The problems is that the output of layer conv2d_9 became zero or negative. To solve this problem, you must **design the network so that the input data would not be highly downsampled**.
Here are some possible solutions:
- Use less layers. Especially remove a max-pooling layer, which downsamples a lot (by one third under this setting). - Use smaller max-pooling, e.g. pool_size=(2, 2), which results in downsampling by a half. - Use "same padding" for Conv2D layer, which results in no downsampling during the convolution step.

The problems is that the output of layer `conv2d_4` became zero or negative. To solve this problem, you must design the network so that the input data would not be highly downsampled.
Here are some possible solutions:
* Use less layers. Especially remove a max-pooling layer, which downsamples a lot (by one third under this setting). * Use smaller max-pooling, e.g. `pool_size=(2, 2)`, which results in downsampling by a half. * Use "same padding" for `Conv2D` layer, which results in no downsampling during the convolution step.