精度提高但對許多時期保持不變

Question

我的模型精確度迅速提高到94.3％，但在其他時代停留在那裏。 這裏是我的模型和代碼：

model = Sequential() 
model.add(Conv2D(5, (3,3), strides=(2,2), kernel_initializer='normal', activation='sigmoid', input_shape=(dim, dim, 3))) 
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2,2))) 
model.add(Conv2D(5, (3,3), strides=(2,2), activation='sigmoid')) 
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2,2))) 

# Create the feature vector 
model.add(Flatten()) 
model.add(Dense(12288, activation='sigmoid')) 
model.add(Dropout(0.2)) 
model.add(Dense(1536, activation='sigmoid')) 
model.add(Dropout(0.3)) 
model.add(Dense(384, activation='sigmoid')) 
model.add(Dropout(0.4)) 
model.add(Dense(1, activation='sigmoid')) 

sgd = SGD(lr=0.001, momentum=0.9) 
model.compile(loss="binary_crossentropy", optimizer="sgd", metrics=["accuracy"]) 
model.fit(data, labels, epochs=20, batch_size=100, callbacks=callbacks_list, verbose=1) 
CNN_output = model.predict(data) 

培訓的輸出結果如下： CNN Output

檢查CNN的輸出（從預測）我得到以下（注意，這僅僅是一個樣本）：

ACTUAL: train_0: 
[ 1.] 
PREDICTION: train_0: 
[ 0.] 
ACTUAL: train_1: 
[ 0.] 
PREDICTION: train_1: 
[ 0.] 
ACTUAL: train_2: 
[ 0.] 
PREDICTION: train_2: 
[ 0.] 
ACTUAL: train_3: 
[ 0.] 
PREDICTION: train_3: 
[ 0.] 
ACTUAL: train_4: 
[ 0.] 
PREDICTION: train_4: 
[ 0.] 
ACTUAL: train_5: 
[ 1.] 
PREDICTION: train_5: 
[ 0.] 
ACTUAL: train_6: 
[ 0.] 
PREDICTION: train_6: 
[ 0.] 
ACTUAL: train_7: 
[ 1.] 
PREDICTION: train_7: 
[ 0.] 
ACTUAL: train_8: 
[ 0.] 
PREDICTION: train_8: 
[ 0.] 
ACTUAL: train_9: 
[ 0.] 
PREDICTION: train_9: 
[ 0.] 
ACTUAL: train_10: 
[ 0.] 
PREDICTION: train_10: 
[ 0.] 
ACTUAL: train_11: 
[ 0.] 
PREDICTION: train_11: 
[ 0.] 
ACTUAL: train_12: 
[ 0.] 
PREDICTION: train_12: 
[ 0.] 
ACTUAL: train_13: 
[ 0.] 
PREDICTION: train_13: 
[ 0.] 
ACTUAL: train_14: 
[ 0.] 
PREDICTION: train_14: 
[ 0.] 
ACTUAL: train_15: 
[ 0.] 
PREDICTION: train_15: 
[ 0.] 
ACTUAL: train_16: 
[ 0.] 
PREDICTION: train_16: 
[ 0.] 
ACTUAL: train_17: 
[ 0.] 
PREDICTION: train_17: 
[ 0.] 
ACTUAL: train_18: 
[ 0.] 
PREDICTION: train_18: 
[ 0.] 
ACTUAL: train_19: 
[ 0.] 
PREDICTION: train_19: 
[ 0.] 
ACTUAL: train_20: 
[ 0.] 
