36 lines
1001 B
Python
36 lines
1001 B
Python
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from herrewebpy import logger
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import pandas as pd
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import numpy as np
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import tensorflow as tf
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from sklearn.preprocessing import StandardScaler
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def perceptron_build_model(df, hidden_units=64):
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numerical_features = df.select_dtypes(include=[np.number])
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# Standardize the numerical features
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scaler = StandardScaler()
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scaled_data = scaler.fit_transform(numerical_features)
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# Define the Perceptron model
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input_dim = scaled_data.shape[1]
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model = tf.keras.Sequential([
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tf.keras.layers.Input(shape=(input_dim,)),
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tf.keras.layers.Dense(hidden_units, activation='relu'),
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tf.keras.layers.Dense(1) # Output layer for regression
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])
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# Compile the model
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model.compile(optimizer='adam', loss='mean_squared_error')
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return model, scaled_data
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def train_model(df):
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model, scaled_data = perceptron_build_model(df)
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epochs = 100
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batch_size = 32
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model.fit(scaled_data, scaled_data, epochs=epochs, batch_size=batch_size, verbose=1)
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