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- import keras
- import sklearn
- import numpy as np
- from sklearn import datasets
- from sklearn.model_selection import train_test_split
- from sklearn import preprocessing
- from sklearn.metrics import f1_score
- from keras.utils import to_categorical
- from keras.layers import Dense, Activation
- from keras.models import Sequential, Model
- iris_X, iris_y = datasets.load_iris(return_X_y=True)
- iris_X = preprocessing.scale(iris_X)
- iris_y = to_categorical(iris_y)
- train_X, test_X, train_y, test_y = train_test_split(iris_X, iris_y, test_size=0.25)
- train_X, valid_X, train_y, valid_y = train_test_split(iris_X, iris_y, test_size=0.2)
- model = Sequential()
- model.add(Dense(10, input_dim=4, activation='relu'))
- model.add(Dense(3, activation='softmax'))
- model.compile(loss="binary_crossentropy", optimizer="adam", metrics=['accuracy'])
- model.fit(train_X, train_y, epochs=30, batch_size=1, validation_data=(valid_X, valid_y))
- pred_y = model.predict(test_X)
- print(f1_score(np.argmax(test_y, 1), np.argmax(pred_y, 1), average='macro'))
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