TypeError: 'NoneType' object is not subscriptable about hls4ml HOT 7 OPEN

It it possible to put the model somewhere or paste a script to create a simple untrained model that has the same issue?

from hls4ml.

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 = QConv1D(32, 5, kernel_quantizer="quantized_bits(16,6)", padding='same', use_bias=False)(x)
x = QBatchNormalization()(x)
x = QActivation("quantized_relu(16,6)")(x)
x = MaxPooling1D(2, strides = 2, padding='same') (x)

x = QConv1D(16, 5, kernel_quantizer="quantized_bits(16,6)", padding='same', use_bias=False)(x)
x = QBatchNormalization()(x)
x = QActivation("quantized_relu(16,6)")(x)
x = MaxPooling1D(2, strides=2, padding='same') (x)

x = Flatten()(x)

dense_1 = QDense(128, activation="quantized_relu(16,6)", use_bias=False)(x)
dropout_1 = Dropout(0.25)(dense_1)
dense_2 = QDense(128, activation="quantized_relu(16,6)", use_bias=False)(dropout_1)
dropout_2 = Dropout(0.5)(dense_2)
softmax = QDense(7, kernel_quantizer="quantized_bits(16,6)", use_bias=False)(dropout_2)
softmax = Activation('softmax')(softmax)

opt = keras.optimizers.Adam(learning_rate=0.0001)
model= keras.Model(rf_in, softmax)
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=["accuracy"])
model.summary()

from hls4ml.

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 for it, but in the meantime, here is a workaround. Replace:

dense_1 = QDense(128, activation="quantized_relu(16,6)", use_bias=False)(x)

by

dense_1_noact = Dense(128, use_bias=False)(x)
dense_1 = QActivation(activation="quantized_relu(16,6)")(dense_1_noact)

from hls4ml.

Try using https://github.com/fastmachinelearning/hls4ml/tree/weight_quantizer_none

from hls4ml.

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 for it, but in the meantime, here is a workaround. Replace:

dense_1 = QDense(128, activation="quantized_relu(16,6)", use_bias=False)(x)

by

dense_1_noact = Dense(128, use_bias=False)(x)
dense_1 = QActivation(activation="quantized_relu(16,6)")(dense_1_noact)

I added the two code line to my code, but still the same error. nothing change.

dense_1 = Dense(128, use_bias=False)(x)
dense_1 = QActivation("quantized_relu(16,6)")(dense_1)
dropout_1 = Dropout(0.25)(dense_1)
dense_2 = Dense(128, use_bias=False)(dropout_1)
dense_2 = QActivation("quantized_relu(16,6)")(dense_2)
dropout_2 = Dropout(0.5)(dense_2)
softmax = Dense(7, use_bias=False)(dropout_2)
softmax = QActivation("quantized_relu(16,6)")(softmax)
output = Activation('softmax')(softmax)

from hls4ml.

Even I installed hls4ml library again and still same error.

from hls4ml.

+1 Same issue when using QKeras layers (QLSTM)

from hls4ml.