PREDICTION: train_20: 
[ 0.] 
ACTUAL: train_21: 
[ 0.] 
PREDICTION: train_21: 
[ 0.] 
ACTUAL: train_22: 
[ 0.] 
PREDICTION: train_22: 
[ 0.] 
ACTUAL: train_23: 
[ 0.] 
PREDICTION: train_23: 
[ 0.] 
ACTUAL: train_24: 
[ 0.] 
PREDICTION: train_24: 
[ 0.] 
ACTUAL: train_25: 
[ 0.] 
PREDICTION: train_25: 
[ 0.] 
ACTUAL: train_26: 
[ 0.] 
PREDICTION: train_26: 
[ 0.] 
ACTUAL: train_27: 
[ 0.] 
PREDICTION: train_27: 
[ 0.] 
ACTUAL: train_28: 
[ 0.] 
PREDICTION: train_28: 
[ 0.] 
ACTUAL: train_29: 
[ 0.] 
PREDICTION: train_29: 
[ 0.] 
ACTUAL: train_30: 
[ 0.] 
PREDICTION: train_30: 
[ 0.] 
ACTUAL: train_31: 
[ 0.] 
PREDICTION: train_31: 
[ 0.] 
ACTUAL: train_32: 
[ 0.] 
PREDICTION: train_32: 
[ 0.] 
ACTUAL: train_33: 
[ 0.] 
PREDICTION: train_33: 
[ 0.] 
ACTUAL: train_34: 
[ 0.] 
PREDICTION: train_34: 
[ 0.] 
ACTUAL: train_35: 
[ 0.] 
PREDICTION: train_35: 
[ 0.] 
ACTUAL: train_36: 
[ 0.] 
PREDICTION: train_36: 
[ 0.] 
ACTUAL: train_37: 
[ 0.] 
PREDICTION: train_37: 
[ 0.] 
ACTUAL: train_38: 
[ 0.] 
PREDICTION: train_38: 
[ 0.] 
ACTUAL: train_39: 
[ 0.] 
PREDICTION: train_39: 
[ 0.] 
ACTUAL: train_40: 
[ 0.] 
PREDICTION: train_40: 
[ 0.] 
ACTUAL: train_41: 
[ 0.] 
PREDICTION: train_41: 
[ 0.] 
ACTUAL: train_42: 
[ 0.] 
PREDICTION: train_42: 
[ 0.] 
ACTUAL: train_43: 
[ 1.] 
PREDICTION: train_43: 
[ 0.] 
ACTUAL: train_44: 
[ 0.] 
PREDICTION: train_44: 
[ 0.] 
ACTUAL: train_45: 
[ 0.] 
PREDICTION: train_45: 
[ 0.] 
ACTUAL: train_46: 
[ 0.] 
PREDICTION: train_46: 
[ 0.] 
ACTUAL: train_47: 
[ 0.] 
PREDICTION: train_47: 
[ 0.] 
ACTUAL: train_48: 
[ 0.] 
PREDICTION: train_48: 
[ 0.] 
ACTUAL: train_49: 
[ 0.] 
PREDICTION: train_49: 
[ 0.] 
ACTUAL: train_50: 
[ 0.] 
PREDICTION: train_50: 
[ 0.] 
ACTUAL: train_51: 
[ 0.] 
PREDICTION: train_51: 
[ 0.] 
ACTUAL: train_52: 
[ 0.] 
PREDICTION: train_52: 
[ 0.] 
ACTUAL: train_53: 
[ 0.] 
PREDICTION: train_53: 
[ 0.] 
ACTUAL: train_54: 
[ 0.] 
PREDICTION: train_54: 
[ 0.] 
ACTUAL: train_55: 
[ 0.] 
PREDICTION: train_55: 
[ 0.] 
ACTUAL: train_56: 
[ 0.] 
PREDICTION: train_56: 
[ 0.] 
ACTUAL: train_57: 
[ 0.] 
PREDICTION: train_57: 
[ 0.] 
ACTUAL: train_58: 
[ 0.] 
