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- import tensorflow
- from sklearn.datasets import load_iris
- from tensorflow.keras.models import Sequential
- from tensorflow.keras.layers import Dense
- iris = load_iris()
- X = iris.data
- Y = iris.target
- y= tensorflow.keras.utils.to_categorical(Y)
- model = Sequential()
- model.add(Dense(8, input_dim=4, activation = 'relu'))
- model.add(Dense(8,activation='relu'))
- model.add(Dense(3,activation='softmax'))
- model.compile(optimizer='adam', loss='categorical_crossentropy',metrics=['accuracy'])
- model.fit(X,y, batch_size=5, epochs = 200)
- model.save('iris.h5')
- print(model.summary)
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