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
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
sys.path.append('./resources/libraries')
import ei_tensorflow.training
WEIGHTS_PATH = './transfer-learning-weights/keras/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_0.75_160.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 = (160, 160, 3)
base_model = tf.keras.applications.MobileNetV2(
input_shape = INPUT_SHAPE, alpha=0.75,
weights = WEIGHTS_PATH
)
base_model.trainable = False
model = Sequential()
model.add(InputLayer(input_shape=INPUT_SHAPE, name='x_input'))
# Don't include the base model's top layers
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(Dropout(0.1))
model.add(Flatten())
model.add(Dense(classes, activation='softmax'))
BATCH_SIZE = args.batch_size or 32
EPOCHS = args.epochs or 20
LEARNING_RATE = args.learning_rate or 0.0005
# If True, non-deterministic functions (e.g. shuffling batches) are not used.
# This is False by default.
ENSURE_DETERMINISM = args.ensure_determinism
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=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)
print('')
print('Initial training done.', flush=True)
# How many epochs we will fine tune the model
FINE_TUNE_EPOCHS = 10
# What percentage of the base model's layers we will fine tune
FINE_TUNE_PERCENTAGE = 65
print('Fine-tuning best model for {} epochs...'.format(FINE_TUNE_EPOCHS), flush=True)
# Load best model from initial training
model = ei_tensorflow.training.load_best_model(BEST_MODEL_PATH)
# Determine which layer to begin fine tuning at
model_layer_count = len(model.layers)
fine_tune_from = math.ceil(model_layer_count * ((100 - FINE_TUNE_PERCENTAGE) / 100))
# Allow the entire base model to be trained
model.trainable = True
# Freeze all the layers before the 'fine_tune_from' layer
for layer in model.layers[:fine_tune_from]:
layer.trainable = False
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.000045),
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(train_dataset,
epochs=FINE_TUNE_EPOCHS,
verbose=2,
validation_data=validation_dataset,
callbacks=callbacks,
class_weight=None
)
Input layer (76,800 features)
MobileNetV2 160x160 0.75 (no final dense layer, 0.1 dropout)
Output layer (3 classes)
Model
Model version:
