Keras中的簡單神經網絡 - 錯誤的分類

Question

爲了深入瞭解Keras和TensorFlow後端的進一步研究項目，我試着實現一個簡單分類問題的神經網絡。我只想區分randomly distributed points in 2D into two categories depending on their coordinates（顏色表示類別）。 相關的代碼以產生所述數據爲：

import numpy as np  
np.random.seed(40) 

def createData(N=120, M=75): 
    train_x1 = np.random.random(size=N) 
    train_x2 = np.random.random(size=N) 

    test_x1 = np.random.random(size=M) 
    test_x2 = np.random.random(size=M) 

    train_x = np.zeros((N, 2)) 
    train_y = np.zeros((N, 1)) 

    test_x = np.zeros((M, 2)) 
    test_y = np.zeros((M, 1)) 

    for i in range(N): 
     train_x[i][0] = train_x1[i] 
     train_x[i][1] = train_x2[i] 
     if train_x1[i] < 0.5: 
      if train_x2[i] < 0.5: 
       train_y[i][0] = 1 
      else: 
       train_y[i][0] = 2 
     else: 
      if train_x2[i] < 0.5: 
       train_y[i][0] = 2 
      else: 
       train_y[i][0] = 1 

    for j in range(M): 
     test_x[j][0] = test_x1[j] 
     test_x[j][1] = test_x2[j] 
     if test_x1[j] < 0.5: 
      if test_x2[j] < 0.5: 
       test_y[j][0] = 1 
      else: 
       test_y[j][0] = 2 
     else: 
      if test_x2[j] < 0.5: 
       test_y[j][0] = 2 
      else: 
       test_y[j][0] = 1 

    return train_x, train_y, test_x, test_y 

我給神經網絡代碼如下：

from keras.models import Sequential 
from keras.layers import Dense 
import numpy as np 
import matplotlib.pyplot as plt 

X, Y, x, y = createData() 

# Model 
model = Sequential() 
model.add(Dense(4, input_dim=2, activation='relu')) 
model.add(Dense(10, activation='sigmoid')) 
model.add(Dense(1, activation='sigmoid')) 

# Compile 
model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy']) 

# Fit 
model.fit(X, Y, epochs=500, batch_size=25) 

# Evaluation 
scores = model.evaluate(X, Y) 
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100)) 

# Predictions 
predictions = model.predict(x) 
rounded = [round(z[0]) for z in predictions] 
print(rounded) 

我的問題是，該神經網絡達到47.5％與上述的精度提到種子和配置。它分配all test coordinates to only one category。然而，其他配置（更多層，更多/更少的神經元，其他激活功能，其他損失函數等）導致類似的結果。

外殼上的「監控時代」的最後幾行看起來是這樣的：

Epoch 490/500 
120/120 [==============================] - 0s - loss: -8.3351 - acc: 0.4750  
Epoch 491/500 
120/120 [==============================] - 0s - loss: -8.1866 - acc: 0.4750  
Epoch 492/500 
120/120 [==============================] - 0s - loss: -8.3524 - acc: 0.4750  
Epoch 493/500 
120/120 [==============================] - 0s - loss: -8.2608 - acc: 0.4750  
Epoch 494/500 
120/120 [==============================] - 0s - loss: -8.3269 - acc: 0.4750  
Epoch 495/500 
120/120 [==============================] - 0s - loss: -8.2039 - acc: 0.4750  
Epoch 496/500 
120/120 [==============================] - 0s - loss: -8.1786 - acc: 0.4750  
Epoch 497/500 
120/120 [==============================] - 0s - loss: -8.2488 - acc: 0.4750  
Epoch 498/500 
120/120 [==============================] - 0s - loss: -8.3090 - acc: 0.4750  
Epoch 499/500 
120/120 [==============================] - 0s - loss: -8.3457 - acc: 0.4750  
Epoch 500/500 
120/120 [==============================] - 0s - loss: -8.1235 - acc: 0.4750 

如何提高神經網絡從這個愚蠢的行爲脫身？非常感謝您的任何意見和建議！

Answer 1

首先，我會檢查您的輸入數據是否正確創建/標記。嘗試打印出一對值，以確保它有意義。

其次，當學習率很高時，我通常會看到這個問題。您可以嘗試移除中間有10個神經元的隱藏層或降低學習速率。我認爲一個深層的網絡對於這個問題是過度的。嘗試使用這個你的編譯線。

model.compile(loss='binary_crossentropy', optimizer=keras.optimizers.Adam(lr=0.0001), metrics=['accuracy']) 

學習率爲0.001是Adam的默認值，但我發現如果0.001不使用0.0001總是會收斂。