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
from ei_tensorflow.velo import train_keras_model_with_velo
sys.path.append('./resources/libraries')
import ei_tensorflow.training
WEIGHTS_PATH = './transfer-learning-weights/keras/mobilenet_2_5_128_tf.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 = (96, 96, 3)
base_model = tf.keras.applications.MobileNet(
input_shape = INPUT_SHAPE,
weights = WEIGHTS_PATH,
alpha = 0.25
)
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 = -5
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'))
# Implements the data augmentation policy
def augment_image(image, label):
# Flips the image randomly
image = tf.image.random_flip_left_right(image)
# Increase the image size, then randomly crop it down to
# the original dimensions
resize_factor = random.uniform(1, 1.2)
new_height = math.floor(resize_factor * INPUT_SHAPE[0])
new_width = math.floor(resize_factor * INPUT_SHAPE[1])
image = tf.image.resize_with_crop_or_pad(image, new_height, new_width)
image = tf.image.random_crop(image, size=INPUT_SHAPE)
# Vary the brightness of the image
image = tf.image.random_brightness(image, max_delta=0.2)
return image, label
train_dataset = train_dataset.map(augment_image, num_parallel_calls=tf.data.AUTOTUNE)
BATCH_SIZE = args.batch_size or 256
EPOCHS = args.epochs or 20
# 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))
train_keras_model_with_velo(
keras_model=model,
training_data=train_dataset,
validation_data=validation_dataset,
loss_fn=tf.keras.metrics.categorical_crossentropy,
num_epochs=EPOCHS,
callbacks=callbacks
)
Input layer (27,648 features)
MobileNetV1 96x96 0.25 (no final dense layer, 0.1 dropout)
Output layer (5 classes)
Model
Model version:
