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38 lines
1.1 KiB
JavaScript
38 lines
1.1 KiB
JavaScript
import tensorflow as tf
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Dense
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from tensorflow.keras.utils import to_categorical
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import pandas as pd
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from sklearn.model_selection import train_test_split
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# Load your dataset
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data = pd.read_csv('path/to/your/dataset.csv')
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# Assuming the last column is the label
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labels = data.iloc[:, -1]
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features = data.iloc[:, :-1]
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# Convert labels to categorical if necessary
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labels = to_categorical(labels)
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# Split the data into training and testing sets
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train_data, test_data, train_labels, test_labels = train_test_split(features, labels, test_size=0.2)
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# Build the model
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model = Sequential([
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Dense(64, input_shape=(features.shape[1],), activation='relu'),
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Dense(64, activation='relu'),
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Dense(labels.shape[1], activation='softmax')
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])
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# Compile the model
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model.compile(optimizer='adam',
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loss='categorical_crossentropy',
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metrics=['accuracy'])
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# Train the model
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model.fit(train_data, train_labels, epochs=50, batch_size=32)
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# Evaluate the model
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loss, accuracy = model.evaluate(test_data, test_labels)
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print(f'Test accuracy: {accuracy}') |