정확도를 0.9 이상으로 올리는 방법을 알려주세요 ㅠㅠ

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import tensorflow as tf import numpy as np import matplotlib.pyplot as plt %matplotlib inline //학습 데이터 x_data &#61; [[ 8.62730150e-03, 3.21975275e-02], [ 3.33333333e-02, 5.77350269e-02], [ 7.07106781e-02, 7.07106781e-02], [ 1.15470054e-01, 6.66666667e-02], [ 1.60987638e-01, 4.31365075e-02], [ 2.00000000e-01, 1.22464680e-17], [ 2.25382693e-01, -6.03911105e-02], [ 2.30940108e-01, -1.33333333e-01], [ 2.12132034e-01, -2.12132034e-01], [ 1.66666667e-01,-2.88675135e-01], [ 9.49003165e-02, -3.54172803e-01], [ 4.89858720e-17, -4.00000000e-01], [-1.12154920e-01, -4.18567858e-01], [-2.33333333e-01, -4.04145188e-01], [-3.53553391e-01, -3.53553391e-01], [-4.61880215e-01, -2.66666667e-01], [-5.47357968e-01, -1.46664126e-01], [-6.00000000e-01, -1.10218212e-16], [-6.11753023e-01, 1.63918729e-01], [-5.77350269e-01, 3.33333333e-01], [-4.94974747e-01, 4.94974747e-01], [-3.66666667e-01, 6.35085296e-01], [-1.98427935e-01, 7.40543133e-01], [-1.95943488e-16, 8.00000000e-01], [ 2.15682538e-01, 8.04938189e-01], [ 4.33333333e-01, 7.50555350e-01], [ 6.36396103e-01, 6.36396103e-01], [ 8.08290377e-01, 4.66666667e-01], [ 9.33728299e-01, 2.50191744e-01], [ 1.00000000e&#43;00, 3.06161700e-16], [ 0.00000000e&#43;00, -0.00000000e&#43;00], [-8.62730150e-03, -3.21975275e-02], [-3.33333333e-02, -5.77350269e-02], [-7.07106781e-02, -7.07106781e-02], [-1.15470054e-01, -6.66666667e-02], [-1.60987638e-01, -4.31365075e-02], [-2.00000000e-01, -3.67394040e-17], [-2.25382693e-01, 6.03911105e-02], [-2.30940108e-01, 1.33333333e-01], [-2.12132034e-01, 2.12132034e-01], [-1.66666667e-01, 2.88675135e-01], [-9.49003165e-02, 3.54172803e-01], [-9.79717439e-17, 4.00000000e-01], [ 1.12154920e-01, 4.18567858e-01], [ 2.33333333e-01, 4.04145188e-01], [ 3.53553391e-01, 3.53553391e-01], [ 4.61880215e-01, 2.66666667e-01], [ 5.47357968e-01, 1.46664126e-01], [ 6.00000000e-01, 1.83697020e-16], [ 6.11753023e-01, -1.63918729e-01], [ 5.77350269e-01, -3.33333333e-01], [ 4.94974747e-01, -4.94974747e-01], [ 3.66666667e-01, -6.35085296e-01], [ 1.98427935e-01, -7.40543133e-01], [ 2.93915232e-16, -8.00000000e-01], [-2.15682538e-01, -8.04938189e-01], [-4.33333333e-01, -7.50555350e-01], [-6.36396103e-01, -6.36396103e-01], [-8.08290377e-01, -4.66666667e-01], [-9.33728299e-01, -2.50191744e-01]] y_data &#61; [[1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0]] for i in range(len(x_data)): _x&#61;np.array(x_data[i]) _y&#61;np.array(y_data[i]) if _y > 0.5: plt.plot(_x[0],_x[1],'yo') else: plt.plot(_x[0],_x[1],'go') plt.show() x &#61; tf.constant(x_data, dtype&#61;tf.float32) y &#61; tf.constant(y_data, dtype&#61;tf.float32) ################ 수정해야 할 부분 ######################## a &#61; 0.05 #학습률 W1 &#61; tf.Variable(tf.random.normal([2,2]), dtype&#61;tf.float32) b1 &#61; tf.Variable(tf.random.normal([2]), dtype&#61;tf.float32) H1 &#61; tf.nn.sigmoid(tf.matmul(x,W1)&#43;b1) W2 &#61; tf.Variable(tf.random.normal([2,1]), dtype&#61;tf.float32) b2 &#61; tf.Variable(tf.random.normal([1]), dtype&#61;tf.float32) H &#61; tf.sigmoid(tf.matmul(H1,W2)&#43;b2) ########################################################## def cost(H, y): #비용함수 return - tf.reduce_mean(y * tf.math.log(H) &#43; (1 - y) * tf.math.log(1 - H)) # 정확도 계산 # 가설 H가 0.5보다 크면 1 작으면 0 def predicted(H): return tf.cast(H > 0.5, dtype&#61;tf.float32).numpy() def accuracy(predicted, y): return tf.reduce_mean(tf.cast(tf.equal(predicted, y), dtype&#61;tf.float32)).numpy() optimizer &#61; tf.optimizers.SGD(a) cost_list &#61; [] iteration &#61; 10000 for step in range(iteration): with tf.GradientTape() as tape: H1 &#61; tf.nn.sigmoid(tf.matmul(x,W1)&#43;b1) H &#61; tf.sigmoid(tf.matmul(H1,W2)&#43;b2) current_loss &#61; cost(H, y) grads &#61; tape.gradient(current_loss , [W1, W2, b1, b2]) optimizer.apply_gradients(zip(grads , [W1, W2, b1, b2])) cost_list.append(cost(H, y).numpy()) if step % (iteration//10) &#61;&#61; 0: # 전체 학습 중 10번만 비용함수/정확도 표시 print(&#34;Step : %i, 비용함수 : %s 정확도 : %s&#34; %(step, cost(H, y).numpy(), accuracy(predicted(H), y))) plt.plot(cost_list, &#34;c&#34;) plt.show()

posj · Answer

옛날에 배워서 가물가물한데학습률 조정 - a의 값을 바꿔보세용 반복횟수 조정 - 반복횟수를 늘리세요 너무 늘리진마시구 오버피팅되니깐~학부생이신가? 드롭아웃이나 이런 기법 되시면 넣어보는게 좋을것같아용

정확도를 0.9 이상으로 올리는 방법을 알려주세요 ㅠㅠ

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