Project ID:
363328
Context/Use case:
I’m following the example RC project to deploy an audio classification model to my Nicla Voice board. All the steps are exactly the same, except that I modified the model in expert mode (specifically, I replaced the 2-cov model with a MobileNet V2 alpha 0.1 model). Here is my modified code block:
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, Permute, ReLU, Softmax
from tensorflow.keras.optimizers.legacy import Adam
import math, requests
from tensorflow.keras import Model
from tensorflow.keras.layers import (
Dense, InputLayer, Dropout, Conv1D, Flatten, Reshape, MaxPooling1D, BatchNormalization,
Conv2D, GlobalMaxPooling2D, Lambda, GlobalAveragePooling2D)
from tensorflow.keras.optimizers.legacy import Adam, Adadelta
from tensorflow.keras.losses import categorical_crossentropy
EPOCHS = args.epochs or 150
LEARNING_RATE = args.learning_rate or 0.002
# If True, non-deterministic functions (e.g. shuffling batches) are not used.
# This is False by default.
ENSURE_DETERMINISM = args.ensure_determinism
# this controls the batch size, or you can manipulate the tf.data.Dataset objects yourself
BATCH_SIZE = args.batch_size or 32
if not ENSURE_DETERMINISM:
train_dataset = train_dataset.shuffle(buffer_size=BATCH_SIZE*4)
train_dataset=train_dataset.batch(BATCH_SIZE, drop_remainder=False)
validation_dataset = validation_dataset.batch(BATCH_SIZE, drop_remainder=False)
channels = 1
columns = 40
rows = int(input_length / (columns * channels))
INPUT_SHAPE = (rows, columns, channels)
base_model = tf.keras.applications.MobileNetV2(
input_shape = INPUT_SHAPE, alpha=0.1,
weights = None
)
base_model.trainable = True
model = Sequential()
model.add(Reshape(INPUT_SHAPE, input_shape=(input_length, )))
model.add(Permute((2,1,3))) # H and W are reversed for NDP120 Conv 2D input
model.add(InputLayer(input_shape=INPUT_SHAPE, name='x_input'))
last_layer_index = -3
model.add(Model(inputs=base_model.inputs, outputs=base_model.layers[last_layer_index].output))
model.add(Reshape((-1, model.layers[-1].output.shape[3])))
model.add(Dense(8, activation='relu'))
model.add(Dropout(0.1))
model.add(Flatten())
model.add(Dense(classes, activation='softmax'))
# 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, ensure_determinism=ENSURE_DETERMINISM))
# # 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)
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE),
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(train_dataset, validation_data=validation_dataset, epochs=EPOCHS, verbose=2, callbacks=callbacks)
# Use this flag to disable per-channel quantization for a model.
# This can reduce RAM usage for convolutional models, but may have
# an impact on accuracy.
disable_per_channel_quantization = False
Error:
After training the model for 150 epochs, I got good precisions. However, when I tried to build the firmware, I got the error output below:
Creating job... OK (ID: 17614803)
Scheduling job in cluster...
Container image pulled!
Job started
Writing templates OK
Scheduling job in cluster...
Container image pulled!
Job started
Removing clutter...
Removing clutter OK
Copying output...
Copying output OK
Scheduling job in cluster...
Container image pulled!
Job started
generate_synpkg for NDP120
Extracting model...
Extracting model OK
Found Audio block
Loading neural network...
Loading neural network OK
Loading data set...
Loading data set OK, found 3232 items
Converting sequential model to functional API...
Reshape layer detected
(None, 40, 40, 1)
Skipping Permute layer
Reshape layer detected
(None, 4, 1280)
Traceback (most recent call last):
File "/app/generate_synpkg.py", line 206, in <module>
print('Reshape layer should contain 40 columns', e, flush=True)
NameError: name 'e' is not defined
Application exited with code 1
Job failed (see above)
I’m not super familiar with Syntiant, but it seems to be coupled tightly with Edge Impulse. For example, I could not find the generate_synpkg.py file anywhere. In this case, I really wish to get help from you.
Guess:
I noticed that there is a comment in your expert mode of model code block: # Syntiant TDK supports only valid padding. I’m just guessing randomly, but could this be the reason for the error?
