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42 lines
1.1 KiB
Python
42 lines
1.1 KiB
Python
5 years ago
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import tensorflow as tf
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from tensorflow import keras
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import pandas as pd
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import numpy as np
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train_df = pd.read_csv('./data/train.csv')
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color_dict = {'red': 0, 'blue': 1, 'green': 2, 'teal': 3, 'orange': 4, 'purple': 5}
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train_df['color'] = train_df.color.apply(lambda x: color_dict[x])
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np.random.shuffle(train_df.values)
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print(train_df.head())
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print(train_df.color.unique())
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model = keras.Sequential([
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keras.layers.Dense(32, input_shape=(2,), activation='relu'),
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keras.layers.Dense(32, activation='relu'),
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keras.layers.Dense(6, activation='sigmoid')])
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model.compile(optimizer='adam',
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loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
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metrics=['accuracy'])
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x = np.column_stack((train_df.x.values, train_df.y.values))
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model.fit(x, train_df.color.values, batch_size=4, epochs=10)
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test_df = pd.read_csv('./data/test.csv')
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test_x = np.column_stack((test_df.x.values, test_df.y.values))
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print("EVALUATION")
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test_df['color'] = test_df.color.apply(lambda x: color_dict[x])
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model.evaluate(test_x, test_df.color.values)
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print("Prediction", np.round(model.predict(np.array([[0,3]]))))
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