PREDICTION: train_58: 
[ 0.] 
ACTUAL: train_59: 
[ 0.] 
PREDICTION: train_59: 
[ 0.] 
ACTUAL: train_60: 
[ 1.] 
PREDICTION: train_60: 
[ 0.] 
ACTUAL: train_61: 
[ 0.] 
PREDICTION: train_61: 
[ 0.] 
ACTUAL: train_62: 
[ 0.] 
PREDICTION: train_62: 
[ 0.] 
ACTUAL: train_63: 
[ 0.] 
PREDICTION: train_63: 
[ 0.] 
ACTUAL: train_64: 
[ 0.] 
PREDICTION: train_64: 
[ 0.] 
ACTUAL: train_65: 
[ 0.] 
PREDICTION: train_65: 
[ 0.] 
ACTUAL: train_66: 
[ 0.] 
PREDICTION: train_66: 
[ 0.] 
ACTUAL: train_67: 
[ 0.] 
PREDICTION: train_67: 
[ 0.] 
ACTUAL: train_68: 
[ 0.] 
PREDICTION: train_68: 
[ 0.] 
ACTUAL: train_69: 
[ 0.] 
PREDICTION: train_69: 
[ 0.] 
ACTUAL: train_70: 
[ 0.] 
PREDICTION: train_70: 
[ 0.] 
ACTUAL: train_71: 
[ 0.] 
PREDICTION: train_71: 
[ 0.] 
ACTUAL: train_72: 
[ 0.] 
PREDICTION: train_72: 
[ 0.] 
ACTUAL: train_73: 
[ 0.] 
PREDICTION: train_73: 
[ 0.] 
ACTUAL: train_74: 
[ 0.] 
PREDICTION: train_74: 
[ 0.] 
ACTUAL: train_75: 
[ 0.] 
PREDICTION: train_75: 
[ 0.] 
ACTUAL: train_76: 
[ 0.] 
PREDICTION: train_76: 
[ 0.] 
ACTUAL: train_77: 
[ 0.] 
PREDICTION: train_77: 
[ 0.] 
ACTUAL: train_78: 
[ 0.] 
PREDICTION: train_78: 
[ 0.] 
ACTUAL: train_79: 
[ 0.] 
PREDICTION: train_79: 
[ 0.] 
ACTUAL: train_80: 
[ 0.] 
PREDICTION: train_80: 
[ 0.] 
ACTUAL: train_81: 
[ 0.] 
PREDICTION: train_81: 
[ 0.] 
ACTUAL: train_82: 
[ 0.] 
PREDICTION: train_82: 
[ 0.] 
ACTUAL: train_83: 
[ 0.] 
PREDICTION: train_83: 
[ 0.] 
ACTUAL: train_84: 
[ 0.] 
PREDICTION: train_84: 
[ 0.] 
ACTUAL: train_85: 
[ 0.] 
PREDICTION: train_85: 
[ 0.] 
ACTUAL: train_86: 
[ 0.] 
PREDICTION: train_86: 
[ 0.] 
ACTUAL: train_87: 
[ 0.] 
PREDICTION: train_87: 
[ 0.] 
ACTUAL: train_88: 
[ 0.] 
PREDICTION: train_88: 
[ 0.] 
ACTUAL: train_89: 
[ 0.] 
PREDICTION: train_89: 
[ 0.] 
ACTUAL: train_90: 
[ 0.] 
PREDICTION: train_90: 
[ 0.] 
ACTUAL: train_91: 
[ 0.] 
PREDICTION: train_91: 
[ 0.] 
ACTUAL: train_92: 
[ 0.] 
PREDICTION: train_92: 
[ 0.] 
ACTUAL: train_93: 
[ 0.] 
PREDICTION: train_93: 
[ 0.] 
ACTUAL: train_94: 
[ 0.] 
PREDICTION: train_94: 
[ 0.] 
ACTUAL: train_95: 
[ 0.] 
PREDICTION: train_95: 
[ 0.] 
ACTUAL: train_96: 
[ 0.] 
PREDICTION: train_96: 
