Zeinab Sadat Rabbani
Zeinab Sadat Rabbani
rf_in = Input(shape=(1024, 2), name = 'rf_input') x = QConv1D(64, 5, kernel_quantizer="quantized_bits(16,6)", padding='same', use_bias=False)(rf_in) x = QBatchNormalization()(x) x = QActivation("quantized_relu(16,6)")(x) x = MaxPooling1D(2, strides = 2, padding='same') (x) x =...
> I think I understand the problem. The hls4ml software assumes that QDense will always have kernel_quantizer defined, but that is not the case here. I will add a check...
Even I installed hls4ml library again and still same error.
As you can see, I used the correct setup but did not get any results. Could you help me with it?
@returnwellbeing As I check myproject.cpp, everything looks fine and I didn't get that comment.
So because of large model I can not use the hls4ml?
> I think this is fixed by #997. The model however is too large for hls4ml and won't work. I used a CNN model with 115,072 parameters and used the...
Does anyone have an idea how to add my weights to ZCU104? I just added the files mentioned in the tutorial.
I have the same problem. I already have the updated version of 0.8.1 but still have the same error.
**### Model:** def resnet_block(input_data, filters, conv_size): shortcut = input_data # Store the original input for the shortcut connection # Pre-activation (Batch Normalization and Activation before Convolution) x = BatchNormalization()(input_data) x...