40 lines
982 B
Python
40 lines
982 B
Python
import pandas as pd
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import numpy as np
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from tensorflow import keras
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import matplotlib.pyplot as plt
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from keras import backend as K
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from sklearn.metrics import mean_squared_error
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from tensorflow.keras import layers
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from tensorflow.keras.layers.experimental import preprocessing
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import tensorflow as tf
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train = pd.read_csv('train.csv')
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test = pd.read_csv('test.csv')
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validate = pd.read_csv('validate.csv')
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# # podział train set
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X_train = train.loc[:, train.columns != 'suicides_no']
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y_train = train[['suicides_no']]
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X_test = test.loc[:, train.columns != 'suicides_no']
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y_test = test[['suicides_no']]
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normalizer = preprocessing.Normalization()
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normalizer.adapt(np.array(X_train))
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model = tf.keras.Sequential([
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normalizer,
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layers.Dense(units=1)
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])
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model.summary()
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model.load_weights('suicide_model.h5')
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# predictions = model.predict(X_test)
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# error = mean_squared_error(y_test, predictions)
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# with open('eval_results.txt', 'a') as f:
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# f.write(str(error) + "\n")
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