[ 0.] 
ACTUAL: train_97: 
[ 0.] 
PREDICTION: train_97: 
[ 0.] 
ACTUAL: train_98: 
[ 0.] 
PREDICTION: train_98: 
[ 0.] 
ACTUAL: train_99: 
[ 0.] 
PREDICTION: train_99: 
[ 0.] 
ACTUAL: train_100: 
[ 0.] 
PREDICTION: train_100: 
[ 0.] 
ACTUAL: train_101: 
[ 0.] 
PREDICTION: train_101: 
[ 0.] 
ACTUAL: train_102: 
[ 0.] 
PREDICTION: train_102: 
[ 0.] 
ACTUAL: train_103: 
[ 0.] 
PREDICTION: train_103: 
[ 0.] 
ACTUAL: train_104: 
[ 1.] 
PREDICTION: train_104: 
[ 0.] 
ACTUAL: train_105: 
[ 0.] 
PREDICTION: train_105: 
[ 0.] 
ACTUAL: train_106: 
[ 0.] 
PREDICTION: train_106: 
[ 0.] 
ACTUAL: train_107: 
[ 0.] 
PREDICTION: train_107: 
[ 0.] 
ACTUAL: train_108: 
[ 0.] 
PREDICTION: train_108: 
[ 0.] 
ACTUAL: train_109: 
[ 0.] 
PREDICTION: train_109: 
[ 0.] 
ACTUAL: train_110: 
[ 0.] 
PREDICTION: train_110: 
[ 0.] 
ACTUAL: train_111: 
[ 0.] 
PREDICTION: train_111: 
[ 0.] 
ACTUAL: train_112: 
[ 0.] 
PREDICTION: train_112: 
[ 0.] 
ACTUAL: train_113: 
[ 0.] 
PREDICTION: train_113: 
[ 0.] 
ACTUAL: train_114: 
[ 0.] 
PREDICTION: train_114: 
[ 0.] 
ACTUAL: train_115: 
[ 0.] 
PREDICTION: train_115: 
[ 0.] 
ACTUAL: train_116: 
[ 0.] 
PREDICTION: train_116: 
[ 0.] 
ACTUAL: train_117: 
[ 0.] 
PREDICTION: train_117: 
[ 0.] 
ACTUAL: train_118: 
[ 0.] 
PREDICTION: train_118: 
[ 0.] 
ACTUAL: train_119: 
[ 0.] 
PREDICTION: train_119: 
[ 0.] 
ACTUAL: train_120: 
[ 0.] 
PREDICTION: train_120: 
[ 0.] 
ACTUAL: train_121: 
[ 0.] 
PREDICTION: train_121: 
[ 0.] 
ACTUAL: train_122: 
[ 0.] 
PREDICTION: train_122: 
[ 0.] 
ACTUAL: train_123: 
[ 0.] 
PREDICTION: train_123: 
[ 0.] 
ACTUAL: train_124: 
[ 0.] 
PREDICTION: train_124: 
[ 0.] 
ACTUAL: train_125: 
[ 0.] 
PREDICTION: train_125: 
[ 0.] 
ACTUAL: train_126: 
[ 0.] 
PREDICTION: train_126: 
[ 0.] 
ACTUAL: train_127: 
[ 0.] 
PREDICTION: train_127: 
[ 0.] 
ACTUAL: train_128: 
[ 0.] 
PREDICTION: train_128: 
[ 0.] 
ACTUAL: train_129: 
[ 0.] 
PREDICTION: train_129: 
[ 0.] 
ACTUAL: train_130: 
[ 0.] 
PREDICTION: train_130: 
[ 0.] 
ACTUAL: train_131: 
[ 0.] 
PREDICTION: train_131: 
[ 0.] 
ACTUAL: train_132: 
[ 1.] 
PREDICTION: train_132: 
[ 0.] 
ACTUAL: train_133: 
[ 1.] 
