Training settings
Please provide a valid training processor option
Neural network architecture
import math, requests
from pathlib import Path
import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.layers import (
Dense, InputLayer, Dropout, Conv1D, Flatten, Reshape, MaxPooling1D, BatchNormalization,
Conv2D, GlobalMaxPooling2D, Lambda, GlobalAveragePooling2D)
from tensorflow.keras.optimizers.legacy import Nadam
from tensorflow.keras.losses import categorical_crossentropy
sys.path.append('./resources/libraries')
import ei_tensorflow.training
WEIGHTS_PATH = './transfer-learning-weights/edgeimpulse/kws/kws_mfe-dsp-ver-4-mobilenetv1a0.1_train_f1cf6_00000_0_batch_size=256,lr=0.001_2023-01-13_02-42-20_dnu_2048_009.h5'
# Download the model weights
root_url = 'https://cdn.edgeimpulse.com/'
p = Path(WEIGHTS_PATH)
if not p.exists():
print(f"Pretrained weights {WEIGHTS_PATH} unavailable; downloading...")
if not p.parent.exists():
p.parent.mkdir(parents=True)
weights_data = requests.get(root_url + WEIGHTS_PATH[2:]).content
with open(WEIGHTS_PATH, 'wb') as f:
f.write(weights_data)
print(f"Pretrained weights {WEIGHTS_PATH} unavailable; downloading OK")
print("")
INPUT_SHAPE = (99, 40, 1)
base_model = tf.keras.applications.MobileNet(
input_shape=INPUT_SHAPE, alpha=0.1,
weights=WEIGHTS_PATH, include_top=False)
base_model.trainable = False
model = Sequential()
model.add(InputLayer(
input_shape=X_train[0].shape, name='x_input'))
model.add(Reshape(INPUT_SHAPE))
model.add(base_model)
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.1))
model.add(Dense(128, activation='relu'))
model.add(Dense(classes, activation='softmax'))
EPOCHS = args.epochs or 30
LEARNING_RATE = args.learning_rate or 0.01
# If True, non-deterministic functions (e.g. shuffling batches) are not used.
# This is False by default.
ENSURE_DETERMINISM = args.ensure_determinism
BATCH_SIZE = args.batch_size or 32
if not ENSURE_DETERMINISM:
train_dataset = train_dataset.shuffle(buffer_size=BATCH_SIZE*4)
prefetch_policy = 1 if ENSURE_DETERMINISM else tf.data.AUTOTUNE
train_dataset = train_dataset.batch(BATCH_SIZE, drop_remainder=False).prefetch(prefetch_policy)
validation_dataset = validation_dataset.batch(BATCH_SIZE, drop_remainder=False).prefetch(prefetch_policy)
callbacks.append(BatchLoggerCallback(BATCH_SIZE, train_sample_count, epochs=EPOCHS, ensure_determinism=ENSURE_DETERMINISM))
model.compile(optimizer=Nadam(learning_rate=LEARNING_RATE),
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(train_dataset, validation_data=validation_dataset, epochs=EPOCHS, verbose=2, callbacks=callbacks, class_weight=ei_tensorflow.training.get_class_weights(Y_train))
Input layer (3,960 features)
MobileNetV1 0.1 (final layer: 128 neurons, 0.1 dropout)
Output layer (5 classes)
Model
Model version:
