Training settings
Please provide a valid training processor option
Audio training options
Neural network architecture
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, InputLayer, Dropout, Conv1D, Conv2D, Flatten, Reshape, MaxPooling1D, MaxPooling2D, AveragePooling2D, BatchNormalization, TimeDistributed, Permute, ReLU, Softmax
from tensorflow.keras.optimizers import Adam
EPOCHS = args.epochs or 200
LEARNING_RATE = args.learning_rate or 0.005
# this controls the batch size, or you can manipulate the tf.data.Dataset objects yourselfs
BATCH_SIZE = 32
train_dataset = train_dataset.batch(BATCH_SIZE, drop_remainder=False)
validation_dataset = validation_dataset.batch(BATCH_SIZE, drop_remainder=False)
import akida_models
model = akida_models.ds_cnn_kws(input_shape=(49,32,1),
classes=4,
include_top=True,
weight_quantization=0,
activ_quantization=0,
input_weight_quantization=0,
input_scaling=None)
# this controls the learning rate
opt = Adam(learning_rate=LEARNING_RATE, beta_1=0.9, beta_2=0.999)
callbacks.append(BatchLoggerCallback(BATCH_SIZE, train_sample_count, epochs=EPOCHS))
# train the neural network
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
model.fit(train_dataset, epochs=EPOCHS, validation_data=validation_dataset, verbose=2, callbacks=callbacks)
def akida_quantize_model(
keras_model,
weight_quantization: int = 4,
activ_quantization: int = 4,
input_weight_quantization: int = 8,
):
import cnn2snn
print("Performing post-training quantization...")
akida_model = cnn2snn.quantize(
keras_model,
weight_quantization=weight_quantization,
activ_quantization=activ_quantization,
input_weight_quantization=input_weight_quantization,
)
print("Performing post-training quantization OK")
print("")
return akida_model
def akida_perform_qat(
akida_model,
train_dataset: tf.data.Dataset,
validation_dataset: tf.data.Dataset,
optimizer: str,
fine_tune_loss: str,
fine_tune_metrics: "list[str]",
callbacks,
stopping_metric: str = "val_accuracy",
fit_verbose: int = 2,
qat_epochs: int = 40,
):
early_stopping = tf.keras.callbacks.EarlyStopping(
monitor=stopping_metric,
mode="max",
verbose=1,
min_delta=0,
patience=10,
restore_best_weights=True,
)
# callbacks.append(early_stopping)
# Create a fresh optimizer, as Adam is statful optimizer
opt = Adam(learning_rate=LEARNING_RATE, beta_1=0.9, beta_2=0.999)
print("Running quantization-aware training...")
akida_model.compile(
optimizer=opt, loss=fine_tune_loss, metrics=fine_tune_metrics
)
akida_model.fit(
train_dataset,
epochs=qat_epochs,
verbose=fit_verbose,
validation_data=validation_dataset,
callbacks=callbacks,
)
print("Running quantization-aware training OK")
print("")
return akida_model
akida_model = akida_quantize_model(model)
akida_model = akida_perform_qat(
akida_model,
train_dataset=train_dataset,
validation_dataset=validation_dataset,
optimizer=opt,
fine_tune_loss='categorical_crossentropy',
fine_tune_metrics=['accuracy'],
callbacks=callbacks)
Input layer (1,568 features)
2D conv / pool layer (8 filters, 3 kernel size, 1 layer)
Dropout (rate 0.5)
2D conv / pool layer (16 filters, 3 kernel size, 1 layer)
Dropout (rate 0.5)
Flatten layer
Output layer (4 classes)
Model
Model version:
