Machine Learning(一) 一个神经元网络-线性函数
一个神经元的网络(线性函数)
线性函数 y = 2 * x -1
程序源代码
| from tensorflow import keras import numpy as np
model = keras.Sequential([keras.layers.Dense(units=1,input_shape=[1])]) model.compile(optimizer='sgd',loss='mean_squared_error')
xs=np.array([-1.0,0.0,1.0,2.0,3.0,4.0], dtype = float) ys=np.array([-3.0,-1.0,1.0,3.0,5.0,7.0], dtype = float)
model.fit(xs,ys,epochs=500)
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训练模型过程(截取)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
| Epoch 1/500 1/1 [==============================] - 0s 388ms/step - loss: 3.4976 Epoch 2/500 1/1 [==============================] - 0s 3ms/step - loss: 2.9127 Epoch 3/500 1/1 [==============================] - 0s 3ms/step - loss: 2.4493 Epoch 4/500 1/1 [==============================] - 0s 3ms/step - loss: 2.0814 Epoch 5/500 1/1 [==============================] - 0s 4ms/step - loss: 1.7888 Epoch 6/500 1/1 [==============================] - 0s 4ms/step - loss: 1.5555 Epoch 7/500 1/1 [==============================] - 0s 4ms/step - loss: 1.3689 Epoch 8/500 1/1 [==============================] - 0s 4ms/step - loss: 1.2191 Epoch 9/500 1/1 [==============================] - 0s 3ms/step - loss: 1.0983 Epoch 10/500 1/1 [==============================] - 0s 4ms/step - loss: 1.0005 Epoch 11/500 1/1 [==============================] - 0s 4ms/step - loss: 0.9207 Epoch 12/500 1/1 [==============================] - 0s 2ms/step - loss: 0.8551 Epoch 13/500 1/1 [==============================] - 0s 5ms/step - loss: 0.8009
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使用模型
| print(model.predict([2021]))
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| array([[18.984968]], dtype=float32)
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