PREDICTION: train_133: 
[ 0.] 
ACTUAL: train_134: 
[ 0.] 
PREDICTION: train_134: 
[ 0.] 
ACTUAL: train_135: 
[ 0.] 
PREDICTION: train_135: 
[ 0.] 
ACTUAL: train_136: 
[ 1.] 
PREDICTION: train_136: 
[ 0.] 
ACTUAL: train_137: 
[ 0.] 
PREDICTION: train_137: 
[ 0.] 
ACTUAL: train_138: 
[ 0.] 
PREDICTION: train_138: 
[ 0.] 
ACTUAL: train_139: 
[ 0.] 
PREDICTION: train_139: 
[ 0.] 
ACTUAL: train_140: 
[ 0.] 
PREDICTION: train_140: 
[ 0.] 
ACTUAL: train_141: 
[ 0.] 
PREDICTION: train_141: 
[ 0.] 
ACTUAL: train_142: 
[ 0.] 
PREDICTION: train_142: 
[ 0.] 
ACTUAL: train_143: 
[ 0.] 
PREDICTION: train_143: 
[ 0.] 
ACTUAL: train_144: 
[ 0.] 
PREDICTION: train_144: 
[ 0.] 
ACTUAL: train_145: 
[ 0.] 
PREDICTION: train_145: 
[ 0.] 
ACTUAL: train_146: 
[ 0.] 
PREDICTION: train_146: 
[ 0.] 
ACTUAL: train_147: 
[ 0.] 
PREDICTION: train_147: 
[ 0.] 
ACTUAL: train_148: 
[ 0.] 
PREDICTION: train_148: 
[ 0.] 
ACTUAL: train_149: 
[ 0.] 
PREDICTION: train_149: 
[ 0.] 
ACTUAL: train_150: 
[ 0.] 
PREDICTION: train_150: 
[ 0.] 
ACTUAL: train_151: 
[ 0.] 
PREDICTION: train_151: 
[ 0.] 
ACTUAL: train_152: 
[ 1.] 
PREDICTION: train_152: 
[ 0.] 
ACTUAL: train_153: 
[ 0.] 
PREDICTION: train_153: 
[ 0.] 
ACTUAL: train_154: 
[ 0.] 
PREDICTION: train_154: 
[ 0.] 
ACTUAL: train_155: 
[ 0.] 
PREDICTION: train_155: 
[ 0.] 
ACTUAL: train_156: 
[ 0.] 
PREDICTION: train_156: 
[ 0.] 
ACTUAL: train_157: 
[ 0.] 
PREDICTION: train_157: 
[ 0.] 
ACTUAL: train_158: 
[ 0.] 
PREDICTION: train_158: 
[ 0.] 
ACTUAL: train_159: 
[ 0.] 
PREDICTION: train_159: 
[ 0.] 
ACTUAL: train_160: 
[ 0.] 
PREDICTION: train_160: 
[ 0.] 
ACTUAL: train_161: 
[ 0.] 
PREDICTION: train_161: 
[ 0.] 
ACTUAL: train_162: 
[ 0.] 
PREDICTION: train_162: 
[ 0.] 
ACTUAL: train_163: 
[ 0.] 
PREDICTION: train_163: 
[ 0.] 
ACTUAL: train_164: 
[ 0.] 
PREDICTION: train_164: 
[ 0.] 
ACTUAL: train_165: 
[ 0.] 
PREDICTION: train_165: 
[ 0.] 
ACTUAL: train_166: 
[ 0.] 
PREDICTION: train_166: 
[ 0.] 
ACTUAL: train_167: 
[ 0.] 
PREDICTION: train_167: 
[ 0.] 
ACTUAL: train_168: 
[ 0.] 
PREDICTION: train_168: 
[ 0.] 
ACTUAL: train_169: 
[ 0.] 
PREDICTION: train_169: 
[ 0.] 
ACTUAL: train_170: 
[ 0.] 
