
|
|
ex.add_artifact("model.pb")
|