27 lines
1.3 KiB
Python
27 lines
1.3 KiB
Python
import tensorflow as tf
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import numpy as np
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from sklearn.datasets import load_iris
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data = load_iris()
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iris_data = np.float32(data.data)
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iris_data_1 = []
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iris_data_2 = []
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for iris in iris_data:
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iris_data_1.append(iris[0])
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iris_data_2.append(iris[1:4])
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iris_target = np.float32(tf.keras.utils.to_categorical(data.target,num_classes=3))
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#注意数据的包裹层数。
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train_data = tf.data.Dataset.from_tensor_slices(((iris_data_1,iris_data_2),iris_target)).batch(128)
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input_xs_1 = tf.keras.Input(shape=(1,), name='input_xs_1') #接收输入参数一
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input_xs_2 = tf.keras.Input(shape=(3,), name='input_xs_2') #接收输入参数二
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input_xs = tf.concat([input_xs_1,input_xs_2],axis=-1) #重新组合参数
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out = tf.keras.layers.Dense(32, activation='relu', name='dense_1')(input_xs)
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out = tf.keras.layers.Dense(64, activation='relu', name='dense_2')(out)
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logits = tf.keras.layers.Dense(3, activation="softmax",name='predictions')(out)
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model = tf.keras.Model(inputs=[input_xs_1,input_xs_2], outputs=logits) #请注意model中的中括号
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opt = tf.optimizers.Adam(1e-3)
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model.compile(optimizer=tf.optimizers.Adam(1e-3), loss=tf.losses.categorical_crossentropy,metrics = ['accuracy'])
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model.fit(x = train_data, epochs=500)
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score = model.evaluate(train_data)
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print("多头score：",score) |