PREDICTION: train_170: 
[ 0.] 
ACTUAL: train_171: 
[ 0.] 
PREDICTION: train_171: 
[ 0.] 
ACTUAL: train_172: 
[ 0.] 
PREDICTION: train_172: 
[ 0.] 
ACTUAL: train_173: 
[ 0.] 
PREDICTION: train_173: 
[ 0.] 
ACTUAL: train_174: 
[ 0.] 
PREDICTION: train_174: 
[ 0.] 
ACTUAL: train_175: 
[ 0.] 
PREDICTION: train_175: 
[ 0.] 
ACTUAL: train_176: 
[ 0.] 
PREDICTION: train_176: 
[ 0.] 
ACTUAL: train_177: 
[ 0.] 
PREDICTION: train_177: 
[ 0.] 
ACTUAL: train_178: 
[ 0.] 
PREDICTION: train_178: 
[ 0.] 
ACTUAL: train_179: 
[ 0.] 
PREDICTION: train_179: 
[ 0.] 
ACTUAL: train_180: 
[ 1.] 
PREDICTION: train_180: 
[ 0.] 
ACTUAL: train_181: 
[ 0.] 
PREDICTION: train_181: 
[ 0.] 
ACTUAL: train_182: 
[ 0.] 
PREDICTION: train_182: 
[ 0.] 
ACTUAL: train_183: 
[ 0.] 
PREDICTION: train_183: 
[ 0.] 
ACTUAL: train_184: 
[ 0.] 
PREDICTION: train_184: 
[ 0.] 
ACTUAL: train_185: 
[ 0.] 
PREDICTION: train_185: 
[ 0.] 
ACTUAL: train_186: 
[ 0.] 
PREDICTION: train_186: 
[ 0.] 
ACTUAL: train_187: 
[ 0.] 
PREDICTION: train_187: 
[ 0.] 
ACTUAL: train_188: 
[ 0.] 
PREDICTION: train_188: 
[ 0.] 
ACTUAL: train_189: 
[ 0.] 
PREDICTION: train_189: 
[ 0.] 
ACTUAL: train_190: 
[ 0.] 
PREDICTION: train_190: 
[ 0.] 
ACTUAL: train_191: 
[ 0.] 
PREDICTION: train_191: 
[ 0.] 
ACTUAL: train_192: 
[ 0.] 
PREDICTION: train_192: 
[ 0.] 
ACTUAL: train_193: 
[ 0.] 
PREDICTION: train_193: 
[ 0.] 
ACTUAL: train_194: 
[ 0.] 
PREDICTION: train_194: 
[ 0.] 
ACTUAL: train_195: 
[ 0.] 
PREDICTION: train_195: 
[ 0.] 
ACTUAL: train_196: 
[ 1.] 
PREDICTION: train_196: 
[ 0.] 
ACTUAL: train_197: 
[ 0.] 
PREDICTION: train_197: 
[ 0.] 
ACTUAL: train_198: 
[ 0.] 
PREDICTION: train_198: 
[ 0.] 
ACTUAL: train_199: 
[ 0.] 
PREDICTION: train_199: 
[ 0.] 

Answer 1

你的數據集只是非常不平衡/傾斜。你有94％的標籤0和標籤1的6％。神經網絡剛剛得知，如果它對所有事情都預測爲0，它可以是非常高效的。

，你能做些什麼來避免要麼改變你的數據集有標籤1的50％和標籤0的50％以上，你可以使用擬合函數的「class_weight」參數：

class_weight：將類映射到權重值的字典映射，用於縮放損失函數（僅限於訓練期間）。 source

在你的情況我會使用

fit(..., class_weight = {0:1, 1:15.5}) 

因爲你已經在0級15.5倍更多的樣本比1。此處的電話號碼，只是說，當你錯誤分類0您的損失乘以1，當你misclasify 1 1損失乘以15.5 ...更多信息here。

此外，我不會使用準確性度量來真正評估您的案例中的結果，但請參閱f1評分度量，這種方式更適合於此類數據集。 f1score on wikipedia

我希望這有助於嗎？