该模型用于二元分类。 这是我的模型:

im_input= layers.Input(shape=[160,160,3])
x = layers.Conv2D(30,(3,3),strides=(2,2),padding='same')(im_input)
z = layers.DepthwiseConv2D((3,3),strides=2,padding='same',depth_multiplier=10)(im_input)
x = layers.ReLU()(x)
z = layers.ReLU()(z)
x = layers.Conv2D(60,(3,3),strides=(2,2),padding='same')(x)
z = layers.Conv2D(60,(3,3),strides=2,padding='same')(z)
x = layers.ReLU()(x)
z = layers.ReLU()(z)
x = layers.Concatenate()([x,z])
x = layers.Conv2D(120,(3,3),strides=2,padding='same')(x)
x = layers.ReLU()(x)
x = layers.Conv2D(200,(3,3),strides=2,padding='same')(x)
x = layers.ReLU()(x)
x = layers.Conv2D(400,(3,3),strides=1,padding='same')(x)
x = layers.ReLU()(x)
x = layers.Conv2D(900,(3,3),strides=1,padding='same')(x)
x = layers.Flatten()(x)
#x = layers.GlobalAveragePooling2D()(x)
x = layers.Dense(100,activation='relu')(x)
x = layers.Dropout(0.2)(x)
x = layers.Dense(20, activation='relu')(x)
out = layers.Dense(1,activation='sigmoid')(x)
smodel = tf.keras.Model(inputs=im_input, outputs=out, name="myModel2")
smodel.summary()

这是损失函数:

cross_entropy = tf.keras.losses.BinaryCrossentropy()

优化器:

optimizer = tf.keras.optimizers.SGD(0.001)

对优化器有什么建议吗？

为什么这个模型亏损不减？ 是不是模型出了问题？ 来人，请帮帮我。。。