Karolina Oparczyk
630aba3217
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45 lines
1.4 KiB
Python
45 lines
1.4 KiB
Python
import pandas as pd
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import numpy as np
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from sklearn.metrics import mean_squared_error
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from tensorflow import keras
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import sys
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def normalize_data(data):
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return (data - np.min(data)) / (np.max(data) - np.min(data))
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data = pd.read_csv("data_train", sep=',', skip_blank_lines=True, nrows=1087, error_bad_lines=False,
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names=["video_id", "last_trending_date", "publish_date", "publish_hour", "category_id",
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"channel_title", "views", "likes", "dislikes", "comment_count"]).dropna()
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X = data.loc[:,data.columns == "views"].astype(int)
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y = data.loc[:,data.columns == "likes"].astype(int)
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min_val_sub = np.min(X)
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max_val_sub = np.max(X)
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X = (X - min_val_sub) / (max_val_sub - min_val_sub)
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print(min_val_sub)
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print(max_val_sub)
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min_val_like = np.min(y)
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max_val_like = np.max(y)
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y = (y - min_val_like) / (max_val_like - min_val_like)
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print(min_val_like)
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print(max_val_like)
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model = keras.Sequential([
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keras.layers.Dense(512,input_dim = X.shape[1], activation='relu'),
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keras.layers.Dense(256, activation='relu'),
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keras.layers.Dense(256, activation='relu'),
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keras.layers.Dense(128, activation='relu'),
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keras.layers.Dense(1,activation='linear'),
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])
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model.compile(loss='mean_absolute_error', optimizer="Adam", metrics=['mean_absolute_error'])
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model.fit(X, y, epochs=int(sys.argv[1]), validation_split = 0.3)
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model.save('model') |