Tensorflow分类问题-损失没有减少

Question

以下是参考代码：

Xtrain2 = df2.iloc[:,:-1].values
ytrain2 = df2['L'].values.reshape((200,1))

print(Xtrain2.shape, ytrain2.shape)

#--------------------------

lrelu = lambda x: tf.keras.activations.relu(x, alpha=0.1)

model2 = tf.keras.models.Sequential([
    tf.keras.layers.Dense(1501, input_dim=1501, activation='relu'),
    tf.keras.layers.Dense(100, activation='relu'),
    tf.keras.layers.Dense(100, activation='relu'),
    #tf.keras.layers.Dense(1),
    #tf.keras.layers.Dense(1, activation=lrelu)
    tf.keras.layers.Dense(1, activation='sigmoid')
])

model2.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

#--------------------------

model2.fit(Xtrain2, ytrain2, epochs=50)#, verbose=0)

只需简单尝试分类器即可。最后一层是Sigmoid，因为它只是一个二进制分类器。损失也适合该问题。输入的维数为1500，样本数为200。我得到以下输出：

(200, 1501) (200, 1)
Train on 200 samples
Epoch 1/50
200/200 [==============================] - 0s 2ms/sample - loss: 0.4201 - accuracy: 0.0300
Epoch 2/50
200/200 [==============================] - 0s 359us/sample - loss: -1.1114 - accuracy: 0.0000e+00
Epoch 3/50
200/200 [==============================] - 0s 339us/sample - loss: -4.6102 - accuracy: 0.0000e+00
Epoch 4/50
200/200 [==============================] - 0s 344us/sample - loss: -13.7864 - accuracy: 0.0000e+00
Epoch 5/50
200/200 [==============================] - 0s 342us/sample - loss: -34.7789 - accuracy: 0.0000e+00
.
.
.
Epoch 40/50
200/200 [==============================] - 0s 348us/sample - loss: -905166.4000 - accuracy: 0.3750
Epoch 41/50
200/200 [==============================] - 0s 344us/sample - loss: -1010177.5300 - accuracy: 0.3400
Epoch 42/50
200/200 [==============================] - 0s 354us/sample - loss: -1129819.1825 - accuracy: 0.3450
Epoch 43/50
200/200 [==============================] - 0s 379us/sample - loss: -1263355.3200 - accuracy: 0.3900
Epoch 44/50
200/200 [==============================] - 0s 359us/sample - loss: -1408803.0400 - accuracy: 0.3750
Epoch 45/50
200/200 [==============================] - 0s 355us/sample - loss: -1566850.5900 - accuracy: 0.3300
Epoch 46/50
200/200 [==============================] - 0s 359us/sample - loss: -1728280.7550 - accuracy: 0.3550
Epoch 47/50
200/200 [==============================] - 0s 354us/sample - loss: -1909759.2400 - accuracy: 0.3400
Epoch 48/50
200/200 [==============================] - 0s 379us/sample - loss: -2108889.7200 - accuracy: 0.3750
Epoch 49/50
200/200 [==============================] - 0s 369us/sample - loss: -2305491.9800 - accuracy: 0.3700
Epoch 50/50
200/200 [==============================] - 0s 374us/sample - loss: -2524282.6300 - accuracy: 0.3050

我看不到上面代码中哪里出了问题。任何帮助将不胜感激！