tensorflow: Why the loss doesn't decrease in my CNN model?

Hi All, I am new to tensorflow, and I find the losses doesn’t decrease when train a CNN model on Cifar100 dataset.

Here is the Model code:

network = Sequential([
    layers.Conv2D(64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), # 32x32
    layers.Conv2D(64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
    layers.MaxPooling2D((2, 2)), # 16x16

    layers.Conv2D(128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
    layers.Conv2D(128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
    layers.MaxPooling2D((2, 2)), # 8x8

    layers.Conv2D(256, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
    layers.Conv2D(256, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
    layers.MaxPooling2D((2, 2)), # 4x4

    layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
    layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
    layers.MaxPooling2D((2, 2)), # 2x2

    layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
    layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
    layers.MaxPooling2D((2, 2)), # 1x1

    layers.Flatten(),

    layers.Dense(256, activation=tf.nn.relu),
    layers.Dense(128, activation=tf.nn.relu),
    layers.Dense(100, activation=None),
])

and this is the link for the entire code: https://colab.research.google.com/drive/1kIT_18Z-ymEgzqgG4nh4sZdBkA1bnJdO

and, It will be OK if I use only one Conv2D layer with maxpooling, like this:

network = Sequential([
    layers.Conv2D(64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), # 32x32
    layers.MaxPooling2D((2, 2)), # 16x16

    layers.Conv2D(128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
    layers.MaxPooling2D((2, 2)), # 8x8

    layers.Conv2D(256, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
    layers.MaxPooling2D((2, 2)), # 4x4

    layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
    layers.MaxPooling2D((2, 2)), # 2x2

    layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
    layers.MaxPooling2D((2, 2)), # 1x1

   #xxxxx
])

thanks~