mxnet.ndarray.Activation(data=None, act_type=_Null, out=None, name=None, **kwargs) Applies an activation function element-wise to the input. 應用激活函數對輸入元素。

參數 data (NDArray) – 輸入數據act_type ({'relu', 'sigmoid', 'softrelu', 'softsign', 'tanh'}, required) – 必須提供的參數，指明激活函數類型out (NDArray, optional) – 保存結果輸出數組返回 out –函數的輸出返回類型 NDArray或NDArray列表 from mxnet import nd import mxnet as mx a = nd.arange(-6,6,0.3) print(a) y=nd.Activation(data=a,act_type="relu") print(y) y=nd.Activation(data=a,act_type="sigmoid") print(y) y=nd.Activation(data=a,act_type="tanh") print(y) y=nd.Activation(data=a,act_type="softrelu") print(y) y=nd.Activation(data=a,act_type="softsign") print(y) [-6. -5.7 -5.4 -5.1 -4.8 -4.5-4.2 -3.8999999 -3.6 -3.3 -3. -2.6999998-2.3999999 -2.1 -1.7999997 -1.5 -1.1999998 -0.8999996-0.5999999 -0.2999997 0. 0.3000002 0.6000004 0.90000011.2000003 1.5000005 1.8000002 2.1000004 2.4000006 2.70000083. 3.3000002 3.6000004 3.9000006 4.200001 4.54.8 5.1000004 5.4000006 5.700001 ] <NDArray 40 @cpu(0)>[-0. -0. -0. -0. -0. -0.-0. -0. -0. -0. -0. -0.-0. -0. -0. -0. -0. -0.-0. -0. 0. 0.3000002 0.6000004 0.90000011.2000003 1.5000005 1.8000002 2.1000004 2.4000006 2.70000083. 3.3000002 3.6000004 3.9000006 4.200001 4.54.8 5.1000004 5.4000006 5.700001 ] <NDArray 40 @cpu(0)>[0.00247262 0.00333481 0.00449627 0.0060598 0.00816257 0.010986940.01477403 0.01984031 0.02659699 0.03557119 0.04742587 0.062973370.08317271 0.10909683 0.14185111 0.18242553 0.23147525 0.289050580.3543437 0.42555758 0.5 0.5744425 0.64565647 0.710949540.7685248 0.81757456 0.85814893 0.89090323 0.9168273 0.93702670.95257413 0.96442884 0.97340304 0.9801597 0.985226 0.98901310.99183744 0.9939402 0.9955037 0.9966652 ] <NDArray 40 @cpu(0)>[-0.9999877 -0.9999776 -0.99995923 -0.9999257 -0.9998646 -0.99975324-0.99955034 -0.99918085 -0.9985079 -0.997283 -0.9950548 -0.99100745-0.9836749 -0.97045195 -0.94680595 -0.90514827 -0.8336545 -0.71629775-0.5370495 -0.29131234 0. 0.29131278 0.5370498 0.7162980.8336547 0.9051483 0.946806 0.97045195 0.9836749 0.991007450.9950548 0.997283 0.9985079 0.99918085 0.99955034 0.999753240.9998646 0.9999257 0.99995923 0.9999776 ] <NDArray 40 @cpu(0)>[2.4757385e-03 3.3402443e-03 4.5065880e-03 6.0782433e-03 8.1958771e-031.1047840e-02 1.4883995e-02 2.0040035e-02 2.6957035e-02 3.6219120e-024.8587322e-02 6.5043688e-02 8.6836338e-02 1.1551952e-01 1.5297771e-012.0141327e-01 2.6328242e-01 3.4115410e-01 4.3748796e-01 5.5435544e-016.9314724e-01 8.5435545e-01 1.0374882e+00 1.2411540e+00 1.4632828e+001.7014138e+00 1.9529779e+00 2.2155199e+00 2.4868367e+00 2.7650442e+003.0485873e+00 3.3362193e+00 3.6269574e+00 3.9200404e+00 4.2148852e+004.5110478e+00 4.8081965e+00 5.1060786e+00 5.4045072e+00 5.7033410e+00] <NDArray 40 @cpu(0)>[-0.85714287 -0.8507463 -0.84375 -0.8360656 -0.82758623 -0.8181818-0.8076923 -0.7959184 -0.7826087 -0.7674418 -0.75 -0.7297297-0.7058823 -0.67741936 -0.64285713 -0.6 -0.5454545 -0.4736841-0.37499997 -0.23076907 0. 0.23076934 0.37500015 0.473684250.5454546 0.6000001 0.6428572 0.6774194 0.70588243 0.72972980.75 0.76744187 0.7826087 0.7959184 0.80769235 0.81818180.82758623 0.8360656 0.84375 0.8507463 ] <NDArray 40 @cpu(0)>

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